Effectiveness of a theory-based mobile phone text message intervention for improving protective behaviors of pregnant women against air pollution: a randomized controlled trial.

PubMed

Jasemzadeh, Mehrnoosh; Khafaie, Morteza Abdullatif; Jaafarzadeh, Nematallah; Araban, Marzieh

2018-03-01

Health impact of exposure to air pollution is a public health concern. The aim of this study was to investigate an extended parallel process model (EPPM)-based mobile phone text message intervention for improving protective behaviors against air pollution among pregnant women. In this randomized controlled trial (IRCT2016102810804N8), 130 pregnant women were randomly assigned into either experimental or control groups. A valid and reliable questionnaire was used to collect data. Experimental group received mobile phone intervention on a daily basis for 2 months. Control group received usual care, only. Data were analyzed using SPSS 15 applying t test, chi-square, and Wilcoxon and Mann-Whitney U test. Although before intervention, there were no significant differences between different structures of EPPM (P > 0.05), after intervention, there were statistically significant differences between perceived severity, response efficacy, self-efficacy, and protective behaviors between two groups (P < 0.05). Implementing EPPM based-mobile phone intervention could promote protective behaviors against air pollution among pregnant women. The present study might be used as a framework for evidence-based health promotion regarding air pollution risk communication and self-care behaviors. IRCT2016102810804N8.

Vested Interest theory and disaster preparedness.

PubMed

Miller, Claude H; Adame, Bradley J; Moore, Scott D

2013-01-01

Three studies were designed to extend a combination of vested interest theory (VI) and the extended parallel process model of fear appeals (EPPM) to provide formative research for creating more effective disaster preparedness social action campaigns. The aim was to develop an effective VI scale for assessing individual awareness and 'vestedness' relevant to disaster preparedness. Typical preparedness behaviours are discussed with emphasis on earthquakes and tornados in particular. Brief overviews of VI and the EPPM are offered, and findings are presented from three studies (one dealing with earthquakes, and two with tornados) conducted to determine the factor structure of the key VI components involved, and to develop and test subscales derived from the two theories. The paper finishes with a discussion of future research needs and suggestions on how the new subscales may be applied in the design and execution of more effective disaster preparedness campaigns. © 2013 The Author(s). Journal compilation © Overseas Development Institute, 2013.

Using the risk behaviour diagnosis scale to understand Australian Aboriginal smoking — A cross-sectional validation survey in regional New South Wales

PubMed Central

Gould, Gillian Sandra; Watt, Kerrianne; Cadet-James, Yvonne; Clough, Alan R.

2014-01-01

Objective To validate, for the first time, the Risk Behaviour Diagnosis (RBD) Scale for Aboriginal Australian tobacco smokers, based on the Extended Parallel Process Model (EPPM). Despite high smoking prevalence, little is known about how Indigenous peoples assess their smoking risks. Methods In a cross-sectional study of 121 aboriginal smokers aged 18–45 in regional New South Wales, in 2014, RBD subscales were assessed for internal consistency. Scales included measures of perceived threat (susceptibility to and severity of smoking risks) and perceived efficacy (response efficacy and self-efficacy for quitting). An Aboriginal community panel appraised face and content validity. EPPM constructs of danger control (protective motivation) and fear control (defensive motivation) were assessed for cogency. Results Scales had acceptable to good internal consistency (Cronbach's alpha = 0.65–1.0). Most participants demonstrated high-perceived threat (77%, n = 93); and half had high-perceived efficacy (52%, n = 63). High-perceived efficacy with high-threat appeared consistent with danger control dominance; low-perceived efficacy with high-threat was consistent with fear control dominance. Conclusions In these Aboriginal smokers of reproductive age, the RBD Scale appeared valid and reliable. Further research is required to assess whether the RBD Scale and EPPM can predict quit attempts and assist with tailored approaches to counselling and targeted health promotion campaigns. PMID:26844043

Ambivalence, communication and past use: understanding what influences women's intentions to use contraceptives.

PubMed

Campo, Shelly; Askelson, Natoshia M; Spies, Erica L; Losch, Mary

2012-01-01

Unintended pregnancy among women in the 18-30 age group is a public health concern. The Extended Parallel Process Model (EPPM) provides a framework for exploring how women's perceptions of threat, efficacy, and fear influence intentions to use contraceptives. Past use and communication with best friends and partners were also considered. A telephone survey of 18-30-year-old women (N = 599) was completed. After univariate and bivariate analyses were conducted, the variables were entered into a hierarchal, multi-variate linear regression with three steps consistent with the EPPM to predict behavioral intention. The first step included the demographic variables of relationship status and income. The constructs for the EPPM were entered into step 2. Step 3 contained the fear measure. The model for the third step was significant, F(10,471) = 36.40, p < 0.001 and the variance explained by this complete model was 0.42. Results suggest that perceived severity of the consequences of an unintended pregnancy (p < 0.01), communication with friends (p < 0.01) and last sexual partner (p < 0.05), relationship status (p < 0.01), and past use (p < 0.001) were associated with women's intentions to use contraceptives. A woman's perception of the severity was related to her intention to use contraceptives. Half of the women (50.3%) reported ambivalence about the severity of an unintended pregnancy. In our study, talking with their last sexual partner had a positive effect on intentions to use contraceptives, while talking with friends influenced intentions in a negative direction. These results reconfirm the need for public health practitioners and health care providers to consider level of ambivalence toward unintended pregnancy, communication with partner, and relationship status when trying to improve women's contraceptive behaviors. Implications for effective communication interventions are discussed.

Public health-specific personal disaster preparedness training: an academic-practice collaboration.

PubMed

Kohn, Sivan; Semon, Natalie; Hedlin, Haley K; Thompson, Carol B; Marum, Felicity; Jenkins, Sebra; Slemp, Catherine C; Barnett, Daniel J

2014-01-01

To measure the following three relevant outcomes of a personal preparedness curriculum for public health workers: 1) the extent of change (increase) in knowledge about personal preparedness activities and knowledge about tools for conducting personal preparedness activities; 2) the extent of change (increase) in preparedness activities performed post-training and/or confidence in conducting these tasks; and 3) an understanding of how to improve levels of personal preparedness using the Extended Parallel Process Model (EPPM) framework. Cross-sectional preinterventional and postinterventional survey using a convenience sample. During 2010, three face-to-face workshops were conducted in three locations in West Virginia. One hundred thirty-one participants (baseline survey); 69 participants (1-year resurvey)-representing West Virginia local health department (LHD) and State Health Department employees. A 3-hour interactive, public health-specific, face-to-face workshop on personal disaster preparedness. Change in 1) knowledge about, and tools for, personal preparedness activities; 2) preparedness activities performed post-training and/or confidence in conducting these activities; and 3) the relationship of EPPM categories to personal preparedness activities. One year postworkshop, 77 percent of respondents reported having personal emergency kits (40 percent at baseline) and 67 percent reported having preparedness plans (38 percent at baseline) suggesting some participants assembled supply kits and plans postworkshop. Within the context of EPPM, respondents in high-threat categories agreed more often than respondents in low-threat categories that severe personal impacts were likely to result from a moderate flood. Compared to respondents categorized as low efficacy, respondents in high-efficacy categories perceived confidence in their knowledge and an impact of their response on their job success at higher rates. Personal disaster preparedness trainings for the LHD workforce can yield gains in relevant preparedness behaviors and attitudes but may require longitudinal reinforcement. The EPPM can offer a useful threat and efficacy-based lens to understand relevant perceptions surrounding personal disaster preparedness behaviors among LHD employees.

A serial mediation model of message framing on intentions to receive the human papillomavirus (HPV) vaccine: revisiting the role of threat and efficacy perceptions.

PubMed

Krieger, Janice L; Sarge, Melanie A

2013-01-01

Previous research has yielded mixed findings regarding the potential for message framing to influence HPV vaccine-related intentions. Drawing on the Extended Parallel Process Model (EPPM), the current study focuses on the role of threat and efficacy as serial mediators linking message framing and HPV vaccine-related intentions. College-age females and their parents participated in a between-subjects, posttest only experiment to investigate whether behavioral intentions to talk to a doctor about the HPV vaccine differ as a function of framing messages in terms of disease prevention. For young women, framing messages as preventing genital warts (as compared to cancer prevention) significantly increased perceptions of self-efficacy, which enhanced response efficacy perceptions that, in turn, increased intentions to talk to a doctor about the HPV vaccine. There were no effects of message framing among parents. However, response efficacy was a significant mediator of self-efficacy and behavioral intentions for both the college-age females and their parents. The results of this study suggest new approaches for considering the relationship among EPPM constructs.

Messages for men: the efficacy of EPPM-based messages targeting men's physical activity.

PubMed

Hatchell, Alexandra C; Bassett-Gunter, Rebecca L; Clarke, Marie; Kimura, Stacey; Latimer-Cheung, Amy E

2013-01-01

The majority of men are insufficiently active. Men's tendencies to participate in risky behaviors and their inactivity likely contribute to their increased risk of morbidity and mortality. Physical activity decreases the risk of developing many chronic diseases and may be an optimal behavior to target in men's health interventions. However, educational resources promoting physical activity for men are lacking. To address this gap, we tested the efficacy of messages based upon the Extended Parallel Process Model (EPPM; Witte, 1992) to increase men's physical activity intentions and behaviors. Men who were not meeting physical activity guidelines (n = 611) were randomly assigned to read high or low efficacy physical activity messages paired with high or no health risk information. Participants read four brief messages on four consecutive days. Intentions were assessed at baseline and the first follow-up (Day 5). Manipulation check measures were assessed at Day 5. Behavior was assessed at baseline and the second follow-up (Day 14). Overall, the messages had small sized effects. A completer analysis revealed that although men's intentions to be active increased over the course of the study regardless of the messages they received, only men who received risk information significantly increased their physical activity. Men who received low efficacy and risk information were less likely to meet the physical activity guidelines at Day 14 than men who only received low efficacy information. From these results, we suggest preliminary recommendations for the development of physical activity messages for men and areas for future EPPM-based research. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Risk Perception of HIV/AIDS and Low Self-Control Trait: Explaining Preventative Behaviors Among Iranian University Students

PubMed Central

Esmaeilzadeh, Safooreh; Allahverdipour, Hamid; Fathi, Behrouz; Shirzadi, Shayesteh

2016-01-01

Background: In spite of developed countries there are progressive trend about HIV/AIDS and its’ aspects of transmission in the low socio-economic societies. The aim of this was to explain the youth's behavior in adopting HIV/AIDS related preventive behaviors in a sample of Iranian university students by emphasizing on fear appeals approaches alongside examining the role of self-control trait for explaining adoption on danger or fear control processes based on Extended Parallel Process Model (EPPM). Methods: A sample of 156 randomly selected university students in Jolfa, Iran was recruited in a predictive cross-sectional study by application of a researcher-designed questionnaire through self-report data collection manner. Sexual high risk behaviors, the EPPM variables, self-control trait, and general self-efficacy were measured as theoretical framework. Results: Findings indicated that 31.3% of participants were in the fear control process versus 68.7% in danger control about HIV/AIDS and also the presence of multi-sex partners and amphetamine consumption amongst the participants. Low self-control trait and low perceived susceptibility significantly were related to having a history of multi-sex partners while high level of self-efficacy significantly increased the probability of condom use. Conclusion: Findings of the study were indicative of the protective role of high level of self-control, perceived susceptibility and self-efficacy factors on youth's high-risk behaviors and their preventative skills as well. PMID:26573026

An extension of the extended parallel process model (EPPM) in television health news: the influence of health consciousness on individual message processing and acceptance.

PubMed

Hong, Hyehyun

2011-06-01

The purpose of this study is to examine the role of health consciousness in processing TV news that contains potential health threats and preventive recommendations. Based on the extended parallel process model (Witte, 1992), relationships among health consciousness, perceived severity, perceived susceptibility, perceived response efficacy, perceived self-efficacy, and message acceptance/rejection were hypothesized. Responses collected from 175 participants after viewing four TV health news stories were analyzed using the bootstrapping analysis (Preacher & Hayes, 2008). Results confirmed three mediators (i.e., perceived severity, response efficacy, self-efficacy) in the influence of health consciousness on message acceptance. A negative association found between health consciousness and perceived susceptibility is discussed in relation to characteristics of health conscious individuals and optimistic bias of health risks.

Correlates of Condom-use Self-efficacy on the EPPM-based Integrated Model among Chinese College Students.

PubMed

Jin, Shan Shan; Bu, Kai; Chen, Fang Fang; Xu, Hui Fang; Li, Yi; Zhao, Dong Hui; Xu, Fang; Li, Jing Yan; Han, Meng Jie; Wang, Ning; Wang, Lu

2017-02-01

To explore the predictors of condom-use self-efficacy in Chinese college students according to the extended parallel process model (EPPM)-based integrated model. A total of 3,081 college students were anonymously surveyed through self-administered questionnaires in Guangzhou and Harbin, China. A structural equation model was applied to assess the integrated model. Among the participants, 1,387 (46.7%) were male, 1,586 (53.3%) were female, and the average age was 18.6 years. The final integrated model was acceptable. Apart from the direct effect (r = 0.23), perceived severity had two indirect effects on condom-use self-efficacy through the attitude to HIV education (r = 0.40) and intention to engage in premarital sex (r = -0.16), respectively. However, the perceived susceptibility mediated through the intention to engage in premarital sex (intent-to-premarital-sex) had a poor indirect impact on condom-use self-efficacy (total effect was -0.06). Furthermore, attitude toward HIV health education (r = 0.49) and intent-to-premarital-sex (r = -0.31) had a strong direct effect on condom-use self-efficacy. In addition, male students perceived higher susceptibility, stronger intent-to-premarital-sex, and lower condom-use self-efficacy than female students. The integrated model may be used to assess the determinants of condom-use self-efficacy among Chinese college students. Future research should focus on raising the severity perception, HIV-risk-reduction motivation, and the premarital abstinence intention among college students. Furthermore, considering the gender differences observed in the present survey, single-sex HIV education is required in school-based HIV/sex intervention. Copyright © 2017 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

What is the truth? An application of the Extended Parallel Process Model to televised truth® ads.

PubMed

Lavoie, Nicole R; Quick, Brian L

2013-01-01

The purpose of this study was to analyze television ads in the truth® campaign using the Extended Parallel Process Model (EPPM) as a framework. Among the ads (n = 86) analyzed, results revealed a heavy reliance on severity messages, modest attention to susceptibility messages, and no inclusion of recommended response messages in the form of self-efficacy and response efficacy. The reliance on emphasizing the health threat, without incorporating recommended response messages, is discussed with respect to the likelihood of galvanizing maladaptive responses such as psychological reactance, denial, and defensive avoidance resulting from exposure to these ads. Additionally, the unintended outcomes for secondary audiences including but not limited to stigma are considered. Implications and suggestions for practitioners and theorists are explored.

Communicating Cardiovascular Disease Risk Due to Elevated Homocysteine Levels: Using the EPPM to Develop Print Materials

ERIC Educational Resources Information Center

McKay, Diane L.; Berkowitz, Judy M.; Blumberg, Jeffrey B.; Goldberg, Jeanne P.

2004-01-01

Improving the effectiveness of written information to promote compliance with therapeutic regimens is essential, particularly among older adults. Guiding their development and evaluating their effectiveness with an accepted communication theory or model may help. A preliminary test of written materials developed within the context of the Extended…

Aligning HIV/AIDS communication with the oral tradition of Africans: a theory-based content analysis of songs' potential in prevention efforts.

PubMed

Bekalu, Mesfin Awoke; Eggermont, Steven

2015-01-01

Despite a growing recognition of songs as a useful HIV/AIDS campaign strategy, little research has investigated their potential and/or actual impact. In this study, through a theory-based content analysis, we have assessed the prevention domains covered and the health-relevant constructs promoted by 23 AIDS songs widely used to aid prevention efforts in Ethiopia. To identify the health-relevant constructs and reveal their potential to facilitate or inhibit positive changes, the Extended Parallel Process Model (EPPM) has been used. The findings revealed that the songs cover most of the prevention domains that constitute the current agenda of behavior change communication in Sub-Saharan Africa. However, although all the EPPM variables have been found in almost every song, there were significantly more efficacy messages than threat messages. This suggests that although the songs may lead to positive changes in HIV/AIDS-related outcomes among audiences who have already perceived the threat posed by HIV/AIDS, they are less likely to motivate and thereby generate responses from audiences who have less or no threat perceptions. It is argued that given their potential as a culturally appropriate strategy in Sub-Saharan Africa where oral channels of communication play significant roles, songs could be harnessed for better outcomes through a theory-based design.

Characterizing hospital workers' willingness to report to duty in an influenza pandemic through threat- and efficacy-based assessment.

PubMed

Balicer, Ran D; Barnett, Daniel J; Thompson, Carol B; Hsu, Edbert B; Catlett, Christina L; Watson, Christopher M; Semon, Natalie L; Gwon, Howard S; Links, Jonathan M

2010-07-26

Hospital-based providers' willingness to report to work during an influenza pandemic is a critical yet under-studied phenomenon. Witte's Extended Parallel Process Model (EPPM) has been shown to be useful for understanding adaptive behavior of public health workers to an unknown risk, and thus offers a framework for examining scenario-specific willingness to respond among hospital staff. We administered an anonymous online EPPM-based survey about attitudes/beliefs toward emergency response, to all 18,612 employees of the Johns Hopkins Hospital from January to March 2009. Surveys were completed by 3426 employees (18.4%), approximately one third of whom were health professionals. Demographic and professional distribution of respondents was similar to all hospital staff. Overall, more than one-in-four (28%) hospital workers indicated they were not willing to respond to an influenza pandemic scenario if asked but not required to do so. Only an additional 10% were willing if required. One-third (32%) of participants reported they would be unwilling to respond in the event of a more severe pandemic influenza scenario. These response rates were consistent across different departments, and were one-third lower among nurses as compared with physicians. Respondents who were hesitant to agree to work additional hours when required were 17 times less likely to respond during a pandemic if asked. Sixty percent of the workers perceived their peers as likely to report to work in such an emergency, and were ten times more likely than others to do so themselves. Hospital employees with a perception of high efficacy had 5.8 times higher declared rates of willingness to respond to an influenza pandemic. Significant gaps exist in hospital workers' willingness to respond, and the EPPM is a useful framework to assess these gaps. Several attitudinal indicators can help to identify hospital employees unlikely to respond. The findings point to certain hospital-based communication and training strategies to boost employees' response willingness, including promoting pre-event plans for home-based dependents; ensuring adequate supplies of personal protective equipment, vaccines and antiviral drugs for all hospital employees; and establishing a subjective norm of awareness and preparedness.

Effect of Message Format and Content on Attitude Accessibility Regarding Sexually Transmitted Infections.

PubMed

Jain, Parul; Hoffman, Eric; Beam, Michael; Xu, Shan Susan

2017-11-01

Sexually transmitted infections (STIs) are widespread in the United States among people ages 15-24 years and cost almost $16 billion yearly. It is therefore important to understand message design strategies that could help reduce these numbers. Guided by exemplification theory and the extended parallel process model (EPPM), this study examines the influence of message format and the presence versus absence of a graphic image on recipients' accessibility of STI attitudes regarding safe sex. Results of the experiment indicate a significant effect from testimonial messages on increased attitude accessibility regarding STIs compared to statistical messages. Results also indicate a conditional indirect effect of testimonial messages on STI attitude accessibility, though threat is greater when a graphic image is included. Implications and directions for future research are discussed.

Examining HPV threat-to-efficacy ratios in the Extended Parallel Process Model.

PubMed

Carcioppolo, Nick; Jensen, Jakob D; Wilson, Steven R; Collins, W Bart; Carrion, Melissa; Linnemeier, Georgiann

2013-01-01

The Extended Parallel Process Model (EPPM) posits that an effective fear appeal includes both threat and efficacy components; however, research has not addressed whether there is an optimal threat-to-efficacy ratio. It is possible that varying levels of threat and efficacy in a persuasive message could yield different effects on attitudes, beliefs, and behaviors. In a laboratory experiment, women (n = 442) were exposed to human papilloma virus (HPV) prevention messages containing one of six threat-to-efficacy ratios and one of two message frames (messages emphasizing the connection between HPV and cervical cancer or HPV and genital warts). Multiple mediation analysis revealed that a 1-to-1 ratio of threat to efficacy was most effective at increasing prevention intentions, primarily because it caused more fear and risk susceptibility than other message ratios. Response efficacy significantly mediated the relationship between message framing and intentions, such that participants exposed to a genital warts message reported significantly higher intentions, and this association can be explained in part through response efficacy. Implications for future theoretical research as well as campaigns and intervention research are discussed.

Reconceptualizing Efficacy in Substance Use Prevention Research: Refusal Response Efficacy and Drug Resistance Self-Efficacy in Adolescent Substance Use

PubMed Central

Choi, Hye Jeong; Krieger, Janice L.; Hecht, Michael L.

2014-01-01

The purpose of this study is to utilize the Extended Parallel Process Model (EPPM) to expand the construct of efficacy in the adolescent substance use context. Using survey data collected from 2,129 seventh-grade students in 39 rural schools, we examined the construct of drug refusal efficacy and demonstrated relationships among response efficacy (RE), self-efficacy (SE), and adolescent drug use. Consistent with the hypotheses, confirmatory factor analyses of a 12-item scale yielded a three-factor solution: refusal RE, alcohol-resistance self-efficacy (ASE), and marijuana-resistance self-efficacy (MSE). Refusal RE and ASE/MSE were negatively related to alcohol use and marijuana use, whereas MSE was positively associated with alcohol use. These data demonstrate that efficacy is a broader construct than typically considered in drug prevention. Prevention programs should reinforce both refusal RE and substance-specific resistance SE. PMID:23330857

Predicting risk behaviors: development and validation of a diagnostic scale.

PubMed

Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

1996-01-01

The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

The future of medical diagnostics: review paper

PubMed Central

2011-01-01

While histopathology of excised tissue remains the gold standard for diagnosis, several new, non-invasive diagnostic techniques are being developed. They rely on physical and biochemical changes that precede and mirror malignant change within tissue. The basic principle involves simple optical techniques of tissue interrogation. Their accuracy, expressed as sensitivity and specificity, are reported in a number of studies suggests that they have a potential for cost effective, real-time, in situ diagnosis. We review the Third Scientific Meeting of the Head and Neck Optical Diagnostics Society held in Congress Innsbruck, Innsbruck, Austria on the 11th May 2011. For the first time the HNODS Annual Scientific Meeting was held in association with the International Photodynamic Association (IPA) and the European Platform for Photodynamic Medicine (EPPM). The aim was to enhance the interdisciplinary aspects of optical diagnostics and other photodynamic applications. The meeting included 2 sections: oral communication sessions running in parallel to the IPA programme and poster presentation sessions combined with the IPA and EPPM posters sessions. PMID:21861912

A theoretically based evaluation of HIV/AIDS prevention campaigns along the trans-Africa highway in Kenya.

PubMed

Witte, K; Cameron, K A; Lapinski, M K; Nzyuko, S

1998-01-01

Print HIV/AIDS prevention campaign materials (e.g., posters, pamphlets, stickers) from 10 public health organizations in Kenya were evaluated according to the Extended Parallel Process Model (EPPM), a health behavior change theory based on the fear appeal literature, at various sites along the Trans-Africa Highway in Kenya. Three groups each of commercial sex workers (CSWs), truck drivers (TDs) and their assistants (ASSTs), and young men (YM) who live and work at the truck stops participated in focus group discussions where reactions to the campaign materials were gathered according to this theoretical base. Reactions to campaign materials varied substantially, according to the poster or pamphlet viewed. Overall, most participants wanted more detailed information about (a) the proper way to use condoms, (b) ideas for how to negotiate condom use with reluctant partners, and (c) accurate information on symptoms of AIDS and what to do once one contracted HIV. Both quantitative and qualitative analyses of the campaign materials are reported.

The Moderating Effects of Self-Esteem and Self-Efficacy on Responses to Graphic Health Warnings on Cigarette Packages: A Comparison of Smokers and Nonsmokers.

PubMed

Chun, Seungwoo; Park, Joon Woo; Heflick, Nathan; Lee, Seon Min; Kim, Daejin; Kwon, Kyenghee

2018-08-01

Do graphic pictorial health warnings (GPHWs) on cigarette packaging work better for some people than others? According to the Extended Parallel Process Model (EPPM), fear appeals should heighten positive change only if a person believes he or she is capable of change (i.e., self-efficacy). We exposed 242 smokers and 241 nonsmokers (aged 18-29) in the Republic of Korea to either a GPHW or a text-only warning in a between-subjects experiment. Results indicated that the GPHW increased intentions and motivations to quit smoking (for smokers) and intentions and motivations to not start smoking (for nonsmokers). However, these effects were moderated by self-efficacy related to quitting or not starting smoking. For smokers, a GPHW was especially effective in increasing desires and intentions to quit for people high in self-efficacy and high in self-esteem. However, for nonsmokers, a GPHW was effective only when self-efficacy was high, regardless of self-esteem level. For smokers and nonsmokers, results were mediated by heightened perceived health estimation. Implications for understanding the effectiveness of warning labels on cigarettes, for the introduction of GPHWs in the Republic of Korea, and for the Extended Parallel Process Model, are discussed.

Theoretical Foundations of Appeals Used in Alcohol-Abuse and Drunk-Driving Public Service Announcements in the United States, 1995-2010.

PubMed

Niederdeppe, Jeff; Avery, Rosemary J; Miller, Emily Elizabeth Namaste

2018-05-01

The study identifies the extent to which theoretical constructs drawn from well-established message effect communication theories are reflected in the content of alcohol-related public service announcements (PSAs) airing in the United States over a 16-year period. Content analysis of 18 530 141 alcohol-abuse (AA) and drunk-driving (DD) PSAs appearing on national network and local cable television stations in the 210 largest designated marketing areas (DMAs) from January 1995 through December 2010. The authors developed a detailed content analytic codebook and trained undergraduate coders to reliably identify the extent to which theoretical constructs and other creative ad elements are reflected in the PSAs. We show these patterns using basic descriptive statistics. Although both classes of alcohol-related PSAs used strategies that are consistent with major message effect theories, their specific theoretical orientations differed dramatically. The AA PSAs were generally consistent with constructs emphasized by the Extended Parallel Process Model (EPPM), whereas DD PSAs were more likely to use normative strategies emphasized by the Focus Theory of Narrative Conduct (FTNC) or source credibility appeals central to the Elaboration Likelihood Model. Having identified message content, future research should use deductive approaches to determine if volume and message content of alcohol-control PSAs have an impact on measures of alcohol consumption and/or measures of drunk driving, such as fatalities or driving while intoxicated/driving under the influence arrests.

Influences of Self-Efficacy, Response Efficacy, and Reactance on Responses to Cigarette Health Warnings: A Longitudinal Study of Adult Smokers in Australia and Canada

PubMed Central

Thrasher, James F.; Swayampakala, Kamala; Borland, Ron; Nagelhout, Gera; Yong, Hua-Hie; Hammond, David; Bansal-Travers, Maansi; Thompson, Mary; Hardin, James

2016-01-01

ABSTRACT Guided by the extended parallel process model (EPPM) and reactance theory, this study examined the relationship between efficacy beliefs, reactance, and adult smokers’ responses to pictorial health warning labels (HWL) on cigarette packaging, including whether efficacy beliefs or reactance modify the relationship between HWL responses and subsequent smoking cessation behavior. Four waves of data were analyzed from prospective cohorts of smokers in Australia and Canada (n = 7,120 observations) over a period of time after implementation of more prominent, pictorial HWLs. Three types of HWL responses were studied: psychological threat responses (i.e., thinking about risks from smoking), forgoing cigarettes due to HWLs, and avoiding HWLs. The results from Generalized Estimating Equation models indicated that stronger efficacy beliefs and lower trait reactance were significantly associated with greater psychological threat responses to HWLs. Similar results were found for models predicting forgoing behavior, although response efficacy was inversely associated with it. Only response efficacy was significantly associated with avoiding HWLs, showing a positive relationship. Higher self-efficacy and stronger responses to HWLs, no matter the type, were associated with attempting to quit in the follow-up period; reactance was unassociated. No statistically significant interactions were found. These results suggest that stronger efficacy beliefs and lower trait reactance are associated with some stronger responses to fear-arousing HWL responses; however, these HWL responses appear no less likely to lead to cessation attempts among smokers with different levels of self-efficacy to quit, of response efficacy beliefs, or of trait reactance against attempts to control their behavior. PMID:27135826

Application of Behavioral Theories to Disaster and Emergency Health Preparedness: A Systematic Review

PubMed Central

Ejeta, Luche Tadesse; Ardalan, Ali; Paton, Douglas

2015-01-01

Background: Preparedness for disasters and emergencies at individual, community and organizational levels could be more effective tools in mitigating (the growing incidence) of disaster risk and ameliorating their impacts. That is, to play more significant roles in disaster risk reduction (DRR). Preparedness efforts focus on changing human behaviors in ways that reduce people’s risk and increase their ability to cope with hazard consequences. While preparedness initiatives have used behavioral theories to facilitate DRR, many theories have been used and little is known about which behavioral theories are more commonly used, where they have been used, and why they have been preferred over alternative behavioral theories. Given that theories differ with respect to the variables used and the relationship between them, a systematic analysis is an essential first step to answering questions about the relative utility of theories and providing a more robust evidence base for preparedness components of DRR strategies. The goal of this systematic review was to search and summarize evidence by assessing the application of behavioral theories to disaster and emergency health preparedness across the world. Methods: The protocol was prepared in which the study objectives, questions, inclusion and exclusion criteria, and sensitive search strategies were developed and pilot-tested at the beginning of the study. Using selected keywords, articles were searched mainly in PubMed, Scopus, Mosby’s Index (Nursing Index) and Safetylit databases. Articles were assessed based on their titles, abstracts, and their full texts. The data were extracted from selected articles and results were presented using qualitative and quantitative methods. Results: In total, 2040 titles, 450 abstracts and 62 full texts of articles were assessed for eligibility criteria, whilst five articles were archived from other sources, and then finally, 33 articles were selected. The Health Belief Model (HBM), Extended Parallel Process Model (EPPM), Theory of Planned Behavior (TPB) and Social Cognitive Theories were most commonly applied to influenza (H1N1 and H5N1), floods, and earthquake hazards. Studies were predominantly conducted in USA (13 studies). In Asia, where the annual number of disasters and victims exceeds those in other continents, only three studies were identified. Overall, the main constructs of HBM (perceived susceptibility, severity, benefits, and barriers), EPPM (higher threat and higher efficacy), TPB (attitude and subjective norm), and the majority of the constructs utilized in Social Cognitive Theories were associated with preparedness for diverse hazards. However, while all the theories described above describe the relationships between constituent variables, with the exception of research on Social Cognitive Theories, few studies of other theories and models used path analysis to identify the interdependence relationships between the constructs described in the respective theories/models. Similarly, few identified how other mediating  variables could influence disaster and emergency preparedness.  Conclusions: The existing evidence on the application of behavioral theories and models to disaster and emergency preparedness is chiefly from developed countries. This raises issues regarding their utility in countries, particularly in Asisa and the Middle East, where cultural characteristics are very different to those prevailing in the Western countries in which theories have been developed and tested. The theories and models discussed here have been applied predominantly to disease outbreaks and natural hazards, and information on their utility as guides to preparedness for man-made hazards is lacking. Hence, future studies related to behavioral theories and models addressing preparedness need to target developing countries where disaster risk  and the consequent need for preparedness is high. A need for additional work on demonstrating the relationships of variables and constructs, including more clearly articulating roles for mediating effects was also identified in this analysis.  PMID:26203400

Fear appeals in HIV-prevention messages: young people's perceptions in northern Tanzania.

PubMed

Bastien, Sheri

2011-12-01

The aims of the study were to elicit the perceptions of young people in Tanzania on the role of fear appeals in HIV-prevention messages and to identify important contextual factors that may influence young people's perceptions of HIV-prevention posters. A total of 10 focus groups were conducted to investigate the role of fear appeals using the extended parallel process model (EPPM) as a guide. Young people were shown a series of images (mostly posters) with alternating high and low-threat messages (fear appeals), and then asked questions about their overall beliefs about HIV and AIDS, as well as about their response in terms of perceived susceptibility to HIV infection, the severity of the message, and their perceptions of self-efficacy and response efficacy. The images and messages that specifically targeted young people were highest in inducing perceived susceptibility to HIV infection, while pictorial descriptions of the physical consequences of HIV infection and those messages related to the stigma and discrimination faced by HIV-infected or affected people induced greater perceptions of severity. The information-based posters rated high in inducing response efficacy, while none of the images seemed to convince young people that they had the self-efficacy to perform the recommended health behaviours. The young people expressed a preference for fear-based appeals and a belief that this could work well in HIV-prevention efforts, yet they also stated a desire for more information-based messages about how to protect themselves. Finally, the messages evoking the most emotional responses were those that had been locally conceived rather than ones developed by large-scale donor-funded campaigns. Finding the appropriate balance between fear and efficacy in HIV-prevention messages is imperative. Further research is needed to better understand how and when fear appeals work and do not work in African settings, especially among young people.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Heng; Ye, Ming; Walker, Anthony P.

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averagingmore » methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Transforming Collaborative Process Models into Interface Process Models by Applying an MDA Approach

NASA Astrophysics Data System (ADS)

Lazarte, Ivanna M.; Chiotti, Omar; Villarreal, Pablo D.

Collaborative business models among enterprises require defining collaborative business processes. Enterprises implement B2B collaborations to execute these processes. In B2B collaborations the integration and interoperability of processes and systems of the enterprises are required to support the execution of collaborative processes. From a collaborative process model, which describes the global view of the enterprise interactions, each enterprise must define the interface process that represents the role it performs in the collaborative process in order to implement the process in a Business Process Management System. Hence, in this work we propose a method for the automatic generation of the interface process model of each enterprise from a collaborative process model. This method is based on a Model-Driven Architecture to transform collaborative process models into interface process models. By applying this method, interface processes are guaranteed to be interoperable and defined according to a collaborative process.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE PAGES

Dai, Heng; Ye, Ming; Walker, Anthony P.; ...

2017-03-28

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Heng; Ye, Ming; Walker, Anthony P.

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

The impact of working memory and the "process of process modelling" on model quality: Investigating experienced versus inexperienced modellers.

PubMed

Martini, Markus; Pinggera, Jakob; Neurauter, Manuel; Sachse, Pierre; Furtner, Marco R; Weber, Barbara

2016-05-09

A process model (PM) represents the graphical depiction of a business process, for instance, the entire process from online ordering a book until the parcel is delivered to the customer. Knowledge about relevant factors for creating PMs of high quality is lacking. The present study investigated the role of cognitive processes as well as modelling processes in creating a PM in experienced and inexperienced modellers. Specifically, two working memory (WM) functions (holding and processing of information and relational integration) and three process of process modelling phases (comprehension, modelling, and reconciliation) were related to PM quality. Our results show that the WM function of relational integration was positively related to PM quality in both modelling groups. The ratio of comprehension phases was negatively related to PM quality in inexperienced modellers and the ratio of reconciliation phases was positively related to PM quality in experienced modellers. Our research reveals central cognitive mechanisms in process modelling and has potential practical implications for the development of modelling software and teaching the craft of process modelling.

The impact of working memory and the “process of process modelling” on model quality: Investigating experienced versus inexperienced modellers

NASA Astrophysics Data System (ADS)

Martini, Markus; Pinggera, Jakob; Neurauter, Manuel; Sachse, Pierre; Furtner, Marco R.; Weber, Barbara

2016-05-01

A process model (PM) represents the graphical depiction of a business process, for instance, the entire process from online ordering a book until the parcel is delivered to the customer. Knowledge about relevant factors for creating PMs of high quality is lacking. The present study investigated the role of cognitive processes as well as modelling processes in creating a PM in experienced and inexperienced modellers. Specifically, two working memory (WM) functions (holding and processing of information and relational integration) and three process of process modelling phases (comprehension, modelling, and reconciliation) were related to PM quality. Our results show that the WM function of relational integration was positively related to PM quality in both modelling groups. The ratio of comprehension phases was negatively related to PM quality in inexperienced modellers and the ratio of reconciliation phases was positively related to PM quality in experienced modellers. Our research reveals central cognitive mechanisms in process modelling and has potential practical implications for the development of modelling software and teaching the craft of process modelling.

The impact of working memory and the “process of process modelling” on model quality: Investigating experienced versus inexperienced modellers

PubMed Central

Martini, Markus; Pinggera, Jakob; Neurauter, Manuel; Sachse, Pierre; Furtner, Marco R.; Weber, Barbara

2016-01-01

A process model (PM) represents the graphical depiction of a business process, for instance, the entire process from online ordering a book until the parcel is delivered to the customer. Knowledge about relevant factors for creating PMs of high quality is lacking. The present study investigated the role of cognitive processes as well as modelling processes in creating a PM in experienced and inexperienced modellers. Specifically, two working memory (WM) functions (holding and processing of information and relational integration) and three process of process modelling phases (comprehension, modelling, and reconciliation) were related to PM quality. Our results show that the WM function of relational integration was positively related to PM quality in both modelling groups. The ratio of comprehension phases was negatively related to PM quality in inexperienced modellers and the ratio of reconciliation phases was positively related to PM quality in experienced modellers. Our research reveals central cognitive mechanisms in process modelling and has potential practical implications for the development of modelling software and teaching the craft of process modelling. PMID:27157858

Enhancement of the Acquisition Process for a Combat System-A Case Study to Model the Workflow Processes for an Air Defense System Acquisition

DTIC Science & Technology

2009-12-01

Business Process Modeling BPMN Business Process Modeling Notation SoA Service-oriented Architecture UML Unified Modeling Language CSP...system developers. Supporting technologies include Business Process Modeling Notation ( BPMN ), Unified Modeling Language (UML), model-driven architecture

Simplified process model discovery based on role-oriented genetic mining.

PubMed

Zhao, Weidong; Liu, Xi; Dai, Weihui

2014-01-01

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Global Sensitivity Analysis for Process Identification under Model Uncertainty

NASA Astrophysics Data System (ADS)

Ye, M.; Dai, H.; Walker, A. P.; Shi, L.; Yang, J.

2015-12-01

The environmental system consists of various physical, chemical, and biological processes, and environmental models are always built to simulate these processes and their interactions. For model building, improvement, and validation, it is necessary to identify important processes so that limited resources can be used to better characterize the processes. While global sensitivity analysis has been widely used to identify important processes, the process identification is always based on deterministic process conceptualization that uses a single model for representing a process. However, environmental systems are complex, and it happens often that a single process may be simulated by multiple alternative models. Ignoring the model uncertainty in process identification may lead to biased identification in that identified important processes may not be so in the real world. This study addresses this problem by developing a new method of global sensitivity analysis for process identification. The new method is based on the concept of Sobol sensitivity analysis and model averaging. Similar to the Sobol sensitivity analysis to identify important parameters, our new method evaluates variance change when a process is fixed at its different conceptualizations. The variance considers both parametric and model uncertainty using the method of model averaging. The method is demonstrated using a synthetic study of groundwater modeling that considers recharge process and parameterization process. Each process has two alternative models. Important processes of groundwater flow and transport are evaluated using our new method. The method is mathematically general, and can be applied to a wide range of environmental problems.

A model for process representation and synthesis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Thomas, R. H.

1971-01-01

The problem of representing groups of loosely connected processes is investigated, and a model for process representation useful for synthesizing complex patterns of process behavior is developed. There are three parts, the first part isolates the concepts which form the basis for the process representation model by focusing on questions such as: What is a process; What is an event; Should one process be able to restrict the capabilities of another? The second part develops a model for process representation which captures the concepts and intuitions developed in the first part. The model presented is able to describe both the internal structure of individual processes and the interface structure between interacting processes. Much of the model's descriptive power derives from its use of the notion of process state as a vehicle for relating the internal and external aspects of process behavior. The third part demonstrates by example that the model for process representation is a useful one for synthesizing process behavior patterns. In it the model is used to define a variety of interesting process behavior patterns. The dissertation closes by suggesting how the model could be used as a semantic base for a very potent language extension facility.

Off-target model based OPC

NASA Astrophysics Data System (ADS)

Lu, Mark; Liang, Curtis; King, Dion; Melvin, Lawrence S., III

2005-11-01

Model-based Optical Proximity correction has become an indispensable tool for achieving wafer pattern to design fidelity at current manufacturing process nodes. Most model-based OPC is performed considering the nominal process condition, with limited consideration of through process manufacturing robustness. This study examines the use of off-target process models - models that represent non-nominal process states such as would occur with a dose or focus variation - to understands and manipulate the final pattern correction to a more process robust configuration. The study will first examine and validate the process of generating an off-target model, then examine the quality of the off-target model. Once the off-target model is proven, it will be used to demonstrate methods of generating process robust corrections. The concepts are demonstrated using a 0.13 μm logic gate process. Preliminary indications show success in both off-target model production and process robust corrections. With these off-target models as tools, mask production cycle times can be reduced.

Statistically Qualified Neuro-Analytic system and Method for Process Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.

1998-11-04

An apparatus and method for monitoring a process involves development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two steps: deterministic model adaption and stochastic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics,augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation emor minimization technique. Stochastic model adaptation involves qualifying any remaining uncertaintymore » in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system.« less

Improving the process of process modelling by the use of domain process patterns

NASA Astrophysics Data System (ADS)

Koschmider, Agnes; Reijers, Hajo A.

2015-01-01

The use of business process models has become prevalent in a wide area of enterprise applications. But while their popularity is expanding, concerns are growing with respect to their proper creation and maintenance. An obvious way to boost the efficiency of creating high-quality business process models would be to reuse relevant parts of existing models. At this point, however, limited support exists to guide process modellers towards the usage of appropriate model content. In this paper, a set of content-oriented patterns is presented, which is extracted from a large set of process models from the order management and manufacturing production domains. The patterns are derived using a newly proposed set of algorithms, which are being discussed in this paper. The authors demonstrate how such Domain Process Patterns, in combination with information on their historic usage, can support process modellers in generating new models. To support the wider dissemination and development of Domain Process Patterns within and beyond the studied domains, an accompanying website has been set up.

Managing Analysis Models in the Design Process

NASA Technical Reports Server (NTRS)

Briggs, Clark

2006-01-01

Design of large, complex space systems depends on significant model-based support for exploration of the design space. Integrated models predict system performance in mission-relevant terms given design descriptions and multiple physics-based numerical models. Both the design activities and the modeling activities warrant explicit process definitions and active process management to protect the project from excessive risk. Software and systems engineering processes have been formalized and similar formal process activities are under development for design engineering and integrated modeling. JPL is establishing a modeling process to define development and application of such system-level models.

Customer-centered careflow modeling based on guidelines.

PubMed

Huang, Biqing; Zhu, Peng; Wu, Cheng

2012-10-01

In contemporary society, customer-centered health care, which stresses customer participation and long-term tailored care, is inevitably becoming a trend. Compared with the hospital or physician-centered healthcare process, the customer-centered healthcare process requires more knowledge and modeling such a process is extremely complex. Thus, building a care process model for a special customer is cost prohibitive. In addition, during the execution of a care process model, the information system should have flexibility to modify the model so that it adapts to changes in the healthcare process. Therefore, supporting the process in a flexible, cost-effective way is a key challenge for information technology. To meet this challenge, first, we analyze various kinds of knowledge used in process modeling, illustrate their characteristics, and detail their roles and effects in careflow modeling. Secondly, we propose a methodology to manage a lifecycle of the healthcare process modeling, with which models could be built gradually with convenience and efficiency. In this lifecycle, different levels of process models are established based on the kinds of knowledge involved, and the diffusion strategy of these process models is designed. Thirdly, architecture and prototype of the system supporting the process modeling and its lifecycle are given. This careflow system also considers the compatibility of legacy systems and authority problems. Finally, an example is provided to demonstrate implementation of the careflow system.

An assembly process model based on object-oriented hierarchical time Petri Nets

NASA Astrophysics Data System (ADS)

Wang, Jiapeng; Liu, Shaoli; Liu, Jianhua; Du, Zenghui

2017-04-01

In order to improve the versatility, accuracy and integrity of the assembly process model of complex products, an assembly process model based on object-oriented hierarchical time Petri Nets is presented. A complete assembly process information model including assembly resources, assembly inspection, time, structure and flexible parts is established, and this model describes the static and dynamic data involved in the assembly process. Through the analysis of three-dimensional assembly process information, the assembly information is hierarchically divided from the whole, the local to the details and the subnet model of different levels of object-oriented Petri Nets is established. The communication problem between Petri subnets is solved by using message database, and it reduces the complexity of system modeling effectively. Finally, the modeling process is presented, and a five layer Petri Nets model is established based on the hoisting process of the engine compartment of a wheeled armored vehicle.

A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models

NASA Astrophysics Data System (ADS)

Brugnach, M.; Neilson, R.; Bolte, J.

2001-12-01

The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in the output are identified, the causes of its variability can be found. Some of the advantages of this approach are that it reduces the dimensionality of the search space, it facilitates the interpretation of the results and it provides information that allows exploration of uncertainty at the process level, and how it might affect model output. We present an example using the vegetation model BIOME-BGC.

Business Process Modeling: Perceived Benefits

NASA Astrophysics Data System (ADS)

Indulska, Marta; Green, Peter; Recker, Jan; Rosemann, Michael

The process-centered design of organizations and information systems is globally seen as an appropriate response to the increased economic pressure on organizations. At the methodological core of process-centered management is process modeling. However, business process modeling in large initiatives can be a time-consuming and costly exercise, making it potentially difficult to convince executive management of its benefits. To date, and despite substantial interest and research in the area of process modeling, the understanding of the actual benefits of process modeling in academia and practice is limited. To address this gap, this paper explores the perception of benefits derived from process modeling initiatives, as reported through a global Delphi study. The study incorporates the views of three groups of stakeholders - academics, practitioners and vendors. Our findings lead to the first identification and ranking of 19 unique benefits associated with process modeling. The study in particular found that process modeling benefits vary significantly between practitioners and academics. We argue that the variations may point to a disconnect between research projects and practical demands.

Negative Binomial Process Count and Mixture Modeling.

PubMed

Zhou, Mingyuan; Carin, Lawrence

2015-02-01

The seemingly disjoint problems of count and mixture modeling are united under the negative binomial (NB) process. A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process for count modeling. A draw from the NB process consists of a Poisson distributed finite number of distinct atoms, each of which is associated with a logarithmic distributed number of data samples. We reveal relationships between various count- and mixture-modeling distributions and construct a Poisson-logarithmic bivariate distribution that connects the NB and Chinese restaurant table distributions. Fundamental properties of the models are developed, and we derive efficient Bayesian inference. It is shown that with augmentation and normalization, the NB process and gamma-NB process can be reduced to the Dirichlet process and hierarchical Dirichlet process, respectively. These relationships highlight theoretical, structural, and computational advantages of the NB process. A variety of NB processes, including the beta-geometric, beta-NB, marked-beta-NB, marked-gamma-NB and zero-inflated-NB processes, with distinct sharing mechanisms, are also constructed. These models are applied to topic modeling, with connections made to existing algorithms under Poisson factor analysis. Example results show the importance of inferring both the NB dispersion and probability parameters.

How to build a course in mathematical-biological modeling: content and processes for knowledge and skill.

PubMed

Hoskinson, Anne-Marie

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments.

How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

PubMed Central

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical–biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments. PMID:20810966

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

PubMed

Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

2018-05-24

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

Computer modeling of lung cancer diagnosis-to-treatment process

PubMed Central

Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick

2015-01-01

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed. PMID:26380181

Models of recognition: a review of arguments in favor of a dual-process account.

PubMed

Diana, Rachel A; Reder, Lynne M; Arndt, Jason; Park, Heekyeong

2006-02-01

The majority of computationally specified models of recognition memory have been based on a single-process interpretation, claiming that familiarity is the only influence on recognition. There is increasing evidence that recognition is, in fact, based on two processes: recollection and familiarity. This article reviews the current state of the evidence for dual-process models, including the usefulness of the remember/know paradigm, and interprets the relevant results in terms of the source of activation confusion (SAC) model of memory. We argue that the evidence from each of the areas we discuss, when combined, presents a strong case that inclusion of a recollection process is necessary. Given this conclusion, we also argue that the dual-process claim that the recollection process is always available is, in fact, more parsimonious than the single-process claim that the recollection process is used only in certain paradigms. The value of a well-specified process model such as the SAC model is discussed with regard to other types of dual-process models.

Implementation of the Business Process Modelling Notation (BPMN) in the modelling of anatomic pathology processes.

PubMed

Rojo, Marcial García; Rolón, Elvira; Calahorra, Luis; García, Felix Oscar; Sánchez, Rosario Paloma; Ruiz, Francisco; Ballester, Nieves; Armenteros, María; Rodríguez, Teresa; Espartero, Rafael Martín

2008-07-15

Process orientation is one of the essential elements of quality management systems, including those in use in healthcare. Business processes in hospitals are very complex and variable. BPMN (Business Process Modelling Notation) is a user-oriented language specifically designed for the modelling of business (organizational) processes. Previous experiences of the use of this notation in the processes modelling within the Pathology in Spain or another country are not known. We present our experience in the elaboration of the conceptual models of Pathology processes, as part of a global programmed surgical patient process, using BPMN. With the objective of analyzing the use of BPMN notation in real cases, a multidisciplinary work group was created, including software engineers from the Dep. of Technologies and Information Systems from the University of Castilla-La Mancha and health professionals and administrative staff from the Hospital General de Ciudad Real. The work in collaboration was carried out in six phases: informative meetings, intensive training, process selection, definition of the work method, process describing by hospital experts, and process modelling. The modelling of the processes of Anatomic Pathology is presented using BPMN. The presented subprocesses are those corresponding to the surgical pathology examination of the samples coming from operating theatre, including the planning and realization of frozen studies. The modelling of Anatomic Pathology subprocesses has allowed the creation of an understandable graphical model, where management and improvements are more easily implemented by health professionals.

A new model integrating short- and long-term aging of copper added to soils

PubMed Central

Zeng, Saiqi; Li, Jumei; Wei, Dongpu

2017-01-01

Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature. PMID:28820888

Implementation of the Business Process Modelling Notation (BPMN) in the modelling of anatomic pathology processes

PubMed Central

Rojo, Marcial García; Rolón, Elvira; Calahorra, Luis; García, Felix Óscar; Sánchez, Rosario Paloma; Ruiz, Francisco; Ballester, Nieves; Armenteros, María; Rodríguez, Teresa; Espartero, Rafael Martín

2008-01-01

Background Process orientation is one of the essential elements of quality management systems, including those in use in healthcare. Business processes in hospitals are very complex and variable. BPMN (Business Process Modelling Notation) is a user-oriented language specifically designed for the modelling of business (organizational) processes. Previous experiences of the use of this notation in the processes modelling within the Pathology in Spain or another country are not known. We present our experience in the elaboration of the conceptual models of Pathology processes, as part of a global programmed surgical patient process, using BPMN. Methods With the objective of analyzing the use of BPMN notation in real cases, a multidisciplinary work group was created, including software engineers from the Dep. of Technologies and Information Systems from the University of Castilla-La Mancha and health professionals and administrative staff from the Hospital General de Ciudad Real. The work in collaboration was carried out in six phases: informative meetings, intensive training, process selection, definition of the work method, process describing by hospital experts, and process modelling. Results The modelling of the processes of Anatomic Pathology is presented using BPMN. The presented subprocesses are those corresponding to the surgical pathology examination of the samples coming from operating theatre, including the planning and realization of frozen studies. Conclusion The modelling of Anatomic Pathology subprocesses has allowed the creation of an understandable graphical model, where management and improvements are more easily implemented by health professionals. PMID:18673511

Statistically qualified neuro-analytic failure detection method and system

DOEpatents

Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.

2002-03-02

An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system. Illustrative of the method and apparatus, the method is applied to a peristaltic pump system.

An object-oriented description method of EPMM process

NASA Astrophysics Data System (ADS)

Jiang, Zuo; Yang, Fan

2017-06-01

In order to use the object-oriented mature tools and language in software process model, make the software process model more accord with the industrial standard, it’s necessary to study the object-oriented modelling of software process. Based on the formal process definition in EPMM, considering the characteristics that Petri net is mainly formal modelling tool and combining the Petri net modelling with the object-oriented modelling idea, this paper provides this implementation method to convert EPMM based on Petri net into object models based on object-oriented description.

Comparison of complex and parsimonious model structures by means of a modular hydrological model concept

NASA Astrophysics Data System (ADS)

Holzmann, Hubert; Massmann, Carolina

2015-04-01

A plenty of hydrological model types have been developed during the past decades. Most of them used a fixed design to describe the variable hydrological processes assuming to be representative for the whole range of spatial and temporal scales. This assumption is questionable as it is evident, that the runoff formation process is driven by dominant processes which can vary among different basins. Furthermore the model application and the interpretation of results is limited by data availability to identify the particular sub-processes, since most models were calibrated and validated only with discharge data. Therefore it can be hypothesized, that simpler model designs, focusing only on the dominant processes, can achieve comparable results with the benefit of less parameters. In the current contribution a modular model concept will be introduced, which allows the integration and neglection of hydrological sub-processes depending on the catchment characteristics and data availability. Key elements of the process modules refer to (1) storage effects (interception, soil), (2) transfer processes (routing), (3) threshold processes (percolation, saturation overland flow) and (4) split processes (rainfall excess). Based on hydro-meteorological observations in an experimental catchment in the Slovak region of the Carpathian mountains a comparison of several model realizations with different degrees of complexity will be discussed. A special focus is given on model parameter sensitivity estimated by Markov Chain Monte Carlo approach. Furthermore the identification of dominant processes by means of Sobol's method is introduced. It could be shown that a flexible model design - and even the simple concept - can reach comparable and equivalent performance than the standard model type (HBV-type). The main benefit of the modular concept is the individual adaptation of the model structure with respect to data and process availability and the option for parsimonious model design.

Application fields for the new Object Management Group (OMG) Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN) in the perioperative field.

PubMed

Wiemuth, M; Junger, D; Leitritz, M A; Neumann, J; Neumuth, T; Burgert, O

2017-08-01

Medical processes can be modeled using different methods and notations. Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail. We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN). First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention. An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

PubMed

Roh, S D; Kim, S W; Cho, W S

2001-10-01

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

Multidimensional Data Modeling for Business Process Analysis

NASA Astrophysics Data System (ADS)

Mansmann, Svetlana; Neumuth, Thomas; Scholl, Marc H.

The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models.

Towards simplification of hydrologic modeling: Identification of dominant processes

USGS Publications Warehouse

Markstrom, Steven; Hay, Lauren E.; Clark, Martyn P.

2016-01-01

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Studying the Accuracy of Software Process Elicitation: The User Articulated Model

ERIC Educational Resources Information Center

Crabtree, Carlton A.

2010-01-01

Process models are often the basis for demonstrating improvement and compliance in software engineering organizations. A descriptive model is a type of process model describing the human activities in software development that actually occur. The purpose of a descriptive model is to provide a documented baseline for further process improvement…

Sensory processing and world modeling for an active ranging device

NASA Technical Reports Server (NTRS)

Hong, Tsai-Hong; Wu, Angela Y.

1991-01-01

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented.

Using Dispersed Modes During Model Correlation

NASA Technical Reports Server (NTRS)

Stewart, Eric C.; Hathcock, Megan L.

2017-01-01

The model correlation process for the modal characteristics of a launch vehicle is well established. After a test, parameters within the nominal model are adjusted to reflect structural dynamics revealed during testing. However, a full model correlation process for a complex structure can take months of man-hours and many computational resources. If the analyst only has weeks, or even days, of time in which to correlate the nominal model to the experimental results, then the traditional correlation process is not suitable. This paper describes using model dispersions to assist the model correlation process and decrease the overall cost of the process. The process creates thousands of model dispersions from the nominal model prior to the test and then compares each of them to the test data. Using mode shape and frequency error metrics, one dispersion is selected as the best match to the test data. This dispersion is further improved by using a commercial model correlation software. In the three examples shown in this paper, this dispersion based model correlation process performs well when compared to models correlated using traditional techniques and saves time in the post-test analysis.

Models of recognition: A review of arguments in favor of a dual-process account

PubMed Central

DIANA, RACHEL A.; REDER, LYNNE M.; ARNDT, JASON; PARK, HEEKYEONG

2008-01-01

The majority of computationally specified models of recognition memory have been based on a single-process interpretation, claiming that familiarity is the only influence on recognition. There is increasing evidence that recognition is, in fact, based on two processes: recollection and familiarity. This article reviews the current state of the evidence for dual-process models, including the usefulness of the remember/know paradigm, and interprets the relevant results in terms of the source of activation confusion (SAC) model of memory. We argue that the evidence from each of the areas we discuss, when combined, presents a strong case that inclusion of a recollection process is necessary. Given this conclusion, we also argue that the dual-process claim that the recollection process is always available is, in fact, more parsimonious than the single-process claim that the recollection process is used only in certain paradigms. The value of a well-specified process model such as the SAC model is discussed with regard to other types of dual-process models. PMID:16724763

An Implicit Model Development Process for Bounding External, Seemingly Intangible/Non-Quantifiable Factors

DTIC Science & Technology

2017-06-01

This research expands the modeling and simulation (M and S) body of knowledge through the development of an Implicit Model Development Process (IMDP...When augmented to traditional Model Development Processes (MDP), the IMDP enables the development of models that can address a broader array of...where a broader, more holistic approach of defining a models referent is achieved. Next, the IMDP codifies the process for implementing the improved model

System Engineering Concept Demonstration, Process Model. Volume 3

DTIC Science & Technology

1992-12-01

Process or Process Model The System Engineering process must be the enactment of the aforementioned definitions. Therefore, a process is an enactment of a...Prototype Tradeoff Scenario demonstrates six levels of abstraction in the Process Model. The Process Model symbology is explained within the "Help" icon ...dnofing no- ubeq t"vidi e /hn -am-a. lmi IzyuO ..pu Row _e._n au"c.ue-w’ ’- anuiildyidwile b ie htplup ?~imsav D symbo ,,ue,.dvu ,,dienl Flw s--..,fu..I

Aspect-Oriented Business Process Modeling with AO4BPMN

NASA Astrophysics Data System (ADS)

Charfi, Anis; Müller, Heiko; Mezini, Mira

Many crosscutting concerns in business processes need to be addressed already at the business process modeling level such as compliance, auditing, billing, and separation of duties. However, existing business process modeling languages including OMG's Business Process Modeling Notation (BPMN) lack appropriate means for expressing such concerns in a modular way. In this paper, we motivate the need for aspect-oriented concepts in business process modeling languages and propose an aspect-oriented extension to BPMN called AO4BPMN. We also present a graphical editor supporting that extension.

Model for Simulating a Spiral Software-Development Process

NASA Technical Reports Server (NTRS)

Mizell, Carolyn; Curley, Charles; Nayak, Umanath

2010-01-01

A discrete-event simulation model, and a computer program that implements the model, have been developed as means of analyzing a spiral software-development process. This model can be tailored to specific development environments for use by software project managers in making quantitative cases for deciding among different software-development processes, courses of action, and cost estimates. A spiral process can be contrasted with a waterfall process, which is a traditional process that consists of a sequence of activities that include analysis of requirements, design, coding, testing, and support. A spiral process is an iterative process that can be regarded as a repeating modified waterfall process. Each iteration includes assessment of risk, analysis of requirements, design, coding, testing, delivery, and evaluation. A key difference between a spiral and a waterfall process is that a spiral process can accommodate changes in requirements at each iteration, whereas in a waterfall process, requirements are considered to be fixed from the beginning and, therefore, a waterfall process is not flexible enough for some projects, especially those in which requirements are not known at the beginning or may change during development. For a given project, a spiral process may cost more and take more time than does a waterfall process, but may better satisfy a customer's expectations and needs. Models for simulating various waterfall processes have been developed previously, but until now, there have been no models for simulating spiral processes. The present spiral-process-simulating model and the software that implements it were developed by extending a discrete-event simulation process model of the IEEE 12207 Software Development Process, which was built using commercially available software known as the Process Analysis Tradeoff Tool (PATT). Typical inputs to PATT models include industry-average values of product size (expressed as number of lines of code), productivity (number of lines of code per hour), and number of defects per source line of code. The user provides the number of resources, the overall percent of effort that should be allocated to each process step, and the number of desired staff members for each step. The output of PATT includes the size of the product, a measure of effort, a measure of rework effort, the duration of the entire process, and the numbers of injected, detected, and corrected defects as well as a number of other interesting features. In the development of the present model, steps were added to the IEEE 12207 waterfall process, and this model and its implementing software were made to run repeatedly through the sequence of steps, each repetition representing an iteration in a spiral process. Because the IEEE 12207 model is founded on a waterfall paradigm, it enables direct comparison of spiral and waterfall processes. The model can be used throughout a software-development project to analyze the project as more information becomes available. For instance, data from early iterations can be used as inputs to the model, and the model can be used to estimate the time and cost of carrying the project to completion.

Laser welding of polymers: phenomenological model for a quick and reliable process quality estimation considering beam shape influences

NASA Astrophysics Data System (ADS)

Timpe, Nathalie F.; Stuch, Julia; Scholl, Marcus; Russek, Ulrich A.

2016-03-01

This contribution presents a phenomenological, analytical model for laser welding of polymers which is suited for a quick process quality estimation for the practitioner. Besides material properties of the polymer and processing parameters like welding pressure, feed rate and laser power the model is based on a simple few parameter description of the size and shape of the laser power density distribution (PDD) in the processing zone. The model allows an estimation of the weld seam tensile strength. It is based on energy balance considerations within a thin sheet with the thickness of the optical penetration depth on the surface of the absorbing welding partner. The joining process itself is modelled by a phenomenological approach. The model reproduces the experimentally known process windows for the main process parameters correctly. Using the parameters describing the shape of the laser PDD the critical dependence of the process windows on the PDD shape will be predicted and compared with experiments. The adaption of the model to other laser manufacturing processes where the PDD influence can be modelled comparably will be discussed.

A Model of Process-Based Automation: Cost and Quality Implications in the Medication Management Process

ERIC Educational Resources Information Center

Spaulding, Trent Joseph

2011-01-01

The objective of this research is to understand how a set of systems, as defined by the business process, creates value. The three studies contained in this work develop the model of process-based automation. The model states that complementarities among systems are specified by handoffs in the business process. The model also provides theory to…

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY

PubMed Central

Somogyi, Endre; Hagar, Amit; Glazier, James A.

2017-01-01

Living tissues are dynamic, heterogeneous compositions of objects, including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes. Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology (CCOPM) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models. PMID:29282379

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY.

PubMed

Somogyi, Endre; Hagar, Amit; Glazier, James A

2016-12-01

Living tissues are dynamic, heterogeneous compositions of objects , including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes . Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology ( CCOPM ) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement.

PubMed

Kumarapeli, P; De Lusignan, S; Ellis, T; Jones, B

2007-03-01

The Primary Care Data Quality programme (PCDQ) is a quality-improvement programme which processes routinely collected general practice computer data. Patient data collected from a wide range of different brands of clinical computer systems are aggregated, processed, and fed back to practices in an educational context to improve the quality of care. Process modelling is a well-established approach used to gain understanding and systematic appraisal, and identify areas of improvement of a business process. Unified modelling language (UML) is a general purpose modelling technique used for this purpose. We used UML to appraise the PCDQ process to see if the efficiency and predictability of the process could be improved. Activity analysis and thinking-aloud sessions were used to collect data to generate UML diagrams. The UML model highlighted the sequential nature of the current process as a barrier for efficiency gains. It also identified the uneven distribution of process controls, lack of symmetric communication channels, critical dependencies among processing stages, and failure to implement all the lessons learned in the piloting phase. It also suggested that improved structured reporting at each stage - especially from the pilot phase, parallel processing of data and correctly positioned process controls - should improve the efficiency and predictability of research projects. Process modelling provided a rational basis for the critical appraisal of a clinical data processing system; its potential maybe underutilized within health care.

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

ERIC Educational Resources Information Center

Soroush, Masoud; Weinberger, Charles B.

2010-01-01

This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

A model of human decision making in multiple process monitoring situations

NASA Technical Reports Server (NTRS)

Greenstein, J. S.; Rouse, W. B.

1982-01-01

Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.

How certain are the process parameterizations in our models?

NASA Astrophysics Data System (ADS)

Gharari, Shervan; Hrachowitz, Markus; Fenicia, Fabrizio; Matgen, Patrick; Razavi, Saman; Savenije, Hubert; Gupta, Hoshin; Wheater, Howard

2016-04-01

Environmental models are abstract simplifications of real systems. As a result, the elements of these models, including system architecture (structure), process parameterization and parameters inherit a high level of approximation and simplification. In a conventional model building exercise the parameter values are the only elements of a model which can vary while the rest of the modeling elements are often fixed a priori and therefore not subjected to change. Once chosen the process parametrization and model structure usually remains the same throughout the modeling process. The only flexibility comes from the changing parameter values, thereby enabling these models to reproduce the desired observation. This part of modeling practice, parameter identification and uncertainty, has attracted a significant attention in the literature during the last years. However what remains unexplored in our point of view is to what extent the process parameterization and system architecture (model structure) can support each other. In other words "Does a specific form of process parameterization emerge for a specific model given its system architecture and data while no or little assumption has been made about the process parameterization itself? In this study we relax the assumption regarding a specific pre-determined form for the process parameterizations of a rainfall/runoff model and examine how varying the complexity of the system architecture can lead to different or possibly contradictory parameterization forms than what would have been decided otherwise. This comparison implicitly and explicitly provides us with an assessment of how uncertain is our perception of model process parameterization in respect to the extent the data can support.

Model-based software process improvement

NASA Technical Reports Server (NTRS)

Zettervall, Brenda T.

1994-01-01

The activities of a field test site for the Software Engineering Institute's software process definition project are discussed. Products tested included the improvement model itself, descriptive modeling techniques, the CMM level 2 framework document, and the use of process definition guidelines and templates. The software process improvement model represents a five stage cyclic approach for organizational process improvement. The cycles consist of the initiating, diagnosing, establishing, acting, and leveraging phases.

Simulating The Technological Movements Of The Equipment Used For Manufacturing Prosthetic Devices Using 3D Models

NASA Astrophysics Data System (ADS)

Chicea, Anca-Lucia

2015-09-01

The paper presents the process of building geometric and kinematic models of a technological equipment used in the process of manufacturing devices. First, the process of building the model for a six axes industrial robot is presented. In the second part of the paper, the process of building the model for a five-axis CNC milling machining center is also shown. Both models can be used for accurate cutting processes simulation of complex parts, such as prosthetic devices.

Process Correlation Analysis Model for Process Improvement Identification

PubMed Central

Park, Sooyong

2014-01-01

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data. PMID:24977170

Process correlation analysis model for process improvement identification.

PubMed

Choi, Su-jin; Kim, Dae-Kyoo; Park, Sooyong

2014-01-01

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data.

Principal process analysis of biological models.

PubMed

Casagranda, Stefano; Touzeau, Suzanne; Ropers, Delphine; Gouzé, Jean-Luc

2018-06-14

Understanding the dynamical behaviour of biological systems is challenged by their large number of components and interactions. While efforts have been made in this direction to reduce model complexity, they often prove insufficient to grasp which and when model processes play a crucial role. Answering these questions is fundamental to unravel the functioning of living organisms. We design a method for dealing with model complexity, based on the analysis of dynamical models by means of Principal Process Analysis. We apply the method to a well-known model of circadian rhythms in mammals. The knowledge of the system trajectories allows us to decompose the system dynamics into processes that are active or inactive with respect to a certain threshold value. Process activities are graphically represented by Boolean and Dynamical Process Maps. We detect model processes that are always inactive, or inactive on some time interval. Eliminating these processes reduces the complex dynamics of the original model to the much simpler dynamics of the core processes, in a succession of sub-models that are easier to analyse. We quantify by means of global relative errors the extent to which the simplified models reproduce the main features of the original system dynamics and apply global sensitivity analysis to test the influence of model parameters on the errors. The results obtained prove the robustness of the method. The analysis of the sub-model dynamics allows us to identify the source of circadian oscillations. We find that the negative feedback loop involving proteins PER, CRY, CLOCK-BMAL1 is the main oscillator, in agreement with previous modelling and experimental studies. In conclusion, Principal Process Analysis is a simple-to-use method, which constitutes an additional and useful tool for analysing the complex dynamical behaviour of biological systems.

Mathematical Model of Nonstationary Separation Processes Proceeding in the Cascade of Gas Centrifuges in the Process of Separation of Multicomponent Isotope Mixtures

NASA Astrophysics Data System (ADS)

Orlov, A. A.; Ushakov, A. A.; Sovach, V. P.

2017-03-01

We have developed and realized on software a mathematical model of the nonstationary separation processes proceeding in the cascades of gas centrifuges in the process of separation of multicomponent isotope mixtures. With the use of this model the parameters of the separation process of germanium isotopes have been calculated. It has been shown that the model adequately describes the nonstationary processes in the cascade and is suitable for calculating their parameters in the process of separation of multicomponent isotope mixtures.

A Typology for Modeling Processes in Clinical Guidelines and Protocols

NASA Astrophysics Data System (ADS)

Tu, Samson W.; Musen, Mark A.

We analyzed the graphical representations that are used by various guideline-modeling methods to express process information embodied in clinical guidelines and protocols. From this analysis, we distilled four modeling formalisms and the processes they typically model: (1) flowcharts for capturing problem-solving processes, (2) disease-state maps that link decision points in managing patient problems over time, (3) plans that specify sequences of activities that contribute toward a goal, (4) workflow specifications that model care processes in an organization. We characterized the four approaches and showed that each captures some aspect of what a guideline may specify. We believe that a general guideline-modeling system must provide explicit representation for each type of process.

Application of simulation models for the optimization of business processes

NASA Astrophysics Data System (ADS)

Jašek, Roman; Sedláček, Michal; Chramcov, Bronislav; Dvořák, Jiří

2016-06-01

The paper deals with the applications of modeling and simulation tools in the optimization of business processes, especially in solving an optimization of signal flow in security company. As a modeling tool was selected Simul8 software that is used to process modeling based on discrete event simulation and which enables the creation of a visual model of production and distribution processes.

A Comparative of business process modelling techniques

NASA Astrophysics Data System (ADS)

Tangkawarow, I. R. H. T.; Waworuntu, J.

2016-04-01

In this era, there is a lot of business process modeling techniques. This article is the research about differences of business process modeling techniques. For each technique will explain about the definition and the structure. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on 2 criteria: notation and how it works when implemented in Somerleyton Animal Park. Each technique will end with the advantages and disadvantages. The final conclusion will give recommend of business process modeling techniques that easy to use and serve the basis for evaluating further modelling techniques.

Expert models and modeling processes associated with a computer-modeling tool

NASA Astrophysics Data System (ADS)

Zhang, Baohui; Liu, Xiufeng; Krajcik, Joseph S.

2006-07-01

Holding the premise that the development of expertise is a continuous process, this study concerns expert models and modeling processes associated with a modeling tool called Model-It. Five advanced Ph.D. students in environmental engineering and public health used Model-It to create and test models of water quality. Using think aloud technique and video recording, we captured their computer screen modeling activities and thinking processes. We also interviewed them the day following their modeling sessions to further probe the rationale of their modeling practices. We analyzed both the audio-video transcripts and the experts' models. We found the experts' modeling processes followed the linear sequence built in the modeling program with few instances of moving back and forth. They specified their goals up front and spent a long time thinking through an entire model before acting. They specified relationships with accurate and convincing evidence. Factors (i.e., variables) in expert models were clustered, and represented by specialized technical terms. Based on the above findings, we made suggestions for improving model-based science teaching and learning using Model-It.

Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems

NASA Astrophysics Data System (ADS)

Hunter, Jason M.; Maier, Holger R.; Gibbs, Matthew S.; Foale, Eloise R.; Grosvenor, Naomi A.; Harders, Nathan P.; Kikuchi-Miller, Tahali C.

2018-05-01

Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process- or data-driven. The primary problem with process-based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically. There are also often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs) in comparison, is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a generic framework for developing hybrid process and data-driven models of salinity in river systems is introduced and applied in this paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, the degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the Murray River in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, accession of saline groundwater along the length of the reach and the flushing of three waterbodies in the floodplain during overbank flows of various magnitudes. Based on trade-offs between the degree of process understanding and data availability, a process-driven model is developed for in-stream salt transport, an ANN model is used to model saline groundwater accession and three linear regression models are used to account for the flushing of the different floodplain storages. The resulting hybrid model performs very well on approximately 3 years of daily validation data, with a Nash-Sutcliffe efficiency (NSE) of 0.89 and a root mean squared error (RMSE) of 12.62 mg L-1 (over a range from approximately 50 to 250 mg L-1). Each component of the hybrid model results in noticeable improvements in model performance corresponding to the range of flows for which they are developed. The predictive performance of the hybrid model is significantly better than that of a benchmark process-driven model (NSE = -0.14, RMSE = 41.10 mg L-1, Gbench index = 0.90) and slightly better than that of a benchmark data-driven (ANN) model (NSE = 0.83, RMSE = 15.93 mg L-1, Gbench index = 0.36). Apart from improved predictive performance, the hybrid model also has advantages over the ANN benchmark model in terms of increased capacity for improving system understanding and greater ability to support management decisions.

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, B.; Wood, R.T.

1997-04-22

A method is described for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical model. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system. 1 fig.

Comparative evaluation of urban storm water quality models

NASA Astrophysics Data System (ADS)

Vaze, J.; Chiew, Francis H. S.

2003-10-01

The estimation of urban storm water pollutant loads is required for the development of mitigation and management strategies to minimize impacts to receiving environments. Event pollutant loads are typically estimated using either regression equations or "process-based" water quality models. The relative merit of using regression models compared to process-based models is not clear. A modeling study is carried out here to evaluate the comparative ability of the regression equations and process-based water quality models to estimate event diffuse pollutant loads from impervious surfaces. The results indicate that, once calibrated, both the regression equations and the process-based model can estimate event pollutant loads satisfactorily. In fact, the loads estimated using the regression equation as a function of rainfall intensity and runoff rate are better than the loads estimated using the process-based model. Therefore, if only estimates of event loads are required, regression models should be used because they are simpler and require less data compared to process-based models.

Application of Characterization, Modeling, and Analytics Towards Understanding Process Structure Linkages in Metallic 3D Printing (Postprint)

DTIC Science & Technology

2017-08-01

of metallic additive manufacturing processes and show that combining experimental data with modelling and advanced data processing and analytics...manufacturing processes and show that combining experimental data with modelling and advanced data processing and analytics methods will accelerate that...geometries, we develop a methodology that couples experimental data and modelling to convert the scan paths into spatially resolved local thermal histories

The RiverFish Approach to Business Process Modeling: Linking Business Steps to Control-Flow Patterns

NASA Astrophysics Data System (ADS)

Zuliane, Devanir; Oikawa, Marcio K.; Malkowski, Simon; Alcazar, José Perez; Ferreira, João Eduardo

Despite the recent advances in the area of Business Process Management (BPM), today’s business processes have largely been implemented without clearly defined conceptual modeling. This results in growing difficulties for identification, maintenance, and reuse of rules, processes, and control-flow patterns. To mitigate these problems in future implementations, we propose a new approach to business process modeling using conceptual schemas, which represent hierarchies of concepts for rules and processes shared among collaborating information systems. This methodology bridges the gap between conceptual model description and identification of actual control-flow patterns for workflow implementation. We identify modeling guidelines that are characterized by clear phase separation, step-by-step execution, and process building through diagrams and tables. The separation of business process modeling in seven mutually exclusive phases clearly delimits information technology from business expertise. The sequential execution of these phases leads to the step-by-step creation of complex control-flow graphs. The process model is refined through intuitive table and diagram generation in each phase. Not only does the rigorous application of our modeling framework minimize the impact of rule and process changes, but it also facilitates the identification and maintenance of control-flow patterns in BPM-based information system architectures.

Conceptual and logical level of database modeling

NASA Astrophysics Data System (ADS)

Hunka, Frantisek; Matula, Jiri

2016-06-01

Conceptual and logical levels form the top most levels of database modeling. Usually, ORM (Object Role Modeling) and ER diagrams are utilized to capture the corresponding schema. The final aim of business process modeling is to store its results in the form of database solution. For this reason, value oriented business process modeling which utilizes ER diagram to express the modeling entities and relationships between them are used. However, ER diagrams form the logical level of database schema. To extend possibilities of different business process modeling methodologies, the conceptual level of database modeling is needed. The paper deals with the REA value modeling approach to business process modeling using ER-diagrams, and derives conceptual model utilizing ORM modeling approach. Conceptual model extends possibilities for value modeling to other business modeling approaches.

Measuring health care process quality with software quality measures.

PubMed

Yildiz, Ozkan; Demirörs, Onur

2012-01-01

Existing quality models focus on some specific diseases, clinics or clinical areas. Although they contain structure, process, or output type measures, there is no model which measures quality of health care processes comprehensively. In addition, due to the not measured overall process quality, hospitals cannot compare quality of processes internally and externally. To bring a solution to above problems, a new model is developed from software quality measures. We have adopted the ISO/IEC 9126 software quality standard for health care processes. Then, JCIAS (Joint Commission International Accreditation Standards for Hospitals) measurable elements were added to model scope for unifying functional requirements. Assessment (diagnosing) process measurement results are provided in this paper. After the application, it was concluded that the model determines weak and strong aspects of the processes, gives a more detailed picture for the process quality, and provides quantifiable information to hospitals to compare their processes with multiple organizations.

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Treesearch

Louis R. Iverson; Frank R. Thompson; Stephen Matthews; Matthew Peters; Anantha Prasad; William D. Dijak; Jacob Fraser; Wen J. Wang; Brice Hanberry; Hong He; Maria Janowiak; Patricia Butler; Leslie Brandt; Chris Swanston

2016-01-01

Context. Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process...

Diffusion models of the flanker task: Discrete versus gradual attentional selection

PubMed Central

White, Corey N.; Ratcliff, Roger; Starns, Jeffrey S.

2011-01-01

The present study tested diffusion models of processing in the flanker task, in which participants identify a target that is flanked by items that indicate the same (congruent) or opposite response (incongruent). Single- and dual-process flanker models were implemented in a diffusion-model framework and tested against data from experiments that manipulated response bias, speed/accuracy tradeoffs, attentional focus, and stimulus configuration. There was strong mimcry among the models, and each captured the main trends in the data for the standard conditions. However, when more complex conditions were used, a single-process spotlight model captured qualitative and quantitative patterns that the dual-process models could not. Since the single-process model provided the best balance of fit quality and parsimony, the results indicate that processing in the simple versions of the flanker task is better described by gradual rather than discrete narrowing of attention. PMID:21964663

Composing Models of Geographic Physical Processes

NASA Astrophysics Data System (ADS)

Hofer, Barbara; Frank, Andrew U.

Processes are central for geographic information science; yet geographic information systems (GIS) lack capabilities to represent process related information. A prerequisite to including processes in GIS software is a general method to describe geographic processes independently of application disciplines. This paper presents such a method, namely a process description language. The vocabulary of the process description language is derived formally from mathematical models. Physical processes in geography can be described in two equivalent languages: partial differential equations or partial difference equations, where the latter can be shown graphically and used as a method for application specialists to enter their process models. The vocabulary of the process description language comprises components for describing the general behavior of prototypical geographic physical processes. These process components can be composed by basic models of geographic physical processes, which is shown by means of an example.

Process Modeling and Dynamic Simulation for EAST Helium Refrigerator

NASA Astrophysics Data System (ADS)

Lu, Xiaofei; Fu, Peng; Zhuang, Ming; Qiu, Lilong; Hu, Liangbing

2016-06-01

In this paper, the process modeling and dynamic simulation for the EAST helium refrigerator has been completed. The cryogenic process model is described and the main components are customized in detail. The process model is controlled by the PLC simulator, and the realtime communication between the process model and the controllers is achieved by a customized interface. Validation of the process model has been confirmed based on EAST experimental data during the cool down process of 300-80 K. Simulation results indicate that this process simulator is able to reproduce dynamic behaviors of the EAST helium refrigerator very well for the operation of long pulsed plasma discharge. The cryogenic process simulator based on control architecture is available for operation optimization and control design of EAST cryogenic systems to cope with the long pulsed heat loads in the future. supported by National Natural Science Foundation of China (No. 51306195) and Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, CAS (No. CRYO201408)

A Petri Net-Based Software Process Model for Developing Process-Oriented Information Systems

NASA Astrophysics Data System (ADS)

Li, Yu; Oberweis, Andreas

Aiming at increasing flexibility, efficiency, effectiveness, and transparency of information processing and resource deployment in organizations to ensure customer satisfaction and high quality of products and services, process-oriented information systems (POIS) represent a promising realization form of computerized business information systems. Due to the complexity of POIS, explicit and specialized software process models are required to guide POIS development. In this chapter we characterize POIS with an architecture framework and present a Petri net-based software process model tailored for POIS development with consideration of organizational roles. As integrated parts of the software process model, we also introduce XML nets, a variant of high-level Petri nets as basic methodology for business processes modeling, and an XML net-based software toolset providing comprehensive functionalities for POIS development.

Conceptual models of information processing

NASA Technical Reports Server (NTRS)

Stewart, L. J.

1983-01-01

The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.

Monitoring autocorrelated process: A geometric Brownian motion process approach

NASA Astrophysics Data System (ADS)

Li, Lee Siaw; Djauhari, Maman A.

2013-09-01

Autocorrelated process control is common in today's modern industrial process control practice. The current practice of autocorrelated process control is to eliminate the autocorrelation by using an appropriate model such as Box-Jenkins models or other models and then to conduct process control operation based on the residuals. In this paper we show that many time series are governed by a geometric Brownian motion (GBM) process. Therefore, in this case, by using the properties of a GBM process, we only need an appropriate transformation and model the transformed data to come up with the condition needs in traditional process control. An industrial example of cocoa powder production process in a Malaysian company will be presented and discussed to illustrate the advantages of the GBM approach.

Use of Statechart Assertions for Modeling Human-in-the-Loop Security Analysis and Decision-Making Processes

DTIC Science & Technology

2012-06-01

THIS PAGE INTENTIONALLY LEFT BLANK xv LIST OF ACRONYMS AND ABBREVIATIONS BPM Business Process Model BPMN Business Process Modeling Notation C&A...checking leads to an improvement in the quality and success of enterprise software development. Business Process Modeling Notation ( BPMN ) is an...emerging standard that allows business processes to be captured in a standardized format. BPMN lacks formal semantics which leaves many of its features

Modelling of additive manufacturing processes: a review and classification

NASA Astrophysics Data System (ADS)

Stavropoulos, Panagiotis; Foteinopoulos, Panagis

2018-03-01

Additive manufacturing (AM) is a very promising technology; however, there are a number of open issues related to the different AM processes. The literature on modelling the existing AM processes is reviewed and classified. A categorization of the different AM processes in process groups, according to the process mechanism, has been conducted and the most important issues are stated. Suggestions are made as to which approach is more appropriate according to the key performance indicator desired to be modelled and a discussion is included as to the way that future modelling work can better contribute to improving today's AM process understanding.

Visual Modelling of Learning Processes

ERIC Educational Resources Information Center

Copperman, Elana; Beeri, Catriel; Ben-Zvi, Nava

2007-01-01

This paper introduces various visual models for the analysis and description of learning processes. The models analyse learning on two levels: the dynamic level (as a process over time) and the functional level. Two types of model for dynamic modelling are proposed: the session trace, which documents a specific learner in a particular learning…

Multicriteria framework for selecting a process modelling language

NASA Astrophysics Data System (ADS)

Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel

2016-01-01

The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.

A dual-process perspective on fluency-based aesthetics: the pleasure-interest model of aesthetic liking.

PubMed

Graf, Laura K M; Landwehr, Jan R

2015-11-01

In this article, we develop an account of how aesthetic preferences can be formed as a result of two hierarchical, fluency-based processes. Our model suggests that processing performed immediately upon encountering an aesthetic object is stimulus driven, and aesthetic preferences that accrue from this processing reflect aesthetic evaluations of pleasure or displeasure. When sufficient processing motivation is provided by a perceiver's need for cognitive enrichment and/or the stimulus' processing affordance, elaborate perceiver-driven processing can emerge, which gives rise to fluency-based aesthetic evaluations of interest, boredom, or confusion. Because the positive outcomes in our model are pleasure and interest, we call it the Pleasure-Interest Model of Aesthetic Liking (PIA Model). Theoretically, this model integrates a dual-process perspective and ideas from lay epistemology into processing fluency theory, and it provides a parsimonious framework to embed and unite a wealth of aesthetic phenomena, including contradictory preference patterns for easy versus difficult-to-process aesthetic stimuli. © 2015 by the Society for Personality and Social Psychology, Inc.

Computational Process Modeling for Additive Manufacturing

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2014-01-01

Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.

Information-Processing Models and Curriculum Design

ERIC Educational Resources Information Center

Calfee, Robert C.

1970-01-01

"This paper consists of three sections--(a) the relation of theoretical analyses of learning to curriculum design, (b) the role of information-processing models in analyses of learning processes, and (c) selected examples of the application of information-processing models to curriculum design problems." (Author)

Business Performer-Centered Design of User Interfaces

NASA Astrophysics Data System (ADS)

Sousa, Kênia; Vanderdonckt, Jean

Business Performer-Centered Design of User Interfaces is a new design methodology that adopts business process (BP) definition and a business performer perspective for managing the life cycle of user interfaces of enterprise systems. In this methodology, when the organization has a business process culture, the business processes of an organization are firstly defined according to a traditional methodology for this kind of artifact. These business processes are then transformed into a series of task models that represent the interactive parts of the business processes that will ultimately lead to interactive systems. When the organization has its enterprise systems, but not yet its business processes modeled, the user interfaces of the systems help derive tasks models, which are then used to derive the business processes. The double linking between a business process and a task model, and between a task model and a user interface model makes it possible to ensure traceability of the artifacts in multiple paths and enables a more active participation of business performers in analyzing the resulting user interfaces. In this paper, we outline how a human-perspective is used tied to a model-driven perspective.

C. botulinum inactivation kinetics implemented in a computational model of a high-pressure sterilization process.

PubMed

Juliano, Pablo; Knoerzer, Kai; Fryer, Peter J; Versteeg, Cornelis

2009-01-01

High-pressure, high-temperature (HPHT) processing is effective for microbial spore inactivation using mild preheating, followed by rapid volumetric compression heating and cooling on pressure release, enabling much shorter processing times than conventional thermal processing for many food products. A computational thermal fluid dynamic (CTFD) model has been developed to model all processing steps, including the vertical pressure vessel, an internal polymeric carrier, and food packages in an axis-symmetric geometry. Heat transfer and fluid dynamic equations were coupled to four selected kinetic models for the inactivation of C. botulinum; the traditional first-order kinetic model, the Weibull model, an nth-order model, and a combined discrete log-linear nth-order model. The models were solved to compare the resulting microbial inactivation distributions. The initial temperature of the system was set to 90 degrees C and pressure was selected at 600 MPa, holding for 220 s, with a target temperature of 121 degrees C. A representation of the extent of microbial inactivation throughout all processing steps was obtained for each microbial model. Comparison of the models showed that the conventional thermal processing kinetics (not accounting for pressure) required shorter holding times to achieve a 12D reduction of C. botulinum spores than the other models. The temperature distribution inside the vessel resulted in a more uniform inactivation distribution when using a Weibull or an nth-order kinetics model than when using log-linear kinetics. The CTFD platform could illustrate the inactivation extent and uniformity provided by the microbial models. The platform is expected to be useful to evaluate models fitted into new C. botulinum inactivation data at varying conditions of pressure and temperature, as an aid for regulatory filing of the technology as well as in process and equipment design.

Moral judgment as information processing: an integrative review.

PubMed

Guglielmo, Steve

2015-01-01

How do humans make moral judgments about others' behavior? This article reviews dominant models of moral judgment, organizing them within an overarching framework of information processing. This framework poses two distinct questions: (1) What input information guides moral judgments? and (2) What psychological processes generate these judgments? Information Models address the first question, identifying critical information elements (including causality, intentionality, and mental states) that shape moral judgments. A subclass of Biased Information Models holds that perceptions of these information elements are themselves driven by prior moral judgments. Processing Models address the second question, and existing models have focused on the relative contribution of intuitive versus deliberative processes. This review organizes existing moral judgment models within this framework and critically evaluates them on empirical and theoretical grounds; it then outlines a general integrative model grounded in information processing, and concludes with conceptual and methodological suggestions for future research. The information-processing framework provides a useful theoretical lens through which to organize extant and future work in the rapidly growing field of moral judgment.

The use of mechanistic descriptions of algal growth and zooplankton grazing in an estuarine eutrophication model

NASA Astrophysics Data System (ADS)

Baird, M. E.; Walker, S. J.; Wallace, B. B.; Webster, I. T.; Parslow, J. S.

2003-03-01

A simple model of estuarine eutrophication is built on biomechanical (or mechanistic) descriptions of a number of the key ecological processes in estuaries. Mechanistically described processes include the nutrient uptake and light capture of planktonic and benthic autotrophs, and the encounter rates of planktonic predators and prey. Other more complex processes, such as sediment biogeochemistry, detrital processes and phosphate dynamics, are modelled using empirical descriptions from the Port Phillip Bay Environmental Study (PPBES) ecological model. A comparison is made between the mechanistically determined rates of ecological processes and the analogous empirically determined rates in the PPBES ecological model. The rates generally agree, with a few significant exceptions. Model simulations were run at a range of estuarine depths and nutrient loads, with outputs presented as the annually averaged biomass of autotrophs. The simulations followed a simple conceptual model of eutrophication, suggesting a simple biomechanical understanding of estuarine processes can provide a predictive tool for ecological processes in a wide range of estuarine ecosystems.

An Information System Development Method Connecting Business Process Modeling and its Experimental Evaluation

NASA Astrophysics Data System (ADS)

Okawa, Tsutomu; Kaminishi, Tsukasa; Kojima, Yoshiyuki; Hirabayashi, Syuichi; Koizumi, Hisao

Business process modeling (BPM) is gaining attention as a measure of analysis and improvement of the business process. BPM analyses the current business process as an AS-IS model and solves problems to improve the current business and moreover it aims to create a business process, which produces values, as a TO-BE model. However, researches of techniques that connect the business process improvement acquired by BPM to the implementation of the information system seamlessly are rarely reported. If the business model obtained by BPM is converted into UML, and the implementation can be carried out by the technique of UML, we can expect the improvement in efficiency of information system implementation. In this paper, we describe a method of the system development, which converts the process model obtained by BPM into UML and the method is evaluated by modeling a prototype of a parts procurement system. In the evaluation, comparison with the case where the system is implemented by the conventional UML technique without going via BPM is performed.

Moral judgment as information processing: an integrative review

PubMed Central

Guglielmo, Steve

2015-01-01

How do humans make moral judgments about others’ behavior? This article reviews dominant models of moral judgment, organizing them within an overarching framework of information processing. This framework poses two distinct questions: (1) What input information guides moral judgments? and (2) What psychological processes generate these judgments? Information Models address the first question, identifying critical information elements (including causality, intentionality, and mental states) that shape moral judgments. A subclass of Biased Information Models holds that perceptions of these information elements are themselves driven by prior moral judgments. Processing Models address the second question, and existing models have focused on the relative contribution of intuitive versus deliberative processes. This review organizes existing moral judgment models within this framework and critically evaluates them on empirical and theoretical grounds; it then outlines a general integrative model grounded in information processing, and concludes with conceptual and methodological suggestions for future research. The information-processing framework provides a useful theoretical lens through which to organize extant and future work in the rapidly growing field of moral judgment. PMID:26579022

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, Brian; Wood, Richard T.

1997-01-01

A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.

Modeling microbiological and chemical processes in municipal solid waste bioreactor, Part II: Application of numerical model BIOKEMOD-3P.

PubMed

Gawande, Nitin A; Reinhart, Debra R; Yeh, Gour-Tsyh

2010-02-01

Biodegradation process modeling of municipal solid waste (MSW) bioreactor landfills requires the knowledge of various process reactions and corresponding kinetic parameters. Mechanistic models available to date are able to simulate biodegradation processes with the help of pre-defined species and reactions. Some of these models consider the effect of critical parameters such as moisture content, pH, and temperature. Biomass concentration is a vital parameter for any biomass growth model and often not compared with field and laboratory results. A more complex biodegradation model includes a large number of chemical and microbiological species. Increasing the number of species and user defined process reactions in the simulation requires a robust numerical tool. A generalized microbiological and chemical model, BIOKEMOD-3P, was developed to simulate biodegradation processes in three-phases (Gawande et al. 2009). This paper presents the application of this model to simulate laboratory-scale MSW bioreactors under anaerobic conditions. BIOKEMOD-3P was able to closely simulate the experimental data. The results from this study may help in application of this model to full-scale landfill operation.

A Strategy for Autogeneration of Space Shuttle Ground Processing Simulation Models for Project Makespan Estimations

NASA Technical Reports Server (NTRS)

Madden, Michael G.; Wyrick, Roberta; O'Neill, Dale E.

2005-01-01

Space Shuttle Processing is a complicated and highly variable project. The planning and scheduling problem, categorized as a Resource Constrained - Stochastic Project Scheduling Problem (RC-SPSP), has a great deal of variability in the Orbiter Processing Facility (OPF) process flow from one flight to the next. Simulation Modeling is a useful tool in estimation of the makespan of the overall process. However, simulation requires a model to be developed, which itself is a labor and time consuming effort. With such a dynamic process, often the model would potentially be out of synchronization with the actual process, limiting the applicability of the simulation answers in solving the actual estimation problem. Integration of TEAMS model enabling software with our existing schedule program software is the basis of our solution. This paper explains the approach used to develop an auto-generated simulation model from planning and schedule efforts and available data.

Verification of ARMA identification for modelling temporal correlation of GPS observations using the toolbox ARMASA

NASA Astrophysics Data System (ADS)

Luo, Xiaoguang; Mayer, Michael; Heck, Bernhard

2010-05-01

One essential deficiency of the stochastic model used in many GNSS (Global Navigation Satellite Systems) software products consists in neglecting temporal correlation of GNSS observations. Analysing appropriately detrended time series of observation residuals resulting from GPS (Global Positioning System) data processing, the temporal correlation behaviour of GPS observations can be sufficiently described by means of so-called autoregressive moving average (ARMA) processes. Using the toolbox ARMASA which is available free of charge in MATLAB® Central (open exchange platform for the MATLAB® and SIMULINK® user community), a well-fitting time series model can be identified automatically in three steps. Firstly, AR, MA, and ARMA models are computed up to some user-specified maximum order. Subsequently, for each model type, the best-fitting model is selected using the combined (for AR processes) resp. generalised (for MA and ARMA processes) information criterion. The final model identification among the best-fitting AR, MA, and ARMA models is performed based on the minimum prediction error characterising the discrepancies between the given data and the fitted model. The ARMA coefficients are computed using Burg's maximum entropy algorithm (for AR processes), Durbin's first (for MA processes) and second (for ARMA processes) methods, respectively. This paper verifies the performance of the automated ARMA identification using the toolbox ARMASA. For this purpose, a representative data base is generated by means of ARMA simulation with respect to sample size, correlation level, and model complexity. The model error defined as a transform of the prediction error is used as measure for the deviation between the true and the estimated model. The results of the study show that the recognition rates of underlying true processes increase with increasing sample sizes and decrease with rising model complexity. Considering large sample sizes, the true underlying processes can be correctly recognised for nearly 80% of the analysed data sets. Additionally, the model errors of first-order AR resp. MA processes converge clearly more rapidly to the corresponding asymptotical values than those of high-order ARMA processes.

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

Business process modeling in healthcare.

PubMed

Ruiz, Francisco; Garcia, Felix; Calahorra, Luis; Llorente, César; Gonçalves, Luis; Daniel, Christel; Blobel, Bernd

2012-01-01

The importance of the process point of view is not restricted to a specific enterprise sector. In the field of health, as a result of the nature of the service offered, health institutions' processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this chapter the application of business process modelling - using the Business Process Modelling Notation (BPMN) standard is described. Main challenges of business process modelling in healthcare are the definition of healthcare processes, the multi-disciplinary nature of healthcare, the flexibility and variability of the activities involved in health care processes, the need of interoperability between multiple information systems, and the continuous updating of scientific knowledge in healthcare.

An Extension of SIC Predictions to the Wiener Coactive Model

PubMed Central

Houpt, Joseph W.; Townsend, James T.

2011-01-01

The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down-up-down form, with an early negative region that is smaller than the later positive region. In this note, we demonstrate that a coactive process based on the sum of two Wiener processes has the same SIC form. PMID:21822333

An Extension of SIC Predictions to the Wiener Coactive Model.

PubMed

Houpt, Joseph W; Townsend, James T

2011-06-01

The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down-up-down form, with an early negative region that is smaller than the later positive region. In this note, we demonstrate that a coactive process based on the sum of two Wiener processes has the same SIC form.

Bridging process-based and empirical approaches to modeling tree growth

Treesearch

Harry T. Valentine; Annikki Makela; Annikki Makela

2005-01-01

The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

further than 2 (Makridakis, et al, 1983, 359). 2-32 Autoregressive Integrated Moving Average ( ARIMA ) Model . Similar to the ARMA model except for...stationary process. ARIMA models are described as ARIMA (p,d,q), where p is the order of the autoregressive process, d is the degree of the...differential process, and q is the order of the moving average process. The ARMA (1,1) model shown above is equivalent to an ARIMA (1,0,1) model . An ARIMA

Capability Maturity Model (CMM) for Software Process Improvements

NASA Technical Reports Server (NTRS)

Ling, Robert Y.

2000-01-01

This slide presentation reviews the Avionic Systems Division's implementation of the Capability Maturity Model (CMM) for improvements in the software development process. The presentation reviews the process involved in implementing the model and the benefits of using CMM to improve the software development process.

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

NASA Technical Reports Server (NTRS)

Liou, Frank; Newkirk, Joseph; Fan, Zhiqiang; Sparks, Todd; Chen, Xueyang; Fletcher, Kenneth; Zhang, Jingwei; Zhang, Yunlu; Kumar, Kannan Suresh; Karnati, Sreekar

2015-01-01

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification.

Extending BPM Environments of Your Choice with Performance Related Decision Support

NASA Astrophysics Data System (ADS)

Fritzsche, Mathias; Picht, Michael; Gilani, Wasif; Spence, Ivor; Brown, John; Kilpatrick, Peter

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Enzymatic corn wet milling: engineering process and cost model

PubMed Central

Ramírez, Edna C; Johnston, David B; McAloon, Andrew J; Singh, Vijay

2009-01-01

Background Enzymatic corn wet milling (E-milling) is a process derived from conventional wet milling for the recovery and purification of starch and co-products using proteases to eliminate the need for sulfites and decrease the steeping time. In 2006, the total starch production in USA by conventional wet milling equaled 23 billion kilograms, including modified starches and starches used for sweeteners and ethanol production [1]. Process engineering and cost models for an E-milling process have been developed for a processing plant with a capacity of 2.54 million kg of corn per day (100,000 bu/day). These models are based on the previously published models for a traditional wet milling plant with the same capacity. The E-milling process includes grain cleaning, pretreatment, enzymatic treatment, germ separation and recovery, fiber separation and recovery, gluten separation and recovery and starch separation. Information for the development of the conventional models was obtained from a variety of technical sources including commercial wet milling companies, industry experts and equipment suppliers. Additional information for the present models was obtained from our own experience with the development of the E-milling process and trials in the laboratory and at the pilot plant scale. The models were developed using process and cost simulation software (SuperPro Designer®) and include processing information such as composition and flow rates of the various process streams, descriptions of the various unit operations and detailed breakdowns of the operating and capital cost of the facility. Results Based on the information from the model, we can estimate the cost of production per kilogram of starch using the input prices for corn, enzyme and other wet milling co-products. The work presented here describes the E-milling process and compares the process, the operation and costs with the conventional process. Conclusion The E-milling process was found to be cost competitive with the conventional process during periods of high corn feedstock costs since the enzymatic process enhances the yields of the products in a corn wet milling process. This model is available upon request from the authors for educational, research and non-commercial uses. PMID:19154623

Social network supported process recommender system.

PubMed

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

[Definition and stabilization of processes I. Management processes and support in a Urology Department].

PubMed

Pascual, Carlos; Luján, Marcos; Mora, José Ramón; Chiva, Vicente; Gamarra, Manuela

2015-01-01

The implantation of total quality management models in clinical departments can better adapt to the 2009 ISO 9004 model. An essential part of implantation of these models is the establishment of processes and their stabilization. There are four types of processes: key, management, support and operative (clinical). Management processes have four parts: process stabilization form, process procedures form, medical activities cost estimation form and, process flow chart. In this paper we will detail the creation of an essential process in a surgical department, such as the process of management of the surgery waiting list.

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Neural Network Modeling for Gallium Arsenide IC Fabrication Process and Device Characteristics.

NASA Astrophysics Data System (ADS)

Creech, Gregory Lee, I.

This dissertation presents research focused on the utilization of neurocomputing technology to achieve enhanced yield and effective yield prediction in integrated circuit (IC) manufacturing. Artificial neural networks are employed to model complex relationships between material and device characteristics at critical stages of the semiconductor fabrication process. Whole wafer testing was performed on the starting substrate material and during wafer processing at four critical steps: Ohmic or Post-Contact, Post-Recess, Post-Gate and Final, i.e., at completion of fabrication. Measurements taken and subsequently used in modeling include, among others, doping concentrations, layer thicknesses, planar geometries, layer-to-layer alignments, resistivities, device voltages, and currents. The neural network architecture used in this research is the multilayer perceptron neural network (MLPNN). The MLPNN is trained in the supervised mode using the generalized delta learning rule. It has one hidden layer and uses continuous perceptrons. The research focuses on a number of different aspects. First is the development of inter-process stage models. Intermediate process stage models are created in a progressive fashion. Measurements of material and process/device characteristics taken at a specific processing stage and any previous stages are used as input to the model of the next processing stage characteristics. As the wafer moves through the fabrication process, measurements taken at all previous processing stages are used as input to each subsequent process stage model. Secondly, the development of neural network models for the estimation of IC parametric yield is demonstrated. Measurements of material and/or device characteristics taken at earlier fabrication stages are used to develop models of the final DC parameters. These characteristics are computed with the developed models and compared to acceptance windows to estimate the parametric yield. A sensitivity analysis is performed on the models developed during this yield estimation effort. This is accomplished by analyzing the total disturbance of network outputs due to perturbed inputs. When an input characteristic bears no, or little, statistical or deterministic relationship to the output characteristics, it can be removed as an input. Finally, neural network models are developed in the inverse direction. Characteristics measured after the final processing step are used as the input to model critical in-process characteristics. The modeled characteristics are used for whole wafer mapping and its statistical characterization. It is shown that this characterization can be accomplished with minimal in-process testing. The concepts and methodologies used in the development of the neural network models are presented. The modeling results are provided and compared to the actual measured values of each characteristic. An in-depth discussion of these results and ideas for future research are presented.

First-principles modeling of laser-matter interaction and plasma dynamics in nanosecond pulsed laser shock processing

NASA Astrophysics Data System (ADS)

Zhang, Zhongyang; Nian, Qiong; Doumanidis, Charalabos C.; Liao, Yiliang

2018-02-01

Nanosecond pulsed laser shock processing (LSP) techniques, including laser shock peening, laser peen forming, and laser shock imprinting, have been employed for widespread industrial applications. In these processes, the main beneficial characteristic is the laser-induced shockwave with a high pressure (in the order of GPa), which leads to the plastic deformation with an ultrahigh strain rate (105-106/s) on the surface of target materials. Although LSP processes have been extensively studied by experiments, few efforts have been put on elucidating underlying process mechanisms through developing a physics-based process model. In particular, development of a first-principles model is critical for process optimization and novel process design. This work aims at introducing such a theoretical model for a fundamental understanding of process mechanisms in LSP. Emphasis is placed on the laser-matter interaction and plasma dynamics. This model is found to offer capabilities in predicting key parameters including electron and ion temperatures, plasma state variables (temperature, density, and pressure), and the propagation of the laser shockwave. The modeling results were validated by experimental data.

Cognitive Theory within the Framework of an Information Processing Model and Learning Hierarchy: Viable Alternative to the Bloom-Mager System.

ERIC Educational Resources Information Center

Stahl, Robert J.

This review of the current status of the human information processing model presents the Stahl Perceptual Information Processing and Operations Model (SPInPrOM) as a model of how thinking, memory, and the processing of information take place within the individual learner. A related system, the Domain of Cognition, is presented as an alternative to…

Probabilistic modeling of discourse-aware sentence processing.

PubMed

Dubey, Amit; Keller, Frank; Sturt, Patrick

2013-07-01

Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. Copyright © 2013 Cognitive Science Society, Inc.

Modeling process-structure-property relationships for additive manufacturing

NASA Astrophysics Data System (ADS)

Yan, Wentao; Lin, Stephen; Kafka, Orion L.; Yu, Cheng; Liu, Zeliang; Lian, Yanping; Wolff, Sarah; Cao, Jian; Wagner, Gregory J.; Liu, Wing Kam

2018-02-01

This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the processstructure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a highefficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Coal conversion systems design and process modeling. Volume 1: Application of MPPR and Aspen computer models

NASA Technical Reports Server (NTRS)

1981-01-01

The development of a coal gasification system design and mass and energy balance simulation program for the TVA and other similar facilities is described. The materials-process-product model (MPPM) and the advanced system for process engineering (ASPEN) computer program were selected from available steady state and dynamic models. The MPPM was selected to serve as the basis for development of system level design model structure because it provided the capability for process block material and energy balance and high-level systems sizing and costing. The ASPEN simulation serves as the basis for assessing detailed component models for the system design modeling program. The ASPEN components were analyzed to identify particular process blocks and data packages (physical properties) which could be extracted and used in the system design modeling program. While ASPEN physical properties calculation routines are capable of generating physical properties required for process simulation, not all required physical property data are available, and must be user-entered.

Correcting Inadequate Model Snow Process Descriptions Dramatically Improves Mountain Hydrology Simulations

NASA Astrophysics Data System (ADS)

Pomeroy, J. W.; Fang, X.

2014-12-01

The vast effort in hydrology devoted to parameter calibration as a means to improve model performance assumes that the models concerned are not fundamentally wrong. By focussing on finding optimal parameter sets and ascribing poor model performance to parameter or data uncertainty, these efforts may fail to consider the need to improve models with more intelligent descriptions of hydrological processes. To test this hypothesis, a flexible physically based hydrological model including a full suite of snow hydrology processes as well as warm season, hillslope and groundwater hydrology was applied to Marmot Creek Research Basin, Canadian Rocky Mountains where excellent driving meteorology and basin biophysical descriptions exist. Model parameters were set from values found in the basin or from similar environments; no parameters were calibrated. The model was tested against snow surveys and streamflow observations. The model used algorithms that describe snow redistribution, sublimation and forest canopy effects on snowmelt and evaporative processes that are rarely implemented in hydrological models. To investigate the contribution of these processes to model predictive capability, the model was "falsified" by deleting parameterisations for forest canopy snow mass and energy, blowing snow, intercepted rain evaporation, and sublimation. Model falsification by ignoring forest canopy processes contributed to a large increase in SWE errors for forested portions of the research basin with RMSE increasing from 19 to 55 mm and mean bias (MB) increasing from 0.004 to 0.62. In the alpine tundra portion, removing blowing processes resulted in an increase in model SWE MB from 0.04 to 2.55 on north-facing slopes and -0.006 to -0.48 on south-facing slopes. Eliminating these algorithms degraded streamflow prediction with the Nash Sutcliffe efficiency dropping from 0.58 to 0.22 and MB increasing from 0.01 to 0.09. These results show dramatic model improvements by including snow redistribution and melt processes associated with wind transport and forest canopies. As most hydrological models do not currently include these processes, it is suggested that modellers first improve the realism of model structures before trying to optimise what are inherently inadequate simulations of hydrology.

Conceptual Model-Based Systems Biology: Mapping Knowledge and Discovering Gaps in the mRNA Transcription Cycle

PubMed Central

Somekh, Judith; Choder, Mordechai; Dori, Dov

2012-01-01

We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure—the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model. PMID:23308089

Pursuing the method of multiple working hypotheses to understand differences in process-based snow models

NASA Astrophysics Data System (ADS)

Clark, Martyn; Essery, Richard

2017-04-01

When faced with the complex and interdisciplinary challenge of building process-based land models, different modelers make different decisions at different points in the model development process. These modeling decisions are generally based on several considerations, including fidelity (e.g., what approaches faithfully simulate observed processes), complexity (e.g., which processes should be represented explicitly), practicality (e.g., what is the computational cost of the model simulations; are there sufficient resources to implement the desired modeling concepts), and data availability (e.g., is there sufficient data to force and evaluate models). Consequently the research community, comprising modelers of diverse background, experience, and modeling philosophy, has amassed a wide range of models, which differ in almost every aspect of their conceptualization and implementation. Model comparison studies have been undertaken to explore model differences, but have not been able to meaningfully attribute inter-model differences in predictive ability to individual model components because there are often too many structural and implementation differences among the different models considered. As a consequence, model comparison studies to date have provided limited insight into the causes of differences in model behavior, and model development has often relied on the inspiration and experience of individual modelers rather than on a systematic analysis of model shortcomings. This presentation will summarize the use of "multiple-hypothesis" modeling frameworks to understand differences in process-based snow models. Multiple-hypothesis frameworks define a master modeling template, and include a a wide variety of process parameterizations and spatial configurations that are used in existing models. Such frameworks provide the capability to decompose complex models into the individual decisions that are made as part of model development, and evaluate each decision in isolation. It is hence possible to attribute differences in system-scale model predictions to individual modeling decisions, providing scope to mimic the behavior of existing models, understand why models differ, characterize model uncertainty, and identify productive pathways to model improvement. Results will be presented applying multiple hypothesis frameworks to snow model comparison projects, including PILPS, SnowMIP, and the upcoming ESM-SnowMIP project.

The Relation between Students' Epistemological Understanding of Computer Models and Their Cognitive Processing on a Modelling Task

ERIC Educational Resources Information Center

Sins, Patrick H. M.; Savelsbergh, Elwin R.; van Joolingen, Wouter R.; van Hout-Wolters, Bernadette H. A. M.

2009-01-01

While many researchers in science education have argued that students' epistemological understanding of models and of modelling processes would influence their cognitive processing on a modelling task, there has been little direct evidence for such an effect. Therefore, this study aimed to investigate the relation between students' epistemological…

Modeling of Electrochemical Process for the Treatment of Wastewater Containing Organic Pollutants

NASA Astrophysics Data System (ADS)

Rodrigo, Manuel A.; Cañizares, Pablo; Lobato, Justo; Sáez, Cristina

Electrocoagulation and electrooxidation are promising electrochemical technologies that can be used to remove organic pollutants contained in wastewaters. To make these technologies competitive with the conventional technologies that are in use today, a better understanding of the processes involved must be achieved. In this context, the development of mathematical models that are consistent with the processes occurring in a physical system is a relevant advance, because such models can help to understand what is happening in the treatment process. In turn, a more detailed knowledge of the physical system can be obtained, and tools for a proper design of the processes, or for the analysis of operating problems, are attained. The modeling of these technologies can be carried out using single-variable or multivariable models. Likewise, the position dependence of the model species can be described with different approaches. In this work, a review of the basics of the modeling of these processes and a description of several representative models for electrochemical oxidation and coagulation are carried out. Regarding electrooxidation, two models are described: one which summarizes the pollution of a wastewater in only one model species and that considers a macroscopic approach to formulate the mass balances and other that considers more detailed profile of concentration to describe the time course of pollutants and intermediates through a mixed maximum gradient/macroscopic approach. On the topic of electrochemical coagulation, two different approaches are also described in this work: one that considers the hydrodynamic conditions as the main factor responsible for the electrochemical coagulation processes and the other that considers the chemical interaction of the reagents and the pollutants as the more significant processes in the description of the electrochemical coagulation of organic compounds. In addition, in this work it is also described a multivariable model for the electrodissolution of anodes (first stage in electrocoagulation processes). This later model use a mixed macroscopic/maximum gradient approach to describe the chemical and electrochemical processes and it also assumes that the rates of all processes are very high, and that they can be successfully modeled using pseudoequilibrium approaches.

Community of Interest Engagement Process Plan

DTIC Science & Technology

2012-02-09

and input from Subject Matter Experts (SMEs), as shown in the far left of Figure 2. The team may prepare a Business Process Model Notation ( BPMN ) 22...22 Business Process Modeling Notation ( BPMN ) is a method of illustrating business processes in the form of a...Community of Interest Engagement Plan Joint Planning and Development Office 21 10. Acronyms BPMN Business Process Modeling Notation COI

Animated-simulation modeling facilitates clinical-process costing.

PubMed

Zelman, W N; Glick, N D; Blackmore, C C

2001-09-01

Traditionally, the finance department has assumed responsibility for assessing process costs in healthcare organizations. To enhance process-improvement efforts, however, many healthcare providers need to include clinical staff in process cost analysis. Although clinical staff often use electronic spreadsheets to model the cost of specific processes, PC-based animated-simulation tools offer two major advantages over spreadsheets: they allow clinicians to interact more easily with the costing model so that it more closely represents the process being modeled, and they represent cost output as a cost range rather than as a single cost estimate, thereby providing more useful information for decision making.

Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

NASA Astrophysics Data System (ADS)

Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

2017-12-01

We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

A combined disease management and process modeling approach for assessing and improving care processes: a fall management case-study.

PubMed

Askari, Marjan; Westerhof, Richard; Eslami, Saied; Medlock, Stephanie; de Rooij, Sophia E; Abu-Hanna, Ameen

2013-10-01

To propose a combined disease management and process modeling approach for evaluating and improving care processes, and demonstrate its usability and usefulness in a real-world fall management case study. We identified essential disease management related concepts and mapped them into explicit questions meant to expose areas for improvement in the respective care processes. We applied the disease management oriented questions to a process model of a comprehensive real world fall prevention and treatment program covering primary and secondary care. We relied on interviews and observations to complete the process models, which were captured in UML activity diagrams. A preliminary evaluation of the usability of our approach by gauging the experience of the modeler and an external validator was conducted, and the usefulness of the method was evaluated by gathering feedback from stakeholders at an invitational conference of 75 attendees. The process model of the fall management program was organized around the clinical tasks of case finding, risk profiling, decision making, coordination and interventions. Applying the disease management questions to the process models exposed weaknesses in the process including: absence of program ownership, under-detection of falls in primary care, and lack of efficient communication among stakeholders due to missing awareness about other stakeholders' workflow. The modelers experienced the approach as usable and the attendees of the invitational conference found the analysis results to be valid. The proposed disease management view of process modeling was usable and useful for systematically identifying areas of improvement in a fall management program. Although specifically applied to fall management, we believe our case study is characteristic of various disease management settings, suggesting the wider applicability of the approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Modeling and analysis of power processing systems: Feasibility investigation and formulation of a methodology

NASA Technical Reports Server (NTRS)

Biess, J. J.; Yu, Y.; Middlebrook, R. D.; Schoenfeld, A. D.

1974-01-01

A review is given of future power processing systems planned for the next 20 years, and the state-of-the-art of power processing design modeling and analysis techniques used to optimize power processing systems. A methodology of modeling and analysis of power processing equipment and systems has been formulated to fulfill future tradeoff studies and optimization requirements. Computer techniques were applied to simulate power processor performance and to optimize the design of power processing equipment. A program plan to systematically develop and apply the tools for power processing systems modeling and analysis is presented so that meaningful results can be obtained each year to aid the power processing system engineer and power processing equipment circuit designers in their conceptual and detail design and analysis tasks.

Process models as tools in forestry research and management

Treesearch

Kurt Johnsen; Lisa Samuelson; Robert Teskey; Steve McNulty; Tom Fox

2001-01-01

Forest process models are mathematical representations of biological systems that incorporate our understanding of physiological and ecological mechanisms into predictive algorithms. These models were originally designed and used for research purposes, but are being developed for use in practical forest management. Process models designed for research...

Validating archetypes for the Multiple Sclerosis Functional Composite.

PubMed

Braun, Michael; Brandt, Alexander Ulrich; Schulz, Stefan; Boeker, Martin

2014-08-03

Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions. A standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process. Four new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically. The quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions.This case study provides evidence that both community- and tool-enabled review processes, structured in the Clinical Knowledge Manager, ensure archetype quality. It offers a pragmatic but feasible way to reduce variation in the representation of clinical information models towards a more unified and interoperable model.

Validating archetypes for the Multiple Sclerosis Functional Composite

PubMed Central

2014-01-01

Background Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions. Methods A standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process. Results Four new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically. Conclusions The quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions. This case study provides evidence that both community- and tool-enabled review processes, structured in the Clinical Knowledge Manager, ensure archetype quality. It offers a pragmatic but feasible way to reduce variation in the representation of clinical information models towards a more unified and interoperable model. PMID:25087081

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

NASA Technical Reports Server (NTRS)

Allada, Rama Kumar; Lange, Kevin; Anderson, Molly

2011-01-01

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA) that were developed using the Aspen Custom Modeler and Aspen Plus process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

MIMO model of an interacting series process for Robust MPC via System Identification.

PubMed

Wibowo, Tri Chandra S; Saad, Nordin

2010-07-01

This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net

NASA Astrophysics Data System (ADS)

Ren, Yujuan; Bao, Hong

2016-11-01

In order to achieve the goals of energy saving and emission reduction of iron and steel enterprises, an increasing number of modeling and simulation technologies are used to research and analyse metallurgical production process. In this paper, the basic principle of Hybrid Petri net is used to model and analyse the Metallurgical Process. Firstly, the definition of Hybrid Petri Net System of Metallurgical Process (MPHPNS) and its modeling theory are proposed. Secondly, the model of MPHPNS based on material flow is constructed. The dynamic flow of materials and the real-time change of each technological state in metallurgical process are simulated vividly by using this model. The simulation process can implement interaction between the continuous event dynamic system and the discrete event dynamic system at the same level, and play a positive role in the production decision.

Extracting business vocabularies from business process models: SBVR and BPMN standards-based approach

NASA Astrophysics Data System (ADS)

Skersys, Tomas; Butleris, Rimantas; Kapocius, Kestutis

2013-10-01

Approaches for the analysis and specification of business vocabularies and rules are very relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, basic aspects of the approach for business vocabularies' semi-automated extraction from business process models are presented. The approach is based on novel business modeling-level OMG standards "Business Process Model and Notation" (BPMN) and "Semantics for Business Vocabularies and Business Rules" (SBVR), thus contributing to OMG's vision about Model-Driven Architecture (MDA) and to model-driven development in general.

Process dissociation and mixture signal detection theory.

PubMed

DeCarlo, Lawrence T

2008-11-01

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely analyzed study. The results suggest that a process other than recollection may be involved in the process dissociation procedure.

Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities.

PubMed

Fenu, A; Guglielmi, G; Jimenez, J; Spèrandio, M; Saroj, D; Lesjean, B; Brepols, C; Thoeye, C; Nopens, I

2010-08-01

Membrane bioreactors (MBRs) have been increasingly employed for municipal and industrial wastewater treatment in the last decade. The efforts for modelling of such wastewater treatment systems have always targeted either the biological processes (treatment quality target) as well as the various aspects of engineering (cost effective design and operation). The development of Activated Sludge Models (ASM) was an important evolution in the modelling of Conventional Activated Sludge (CAS) processes and their use is now very well established. However, although they were initially developed to describe CAS processes, they have simply been transferred and applied to MBR processes. Recent studies on MBR biological processes have reported several crucial specificities: medium to very high sludge retention times, high mixed liquor concentration, accumulation of soluble microbial products (SMP) rejected by the membrane filtration step, and high aeration rates for scouring purposes. These aspects raise the question as to what extent the ASM framework is applicable to MBR processes. Several studies highlighting some of the aforementioned issues are scattered through the literature. Hence, through a concise and structured overview of the past developments and current state-of-the-art in biological modelling of MBR, this review explores ASM-based modelling applied to MBR processes. The work aims to synthesize previous studies and differentiates between unmodified and modified applications of ASM to MBR. Particular emphasis is placed on influent fractionation, biokinetics, and soluble microbial products (SMPs)/exo-polymeric substances (EPS) modelling, and suggestions are put forward as to good modelling practice with regard to MBR modelling both for end-users and academia. A last section highlights shortcomings and future needs for improved biological modelling of MBR processes. (c) 2010 Elsevier Ltd. All rights reserved.

A generic biogeochemical module for earth system models

NASA Astrophysics Data System (ADS)

Fang, Y.; Huang, M.; Liu, C.; Li, H.-Y.; Leung, L. R.

2013-06-01

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from the atmosphere, influencing global climate changes. Integration of these processes into earth system models (e.g. community land models - CLM), however, currently faces three major challenges: (1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, (2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and (3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not impossible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into the CLM model. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems.

Software Framework for Advanced Power Plant Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

John Widmann; Sorin Munteanu; Aseem Jain

2010-08-01

This report summarizes the work accomplished during the Phase II development effort of the Advanced Process Engineering Co-Simulator (APECS). The objective of the project is to develop the tools to efficiently combine high-fidelity computational fluid dynamics (CFD) models with process modeling software. During the course of the project, a robust integration controller was developed that can be used in any CAPE-OPEN compliant process modeling environment. The controller mediates the exchange of information between the process modeling software and the CFD software. Several approaches to reducing the time disparity between CFD simulations and process modeling have been investigated and implemented. Thesemore » include enabling the CFD models to be run on a remote cluster and enabling multiple CFD models to be run simultaneously. Furthermore, computationally fast reduced-order models (ROMs) have been developed that can be 'trained' using the results from CFD simulations and then used directly within flowsheets. Unit operation models (both CFD and ROMs) can be uploaded to a model database and shared between multiple users.« less

Model-Based Reasoning in Upper-division Lab Courses

NASA Astrophysics Data System (ADS)

Lewandowski, Heather

2015-05-01

Modeling, which includes developing, testing, and refining models, is a central activity in physics. Well-known examples from AMO physics include everything from the Bohr model of the hydrogen atom to the Bose-Hubbard model of interacting bosons in a lattice. Modeling, while typically considered a theoretical activity, is most fully represented in the laboratory where measurements of real phenomena intersect with theoretical models, leading to refinement of models and experimental apparatus. However, experimental physicists use models in complex ways and the process is often not made explicit in physics laboratory courses. We have developed a framework to describe the modeling process in physics laboratory activities. The framework attempts to abstract and simplify the complex modeling process undertaken by expert experimentalists. The framework can be applied to understand typical processes such the modeling of the measurement tools, modeling ``black boxes,'' and signal processing. We demonstrate that the framework captures several important features of model-based reasoning in a way that can reveal common student difficulties in the lab and guide the development of curricula that emphasize modeling in the laboratory. We also use the framework to examine troubleshooting in the lab and guide students to effective methods and strategies.

Emissions model of waste treatment operations at the Idaho Chemical Processing Plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schindler, R.E.

1995-03-01

An integrated model of the waste treatment systems at the Idaho Chemical Processing Plant (ICPP) was developed using a commercially-available process simulation software (ASPEN Plus) to calculate atmospheric emissions of hazardous chemicals for use in an application for an environmental permit to operate (PTO). The processes covered by the model are the Process Equipment Waste evaporator, High Level Liquid Waste evaporator, New Waste Calcining Facility and Liquid Effluent Treatment and Disposal facility. The processes are described along with the model and its assumptions. The model calculates emissions of NO{sub x}, CO, volatile acids, hazardous metals, and organic chemicals. Some calculatedmore » relative emissions are summarized and insights on building simulations are discussed.« less

Discretized Streams: A Fault-Tolerant Model for Scalable Stream Processing

DTIC Science & Technology

2012-12-14

Discretized Streams: A Fault-Tolerant Model for Scalable Stream Processing Matei Zaharia Tathagata Das Haoyuan Li Timothy Hunter Scott Shenker Ion...SUBTITLE Discretized Streams: A Fault-Tolerant Model for Scalable Stream Processing 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...time. However, current programming models for distributed stream processing are relatively low-level often leaving the user to worry about consistency of

Simulation Modeling of Software Development Processes

NASA Technical Reports Server (NTRS)

Calavaro, G. F.; Basili, V. R.; Iazeolla, G.

1996-01-01

A simulation modeling approach is proposed for the prediction of software process productivity indices, such as cost and time-to-market, and the sensitivity analysis of such indices to changes in the organization parameters and user requirements. The approach uses a timed Petri Net and Object Oriented top-down model specification. Results demonstrate the model representativeness, and its usefulness in verifying process conformance to expectations, and in performing continuous process improvement and optimization.

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

PubMed

von Stosch, Moritz; Hamelink, Jan-Martijn; Oliveira, Rui

2016-05-01

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

Probabilistic modeling of the fate of Listeria monocytogenes in diced bacon during the manufacturing process.

PubMed

Billoir, Elise; Denis, Jean-Baptiste; Cammeau, Natalie; Cornu, Marie; Zuliani, Veronique

2011-02-01

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, we built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled in the appropriate units (breasts, dice, and then packaging units through the successive steps in the process). To calibrate the model, parameter values were estimated from industrial data, from the literature, and based on expert opinion. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events. The results are reported as contamination distributions and as the probability that the product will be acceptable with regards to the European regulatory safety criterion. Our results are consistent with data provided by industrial partners and highlight that tumbling is a key step for the distribution of the contamination at the end of the process. Process chain models could provide an important added value for risk assessment models that basically consider only the outputs of the process in their risk mitigation strategies. Moreover, a model calibrated to correspond to a specific plant could be used to optimize surveillance. © 2010 Society for Risk Analysis.

A Point-process Response Model for Spike Trains from Single Neurons in Neural Circuits under Optogenetic Stimulation

PubMed Central

Luo, X.; Gee, S.; Sohal, V.; Small, D.

2015-01-01

Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high frequency point process (neuronal spikes) while the input is another high frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, Point-process Responses for Optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the- curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. PMID:26411923

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, L.; Britt, J.; Birkmire, R.

ITN Energy Systems, Inc., and Global Solar Energy, Inc., assisted by NREL's PV Manufacturing R&D program, have continued to advance CIGS production technology by developing trajectory-oriented predictive/control models, fault-tolerance control, control platform development, in-situ sensors, and process improvements. Modeling activities included developing physics-based and empirical models for CIGS and sputter-deposition processing, implementing model-based control, and applying predictive models to the construction of new evaporation sources and for control. Model-based control is enabled by implementing reduced or empirical models into a control platform. Reliability improvement activities include implementing preventive maintenance schedules; detecting failed sensors/equipment and reconfiguring to tinue processing; and systematicmore » development of fault prevention and reconfiguration strategies for the full range of CIGS PV production deposition processes. In-situ sensor development activities have resulted in improved control and indicated the potential for enhanced process status monitoring and control of the deposition processes. Substantial process improvements have been made, including significant improvement in CIGS uniformity, thickness control, efficiency, yield, and throughput. In large measure, these gains have been driven by process optimization, which in turn have been enabled by control and reliability improvements due to this PV Manufacturing R&D program.« less

The Gravitational Process Path (GPP) model (v1.0) - a GIS-based simulation framework for gravitational processes

NASA Astrophysics Data System (ADS)

Wichmann, Volker

2017-09-01

The Gravitational Process Path (GPP) model can be used to simulate the process path and run-out area of gravitational processes based on a digital terrain model (DTM). The conceptual model combines several components (process path, run-out length, sink filling and material deposition) to simulate the movement of a mass point from an initiation site to the deposition area. For each component several modeling approaches are provided, which makes the tool configurable for different processes such as rockfall, debris flows or snow avalanches. The tool can be applied to regional-scale studies such as natural hazard susceptibility mapping but also contains components for scenario-based modeling of single events. Both the modeling approaches and precursor implementations of the tool have proven their applicability in numerous studies, also including geomorphological research questions such as the delineation of sediment cascades or the study of process connectivity. This is the first open-source implementation, completely re-written, extended and improved in many ways. The tool has been committed to the main repository of the System for Automated Geoscientific Analyses (SAGA) and thus will be available with every SAGA release.

Evolutionary inference via the Poisson Indel Process.

PubMed

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Evolutionary inference via the Poisson Indel Process

PubMed Central

Bouchard-Côté, Alexandre; Jordan, Michael I.

2013-01-01

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114–124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments. PMID:23275296

Comparing single- and dual-process models of memory development.

PubMed

Hayes, Brett K; Dunn, John C; Joubert, Amy; Taylor, Robert

2017-11-01

This experiment examined single-process and dual-process accounts of the development of visual recognition memory. The participants, 6-7-year-olds, 9-10-year-olds and adults, were presented with a list of pictures which they encoded under shallow or deep conditions. They then made recognition and confidence judgments about a list containing old and new items. We replicated the main trends reported by Ghetti and Angelini () in that recognition hit rates increased from 6 to 9 years of age, with larger age changes following deep than shallow encoding. Formal versions of the dual-process high threshold signal detection model and several single-process models (equal variance signal detection, unequal variance signal detection, mixture signal detection) were fit to the developmental data. The unequal variance and mixture signal detection models gave a better account of the data than either of the other models. A state-trace analysis found evidence for only one underlying memory process across the age range tested. These results suggest that single-process memory models based on memory strength are a viable alternative to dual-process models for explaining memory development. © 2016 John Wiley & Sons Ltd.

Statistical post-processing of seasonal multi-model forecasts: Why is it so hard to beat the multi-model mean?

NASA Astrophysics Data System (ADS)

Siegert, Stefan

2017-04-01

Initialised climate forecasts on seasonal time scales, run several months or even years ahead, are now an integral part of the battery of products offered by climate services world-wide. The availability of seasonal climate forecasts from various modeling centres gives rise to multi-model ensemble forecasts. Post-processing such seasonal-to-decadal multi-model forecasts is challenging 1) because the cross-correlation structure between multiple models and observations can be complicated, 2) because the amount of training data to fit the post-processing parameters is very limited, and 3) because the forecast skill of numerical models tends to be low on seasonal time scales. In this talk I will review new statistical post-processing frameworks for multi-model ensembles. I will focus particularly on Bayesian hierarchical modelling approaches, which are flexible enough to capture commonly made assumptions about collective and model-specific biases of multi-model ensembles. Despite the advances in statistical methodology, it turns out to be very difficult to out-perform the simplest post-processing method, which just recalibrates the multi-model ensemble mean by linear regression. I will discuss reasons for this, which are closely linked to the specific characteristics of seasonal multi-model forecasts. I explore possible directions for improvements, for example using informative priors on the post-processing parameters, and jointly modelling forecasts and observations.

Social Network Supported Process Recommender System

PubMed Central

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced. PMID:24672309

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Hierarchical, parallel computing strategies using component object model for process modelling responses of forest plantations to interacting multiple stresses

Treesearch

J. G. Isebrands; G. E. Host; K. Lenz; G. Wu; H. W. Stech

2000-01-01

Process models are powerful research tools for assessing the effects of multiple environmental stresses on forest plantations. These models are driven by interacting environmental variables and often include genetic factors necessary for assessing forest plantation growth over a range of different site, climate, and silvicultural conditions. However, process models are...

A methodological framework to support the initiation, design and institutionalization of participatory modeling processes in water resources management

NASA Astrophysics Data System (ADS)

Halbe, Johannes; Pahl-Wostl, Claudia; Adamowski, Jan

2018-01-01

Multiple barriers constrain the widespread application of participatory methods in water management, including the more technical focus of most water agencies, additional cost and time requirements for stakeholder involvement, as well as institutional structures that impede collaborative management. This paper presents a stepwise methodological framework that addresses the challenges of context-sensitive initiation, design and institutionalization of participatory modeling processes. The methodological framework consists of five successive stages: (1) problem framing and stakeholder analysis, (2) process design, (3) individual modeling, (4) group model building, and (5) institutionalized participatory modeling. The Management and Transition Framework is used for problem diagnosis (Stage One), context-sensitive process design (Stage Two) and analysis of requirements for the institutionalization of participatory water management (Stage Five). Conceptual modeling is used to initiate participatory modeling processes (Stage Three) and ensure a high compatibility with quantitative modeling approaches (Stage Four). This paper describes the proposed participatory model building (PMB) framework and provides a case study of its application in Québec, Canada. The results of the Québec study demonstrate the applicability of the PMB framework for initiating and designing participatory model building processes and analyzing barriers towards institutionalization.

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

NASA Technical Reports Server (NTRS)

Allada, Rama Kumar; Lange, Kevin E.; Anderson, Molly S.

2012-01-01

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA). These dynamic models were developed using the Aspen Custom Modeler (Registered TradeMark) and Aspen Plus(Registered TradeMark) process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

What Is Reading? A Comparison of Three Models of the Reading Process.

ERIC Educational Resources Information Center

Angus, Elisabeth

Theoretical models of the reading process have been proposed by F. Smith, E. J. Gibson and H. Levin, and D. LaBerge and S. J. Samuels. These models were examined using the following questions: How are features of print processed by the brain? How important are prior knowledge and expectations to the process (top-down or bottom-up processing)? Is…

A Multi-scale Modeling System with Unified Physics to Study Precipitation Processes

NASA Astrophysics Data System (ADS)

Tao, W. K.

2017-12-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), and (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF). The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitation, processes and their sensitivity on model resolution and microphysics schemes will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the recent developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitating systems and hurricanes/typhoons will be presented. The high-resolution spatial and temporal visualization will be utilized to show the evolution of precipitation processes. Also how to use of the multi-satellite simulator tqimproy precipitation processes will be discussed.

A modified dynamical model of drying process of polymer blend solution coated on a flat substrate

NASA Astrophysics Data System (ADS)

Kagami, Hiroyuki

2008-05-01

We have proposed and modified a model of drying process of polymer solution coated on a flat substrate for flat polymer film fabrication. And for example numerical simulation of the model reproduces a typical thickness profile of the polymer film formed after drying. Then we have clarified dependence of distribution of polymer molecules on a flat substrate on a various parameters based on analysis of numerical simulations. Then we drove nonlinear equations of drying process from the dynamical model and the fruits were reported. The subject of above studies was limited to solution having one kind of solute though the model could essentially deal with solution having some kinds of solutes. But nowadays discussion of drying process of a solution having some kinds of solutes is needed because drying process of solution having some kinds of solutes appears in many industrial scenes. Polymer blend solution is one instance. And typical resist consists of a few kinds of polymers. Then we introduced a dynamical model of drying process of polymer blend solution coated on a flat substrate and results of numerical simulations of the dynamical model. But above model was the simplest one. In this study, we modify above dynamical model of drying process of polymer blend solution adding effects that some parameters change with time as functions of some variables to it. Then we consider essence of drying process of polymer blend solution through comparison between results of numerical simulations of the modified model and those of the former model.

Process-based modeling of silicate mineral weathering responses to increasing atmospheric CO2 and climate change

NASA Astrophysics Data System (ADS)

Banwart, Steven A.; Berg, Astrid; Beerling, David J.

2009-12-01

A mathematical model describes silicate mineral weathering processes in modern soils located in the boreal coniferous region of northern Europe. The process model results demonstrate a stabilizing biological feedback mechanism between atmospheric CO2 levels and silicate weathering rates as is generally postulated for atmospheric evolution. The process model feedback response agrees within a factor of 2 of that calculated by a weathering feedback function of the type generally employed in global geochemical carbon cycle models of the Earth's Phanerozoic CO2 history. Sensitivity analysis of parameter values in the process model provides insight into the key mechanisms that influence the strength of the biological feedback to weathering. First, the process model accounts for the alkalinity released by weathering, whereby its acceleration stabilizes pH at values that are higher than expected. Although the process model yields faster weathering with increasing temperature, because of activation energy effects on mineral dissolution kinetics at warmer temperature, the mineral dissolution rate laws utilized in the process model also result in lower dissolution rates at higher pH values. Hence, as dissolution rates increase under warmer conditions, more alkalinity is released by the weathering reaction, helping maintain higher pH values thus stabilizing the weathering rate. Second, the process model yields a relatively low sensitivity of soil pH to increasing plant productivity. This is due to more rapid decomposition of dissolved organic carbon (DOC) under warmer conditions. Because DOC fluxes strongly influence the soil water proton balance and pH, this increased decomposition rate dampens the feedback between productivity and weathering. The process model is most sensitive to parameters reflecting soil structure; depth, porosity, and water content. This suggests that the role of biota to influence these characteristics of the weathering profile is as important, if not more important, than the role of biota to influence mineral dissolution rates through changes in soil water chemistry. This process-modeling approach to quantify the biological weathering feedback to atmospheric CO2 demonstrates the potential for a far more mechanistic description of weathering feedback in simulations of the global geochemical carbon cycle.

Application of agent-based system for bioprocess description and process improvement.

PubMed

Gao, Ying; Kipling, Katie; Glassey, Jarka; Willis, Mark; Montague, Gary; Zhou, Yuhong; Titchener-Hooker, Nigel J

2010-01-01

Modeling plays an important role in bioprocess development for design and scale-up. Predictive models can also be used in biopharmaceutical manufacturing to assist decision-making either to maintain process consistency or to identify optimal operating conditions. To predict the whole bioprocess performance, the strong interactions present in a processing sequence must be adequately modeled. Traditionally, bioprocess modeling considers process units separately, which makes it difficult to capture the interactions between units. In this work, a systematic framework is developed to analyze the bioprocesses based on a whole process understanding and considering the interactions between process operations. An agent-based approach is adopted to provide a flexible infrastructure for the necessary integration of process models. This enables the prediction of overall process behavior, which can then be applied during process development or once manufacturing has commenced, in both cases leading to the capacity for fast evaluation of process improvement options. The multi-agent system comprises a process knowledge base, process models, and a group of functional agents. In this system, agent components co-operate with each other in performing their tasks. These include the description of the whole process behavior, evaluating process operating conditions, monitoring of the operating processes, predicting critical process performance, and providing guidance to decision-making when coping with process deviations. During process development, the system can be used to evaluate the design space for process operation. During manufacture, the system can be applied to identify abnormal process operation events and then to provide suggestions as to how best to cope with the deviations. In all cases, the function of the system is to ensure an efficient manufacturing process. The implementation of the agent-based approach is illustrated via selected application scenarios, which demonstrate how such a framework may enable the better integration of process operations by providing a plant-wide process description to facilitate process improvement. Copyright 2009 American Institute of Chemical Engineers

Black-Scholes model under subordination

NASA Astrophysics Data System (ADS)

Stanislavsky, A. A.

2003-02-01

In this paper, we consider a new mathematical extension of the Black-Scholes (BS) model in which the stochastic time and stock share price evolution is described by two independent random processes. The parent process is Brownian, and the directing process is inverse to the totally skewed, strictly α-stable process. The subordinated process represents the Brownian motion indexed by an independent, continuous and increasing process. This allows us to introduce the long-term memory effects in the classical BS model.

Development and evaluation of spatial point process models for epidermal nerve fibers.

PubMed

Olsbo, Viktor; Myllymäki, Mari; Waller, Lance A; Särkkä, Aila

2013-06-01

We propose two spatial point process models for the spatial structure of epidermal nerve fibers (ENFs) across human skin. The models derive from two point processes, Φb and Φe, describing the locations of the base and end points of the fibers. Each point of Φe (the end point process) is connected to a unique point in Φb (the base point process). In the first model, both Φe and Φb are Poisson processes, yielding a null model of uniform coverage of the skin by end points and general baseline results and reference values for moments of key physiologic indicators. The second model provides a mechanistic model to generate end points for each base, and we model the branching structure more directly by defining Φe as a cluster process conditioned on the realization of Φb as its parent points. In both cases, we derive distributional properties for observable quantities of direct interest to neurologists such as the number of fibers per base, and the direction and range of fibers on the skin. We contrast both models by fitting them to data from skin blister biopsy images of ENFs and provide inference regarding physiological properties of ENFs. Copyright © 2013 Elsevier Inc. All rights reserved.

Toward a Model for Picture and Word Processing.

ERIC Educational Resources Information Center

Snodgrass, Joan Gay

A model was developed to account for similarities and differences between picture and word processing in a variety of semantic and episodic memory tasks. The model contains three levels of processing: low-level processing of the physical characteristics of externally presented pictures and words; an intermediate level where the low-level processor…

Process Dissociation and Mixture Signal Detection Theory

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2008-01-01

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely…

Toward a General Research Process for Using Dubin's Theory Building Model

ERIC Educational Resources Information Center

Holton, Elwood F.; Lowe, Janis S.

2007-01-01

Dubin developed a widely used methodology for theory building, which describes the components of the theory building process. Unfortunately, he does not define a research process for implementing his theory building model. This article proposes a seven-step general research process for implementing Dubin's theory building model. An example of a…

Modeling Business Processes of the Social Insurance Fund in Information System Runa WFE

NASA Astrophysics Data System (ADS)

Kataev, M. Yu; Bulysheva, L. A.; Xu, Li D.; Loseva, N. V.

2016-08-01

Introduction - Business processes are gradually becoming a tool that allows you at a new level to put employees or to make more efficient document management system. In these directions the main work, and presents the largest possible number of publications. However, business processes are still poorly implemented in public institutions, where it is very difficult to formalize the main existing processes. Us attempts to build a system of business processes for such state agencies as the Russian social insurance Fund (SIF), where virtually all of the processes, when different inputs have the same output: public service. The parameters of the state services (as a rule, time limits) are set by state laws and regulations. The article provides a brief overview of the FSS, the formulation of requirements to business processes, the justification of the choice of software for modeling business processes and create models of work in the system Runa WFE and optimization models one of the main business processes of the FSS. The result of the work of Runa WFE is an optimized model of the business process of FSS.

Managing the travel model process : small and medium-sized MPOs. Instructor guide.

DOT National Transportation Integrated Search

2013-09-01

The learning objectives of this course were to: explain fundamental travel model concepts; describe the model development process; identify key inputs and describe the quality control process; and identify and manage resources.

Managing the travel model process : small and medium-sized MPOs. Participant handbook.

DOT National Transportation Integrated Search

2013-09-01

The learning objectives of this course were to: explain fundamental travel model concepts; describe the model development process; identify key inputs and describe the quality control process; and identify and manage resources.

Physics at a 100 TeV pp Collider: Standard Model Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mangano, M. L.; Zanderighi, G.; Aguilar Saavedra, J. A.

This report summarises the properties of Standard Model processes at the 100 TeV pp collider. We document the production rates and typical distributions for a number of benchmark Standard Model processes, and discuss new dynamical phenomena arising at the highest energies available at this collider. We discuss the intrinsic physics interest in the measurement of these Standard Model processes, as well as their role as backgrounds for New Physics searches.

Dementia Grief: A Theoretical Model of a Unique Grief Experience

PubMed Central

Blandin, Kesstan; Pepin, Renee

2016-01-01

Previous literature reveals a high prevalence of grief in dementia caregivers before physical death of the person with dementia that is associated with stress, burden, and depression. To date, theoretical models and therapeutic interventions with grief in caregivers have not adequately considered the grief process, but instead have focused on grief as a symptom that manifests within the process of caregiving. The Dementia Grief Model explicates the unique process of pre-death grief in dementia caregivers. In this paper we introduce the Dementia Grief Model, describe the unique characteristics dementia grief, and present the psychological states associated with the process of dementia grief. The model explicates an iterative grief process involving three states – separation, liminality, and re-emergence – each with a dynamic mechanism that facilitates or hinders movement through the dementia grief process. Finally, we offer potential applied research questions informed by the model. PMID:25883036

Intelligent sensor-model automated control of PMR-15 autoclave processing

NASA Technical Reports Server (NTRS)

Hart, S.; Kranbuehl, D.; Loos, A.; Hinds, B.; Koury, J.

1992-01-01

An intelligent sensor model system has been built and used for automated control of the PMR-15 cure process in the autoclave. The system uses frequency-dependent FM sensing (FDEMS), the Loos processing model, and the Air Force QPAL intelligent software shell. The Loos model is used to predict and optimize the cure process including the time-temperature dependence of the extent of reaction, flow, and part consolidation. The FDEMS sensing system in turn monitors, in situ, the removal of solvent, changes in the viscosity, reaction advancement and cure completion in the mold continuously throughout the processing cycle. The sensor information is compared with the optimum processing conditions from the model. The QPAL composite cure control system allows comparison of the sensor monitoring with the model predictions to be broken down into a series of discrete steps and provides a language for making decisions on what to do next regarding time-temperature and pressure.

Parallel computing for automated model calibration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, John S.; Danielson, Gary R.; Schulz, Douglas A.

2002-07-29

Natural resources model calibration is a significant burden on computing and staff resources in modeling efforts. Most assessments must consider multiple calibration objectives (for example magnitude and timing of stream flow peak). An automated calibration process that allows real time updating of data/models, allowing scientists to focus effort on improving models is needed. We are in the process of building a fully featured multi objective calibration tool capable of processing multiple models cheaply and efficiently using null cycle computing. Our parallel processing and calibration software routines have been generically, but our focus has been on natural resources model calibration. Somore » far, the natural resources models have been friendly to parallel calibration efforts in that they require no inter-process communication, only need a small amount of input data and only output a small amount of statistical information for each calibration run. A typical auto calibration run might involve running a model 10,000 times with a variety of input parameters and summary statistical output. In the past model calibration has been done against individual models for each data set. The individual model runs are relatively fast, ranging from seconds to minutes. The process was run on a single computer using a simple iterative process. We have completed two Auto Calibration prototypes and are currently designing a more feature rich tool. Our prototypes have focused on running the calibration in a distributed computing cross platform environment. They allow incorporation of?smart? calibration parameter generation (using artificial intelligence processing techniques). Null cycle computing similar to SETI@Home has also been a focus of our efforts. This paper details the design of the latest prototype and discusses our plans for the next revision of the software.« less

Verifying and Validating Proposed Models for FSW Process Optimization

NASA Technical Reports Server (NTRS)

Schneider, Judith

2008-01-01

This slide presentation reviews Friction Stir Welding (FSW) and the attempts to model the process in order to optimize and improve the process. The studies are ongoing to validate and refine the model of metal flow in the FSW process. There are slides showing the conventional FSW process, a couple of weld tool designs and how the design interacts with the metal flow path. The two basic components of the weld tool are shown, along with geometries of the shoulder design. Modeling of the FSW process is reviewed. Other topics include (1) Microstructure features, (2) Flow Streamlines, (3) Steady-state Nature, and (4) Grain Refinement Mechanisms

Clinical engineering and risk management in healthcare technological process using architecture framework.

PubMed

Signori, Marcos R; Garcia, Renato

2010-01-01

This paper presents a model that aids the Clinical Engineering to deal with Risk Management in the Healthcare Technological Process. The healthcare technological setting is complex and supported by three basics entities: infrastructure (IS), healthcare technology (HT), and human resource (HR). Was used an Enterprise Architecture - MODAF (Ministry of Defence Architecture Framework) - to model this process for risk management. Thus, was created a new model to contribute to the risk management in the HT process, through the Clinical Engineering viewpoint. This architecture model can support and improve the decision making process of the Clinical Engineering to the Risk Management in the Healthcare Technological process.

Method for modeling social care processes for national information exchange.

PubMed

Miettinen, Aki; Mykkänen, Juha; Laaksonen, Maarit

2012-01-01

Finnish social services include 21 service commissions of social welfare including Adoption counselling, Income support, Child welfare, Services for immigrants and Substance abuse care. This paper describes the method used for process modeling in the National project for IT in Social Services in Finland (Tikesos). The process modeling in the project aimed to support common national target state processes from the perspective of national electronic archive, increased interoperability between systems and electronic client documents. The process steps and other aspects of the method are presented. The method was developed, used and refined during the three years of process modeling in the national project.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

Towards Using Reo for Compliance-Aware Business Process Modeling

NASA Astrophysics Data System (ADS)

Arbab, Farhad; Kokash, Natallia; Meng, Sun

Business process modeling and implementation of process supporting infrastructures are two challenging tasks that are not fully aligned. On the one hand, languages such as Business Process Modeling Notation (BPMN) exist to capture business processes at the level of domain analysis. On the other hand, programming paradigms and technologies such as Service-Oriented Computing (SOC) and web services have emerged to simplify the development of distributed web systems that underly business processes. BPMN is the most recognized language for specifying process workflows at the early design steps. However, it is rather declarative and may lead to the executable models which are incomplete or semantically erroneous. Therefore, an approach for expressing and analyzing BPMN models in a formal setting is required. In this paper we describe how BPMN diagrams can be represented by means of a semantically precise channel-based coordination language called Reo which admits formal analysis using model checking and bisimulation techniques. Moreover, since additional requirements may come from various regulatory/legislative documents, we discuss the opportunities offered by Reo and its mathematical abstractions for expressing process-related constraints such as Quality of Service (QoS) or time-aware conditions on process states.

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

Reduced order model based on principal component analysis for process simulation and optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lang, Y.; Malacina, A.; Biegler, L.

2009-01-01

It is well-known that distributed parameter computational fluid dynamics (CFD) models provide more accurate results than conventional, lumped-parameter unit operation models used in process simulation. Consequently, the use of CFD models in process/equipment co-simulation offers the potential to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. Because solving CFD models is time-consuming compared to the overall process simulation, we consider the development of fast reduced order models (ROMs) based on CFD results to closely approximate the high-fidelity equipment models in the co-simulation. By considering process equipment items with complicated geometries and detailed thermodynamic property models,more » this study proposes a strategy to develop ROMs based on principal component analysis (PCA). Taking advantage of commercial process simulation and CFD software (for example, Aspen Plus and FLUENT), we are able to develop systematic CFD-based ROMs for equipment models in an efficient manner. In particular, we show that the validity of the ROM is more robust within well-sampled input domain and the CPU time is significantly reduced. Typically, it takes at most several CPU seconds to evaluate the ROM compared to several CPU hours or more to solve the CFD model. Two case studies, involving two power plant equipment examples, are described and demonstrate the benefits of using our proposed ROM methodology for process simulation and optimization.« less

Orthogonal Gaussian process models

DOE PAGES

Plumlee, Matthew; Joseph, V. Roshan

2017-01-01

Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.

Orthogonal Gaussian process models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Plumlee, Matthew; Joseph, V. Roshan

Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.

An analysis of the Petri net based model of the human body iron homeostasis process.

PubMed

Sackmann, Andrea; Formanowicz, Dorota; Formanowicz, Piotr; Koch, Ina; Blazewicz, Jacek

2007-02-01

In the paper a Petri net based model of the human body iron homeostasis is presented and analyzed. The body iron homeostasis is an important but not fully understood complex process. The modeling of the process presented in the paper is expressed in the language of Petri net theory. An application of this theory to the description of biological processes allows for very precise analysis of the resulting models. Here, such an analysis of the body iron homeostasis model from a mathematical point of view is given.

VARTM Process Modeling of Aerospace Composite Structures

NASA Technical Reports Server (NTRS)

Song, Xiao-Lan; Grimsley, Brian W.; Hubert, Pascal; Cano, Roberto J.; Loos, Alfred C.

2003-01-01

A three-dimensional model was developed to simulate the VARTM composite manufacturing process. The model considers the two important mechanisms that occur during the process: resin flow, and compaction and relaxation of the preform. The model was used to simulate infiltration of a carbon preform with an epoxy resin by the VARTM process. The model predicted flow patterns and preform thickness changes agreed qualitatively with the measured values. However, the predicted total infiltration times were much longer than measured most likely due to the inaccurate preform permeability values used in the simulation.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

PubMed

Lord, Dominique; Washington, Simon P; Ivan, John N

2005-01-01

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

Process-Response Modeling and the Scientific Process.

ERIC Educational Resources Information Center

Fichter, Lynn S.

1988-01-01

Discusses the process-response model (PRM) in its theoretical and practical forms. Describes how geologists attempt to reconstruct the process from the response (the geologic phenomenon) being studied. (TW)

An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health

PubMed Central

Mancy, Rebecca; Brock, Patrick M.; Kao, Rowland R.

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature. PMID:29021983

An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health.

PubMed

Mancy, Rebecca; Brock, Patrick M; Kao, Rowland R

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature.

Modeling of ETL-Processes and Processed Information in Clinical Data Warehousing.

PubMed

Tute, Erik; Steiner, Jochen

2018-01-01

Literature describes a big potential for reuse of clinical patient data. A clinical data warehouse (CDWH) is a means for that. To support management and maintenance of processes extracting, transforming and loading (ETL) data into CDWHs as well as to ease reuse of metadata between regular IT-management, CDWH and secondary data users by providing a modeling approach. Expert survey and literature review to find requirements and existing modeling techniques. An ETL-modeling-technique was developed extending existing modeling techniques. Evaluation by exemplarily modeling existing ETL-process and a second expert survey. Nine experts participated in the first survey. Literature review yielded 15 included publications. Six existing modeling techniques were identified. A modeling technique extending 3LGM² and combining it with openEHR information models was developed and evaluated. Seven experts participated in the evaluation. The developed approach can help in management and maintenance of ETL-processes and could serve as interface between regular IT-management, CDWH and secondary data users.

Electronic Education System Model-2

ERIC Educational Resources Information Center

Güllü, Fatih; Kuusik, Rein; Laanpere, Mart

2015-01-01

In this study we presented new EES Model-2 extended from EES model for more productive implementation in e-learning process design and modelling in higher education. The most updates were related to uppermost instructional layer. We updated learning processes object of the layer for adaptation of educational process for young and old people,…

Agent-Based Computing in Distributed Adversarial Planning

DTIC Science & Technology

2010-08-09

plans. An agent is expected to agree to deviate from its optimal uncoordinated plan only if it improves its position. - process models for opponent...Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Improvements ...plan only if it improves its position. – process models for opponent modeling – We have analyzed the suitability of business process models for creating

Simulating carbon capture by enhanced weathering with croplands: an overview of key processes highlighting areas of future model development

PubMed Central

Quegan, Shaun; Banwart, Steven A.

2017-01-01

Enhanced weathering (EW) aims to amplify a natural sink for CO2 by incorporating powdered silicate rock with high reactive surface area into agricultural soils. The goal is to achieve rapid dissolution of minerals and release of alkalinity with accompanying dissolution of CO2 into soils and drainage waters. EW could counteract phosphorus limitation and greenhouse gas (GHG) emissions in tropical soils, and soil acidification, a common agricultural problem studied with numerical process models over several decades. Here, we review the processes leading to soil acidification in croplands and how the soil weathering CO2 sink is represented in models. Mathematical models capturing the dominant processes and human interventions governing cropland soil chemistry and GHG emissions neglect weathering, while most weathering models neglect agricultural processes. We discuss current approaches to modelling EW and highlight several classes of model having the potential to simulate EW in croplands. Finally, we argue for further integration of process knowledge in mathematical models to capture feedbacks affecting both longer-term CO2 consumption and crop growth and yields. PMID:28381633

Combined Log Inventory and Process Simulation Models for the Planning and Control of Sawmill Operations

Treesearch

Guillermo A. Mendoza; Roger J. Meimban; Philip A. Araman; William G. Luppold

1991-01-01

A log inventory model and a real-time hardwood process simulation model were developed and combined into an integrated production planning and control system for hardwood sawmills. The log inventory model was designed to monitor and periodically update the status of the logs in the log yard. The process simulation model was designed to estimate various sawmill...

Analysis of the packet formation process in packet-switched networks

NASA Astrophysics Data System (ADS)

Meditch, J. S.

Two new queueing system models for the packet formation process in packet-switched telecommunication networks are developed, and their applications in process stability, performance analysis, and optimization studies are illustrated. The first, an M/M/1 queueing system characterization of the process, is a highly aggregated model which is useful for preliminary studies. The second, a marked extension of an earlier M/G/1 model, permits one to investigate stability, performance characteristics, and design of the packet formation process in terms of the details of processor architecture, and hardware and software implementations with processor structure and as many parameters as desired as variables. The two new models together with the earlier M/G/1 characterization span the spectrum of modeling complexity for the packet formation process from basic to advanced.

Studies in astronomical time series analysis. IV - Modeling chaotic and random processes with linear filters

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1990-01-01

While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.

On the meaning of meaning when being mean: commentary on Berkowitz's "on the consideration of automatic as well as controlled psychological processes in aggression".

PubMed

Dodge, Kenneth A

2008-01-01

Berkowitz (this issue) makes a cogent case for his cognitive neo-associationist (CNA) model that some aggressive behaviors occur automatically, emotionally, and through conditioned association with other stimuli. He also proposes that they can occur without "processing," that is, without meaning. He contrasts his position with that of social information processing (SIP) models, which he casts as positing only controlled processing mechanisms for aggressive behavior. However, both CNA and SIP models posit automatic as well as controlled processes in aggressive behavior. Most aggressive behaviors occur through automatic processes, which are nonetheless rule governed. SIP models differ from the CNA model in asserting the essential role of meaning (often through nonconscious, automatic, and emotional processes) in mediating the link between a stimulus and an angry aggressive behavioral response. Copyright 2008 Wiley-Liss, Inc.

A multi-site cognitive task analysis for biomedical query mediation.

PubMed

Hruby, Gregory W; Rasmussen, Luke V; Hanauer, David; Patel, Vimla L; Cimino, James J; Weng, Chunhua

2016-09-01

To apply cognitive task analyses of the Biomedical query mediation (BQM) processes for EHR data retrieval at multiple sites towards the development of a generic BQM process model. We conducted semi-structured interviews with eleven data analysts from five academic institutions and one government agency, and performed cognitive task analyses on their BQM processes. A coding schema was developed through iterative refinement and used to annotate the interview transcripts. The annotated dataset was used to reconstruct and verify each BQM process and to develop a harmonized BQM process model. A survey was conducted to evaluate the face and content validity of this harmonized model. The harmonized process model is hierarchical, encompassing tasks, activities, and steps. The face validity evaluation concluded the model to be representative of the BQM process. In the content validity evaluation, out of the 27 tasks for BQM, 19 meet the threshold for semi-valid, including 3 fully valid: "Identify potential index phenotype," "If needed, request EHR database access rights," and "Perform query and present output to medical researcher", and 8 are invalid. We aligned the goals of the tasks within the BQM model with the five components of the reference interview. The similarity between the process of BQM and the reference interview is promising and suggests the BQM tasks are powerful for eliciting implicit information needs. We contribute a BQM process model based on a multi-site study. This model promises to inform the standardization of the BQM process towards improved communication efficiency and accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Multi-Site Cognitive Task Analysis for Biomedical Query Mediation

PubMed Central

Hruby, Gregory W.; Rasmussen, Luke V.; Hanauer, David; Patel, Vimla; Cimino, James J.; Weng, Chunhua

2016-01-01

Objective To apply cognitive task analyses of the Biomedical query mediation (BQM) processes for EHR data retrieval at multiple sites towards the development of a generic BQM process model. Materials and Methods We conducted semi-structured interviews with eleven data analysts from five academic institutions and one government agency, and performed cognitive task analyses on their BQM processes. A coding schema was developed through iterative refinement and used to annotate the interview transcripts. The annotated dataset was used to reconstruct and verify each BQM process and to develop a harmonized BQM process model. A survey was conducted to evaluate the face and content validity of this harmonized model. Results The harmonized process model is hierarchical, encompassing tasks, activities, and steps. The face validity evaluation concluded the model to be representative of the BQM process. In the content validity evaluation, out of the 27 tasks for BQM, 19 meet the threshold for semi-valid, including 3 fully valid: “Identify potential index phenotype,” “If needed, request EHR database access rights,” and “Perform query and present output to medical researcher”, and 8 are invalid. Discussion We aligned the goals of the tasks within the BQM model with the five components of the reference interview. The similarity between the process of BQM and the reference interview is promising and suggests the BQM tasks are powerful for eliciting implicit information needs. Conclusions We contribute a BQM process model based on a multi-site study. This model promises to inform the standardization of the BQM process towards improved communication efficiency and accuracy. PMID:27435950

Adaptive Filtering Using Recurrent Neural Networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Behavior Models for Software Architecture

DTIC Science & Technology

2014-11-01

MP. Existing process modeling frameworks (BPEL, BPMN [Grosskopf et al. 2009], IDEF) usually follow the “single flowchart” paradigm. MP separates...Process: Business Process Modeling using BPMN , Meghan Kiffer Press. HAREL, D., 1987, A Visual Formalism for Complex Systems. Science of Computer

State of the art in pathology business process analysis, modeling, design and optimization.

PubMed

Schrader, Thomas; Blobel, Bernd; García-Rojo, Marcial; Daniel, Christel; Słodkowska, Janina

2012-01-01

For analyzing current workflows and processes, for improving them, for quality management and quality assurance, for integrating hardware and software components, but also for education, training and communication between different domains' experts, modeling business process in a pathology department is inevitable. The authors highlight three main processes in pathology: general diagnostic, cytology diagnostic, and autopsy. In this chapter, those processes are formally modeled and described in detail. Finally, specialized processes such as immunohistochemistry and frozen section have been considered.

Guiding gate-etch process development using 3D surface reaction modeling for 7nm and beyond

NASA Astrophysics Data System (ADS)

Dunn, Derren; Sporre, John R.; Deshpande, Vaibhav; Oulmane, Mohamed; Gull, Ronald; Ventzek, Peter; Ranjan, Alok

2017-03-01

Increasingly, advanced process nodes such as 7nm (N7) are fundamentally 3D and require stringent control of critical dimensions over high aspect ratio features. Process integration in these nodes requires a deep understanding of complex physical mechanisms to control critical dimensions from lithography through final etch. Polysilicon gate etch processes are critical steps in several device architectures for advanced nodes that rely on self-aligned patterning approaches to gate definition. These processes are required to meet several key metrics: (a) vertical etch profiles over high aspect ratios; (b) clean gate sidewalls free of etch process residue; (c) minimal erosion of liner oxide films protecting key architectural elements such as fins; and (e) residue free corners at gate interfaces with critical device elements. In this study, we explore how hybrid modeling approaches can be used to model a multi-step finFET polysilicon gate etch process. Initial parts of the patterning process through hardmask assembly are modeled using process emulation. Important aspects of gate definition are then modeled using a particle Monte Carlo (PMC) feature scale model that incorporates surface chemical reactions.1 When necessary, species and energy flux inputs to the PMC model are derived from simulations of the etch chamber. The modeled polysilicon gate etch process consists of several steps including a hard mask breakthrough step (BT), main feature etch steps (ME), and over-etch steps (OE) that control gate profiles at the gate fin interface. An additional constraint on this etch flow is that fin spacer oxides are left intact after final profile tuning steps. A natural optimization required from these processes is to maximize vertical gate profiles while minimizing erosion of fin spacer films.2

Impact of Physics Parameterization Ordering in a Global Atmosphere Model

DOE PAGES

Donahue, Aaron S.; Caldwell, Peter M.

2018-02-02

Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid-scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effectmore » of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k-means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution.« less

Impact of Physics Parameterization Ordering in a Global Atmosphere Model

NASA Astrophysics Data System (ADS)

Donahue, Aaron S.; Caldwell, Peter M.

2018-02-01

Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid-scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k-means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution.

Impact of Physics Parameterization Ordering in a Global Atmosphere Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donahue, Aaron S.; Caldwell, Peter M.

Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid-scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effectmore » of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k-means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution.« less

Second Generation Crop Yield Models Review

NASA Technical Reports Server (NTRS)

Hodges, T. (Principal Investigator)

1982-01-01

Second generation yield models, including crop growth simulation models and plant process models, may be suitable for large area crop yield forecasting in the yield model development project. Subjective and objective criteria for model selection are defined and models which might be selected are reviewed. Models may be selected to provide submodels as input to other models; for further development and testing; or for immediate testing as forecasting tools. A plant process model may range in complexity from several dozen submodels simulating (1) energy, carbohydrates, and minerals; (2) change in biomass of various organs; and (3) initiation and development of plant organs, to a few submodels simulating key physiological processes. The most complex models cannot be used directly in large area forecasting but may provide submodels which can be simplified for inclusion into simpler plant process models. Both published and unpublished models which may be used for development or testing are reviewed. Several other models, currently under development, may become available at a later date.

Microphysics in Multi-scale Modeling System with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2012-01-01

Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the microphysics development and its performance for the multi-scale modeling system will be presented.

Application of a Model for Simulating the Vacuum Arc Remelting Process in Titanium Alloys

NASA Astrophysics Data System (ADS)

Patel, Ashish; Tripp, David W.; Fiore, Daniel

Mathematical modeling is routinely used in the process development and production of advanced aerospace alloys to gain greater insight into system dynamics and to predict the effect of process modifications or upsets on final properties. This article describes the application of a 2-D mathematical VAR model presented in previous LMPC meetings. The impact of process parameters on melt pool geometry, solidification behavior, fluid-flow and chemistry in Ti-6Al-4V ingots will be discussed. Model predictions were first validated against the measured characteristics of industrially produced ingots, and process inputs and model formulation were adjusted to match macro-etched pool shapes. The results are compared to published data in the literature. Finally, the model is used to examine ingot chemistry during successive VAR melts.

Event-Driven Process Chains (EPC)

NASA Astrophysics Data System (ADS)

Mendling, Jan

This chapter provides a comprehensive overview of Event-driven Process Chains (EPCs) and introduces a novel definition of EPC semantics. EPCs became popular in the 1990s as a conceptual business process modeling language in the context of reference modeling. Reference modeling refers to the documentation of generic business operations in a model such as service processes in the telecommunications sector, for example. It is claimed that reference models can be reused and adapted as best-practice recommendations in individual companies (see [230, 168, 229, 131, 400, 401, 446, 127, 362, 126]). The roots of reference modeling can be traced back to the Kölner Integrationsmodell (KIM) [146, 147] that was developed in the 1960s and 1970s. In the 1990s, the Institute of Information Systems (IWi) in Saarbrücken worked on a project with SAP to define a suitable business process modeling language to document the processes of the SAP R/3 enterprise resource planning system. There were two results from this joint effort: the definition of EPCs [210] and the documentation of the SAP system in the SAP Reference Model (see [92, 211]). The extensive database of this reference model contains almost 10,000 sub-models: 604 of them non-trivial EPC business process models. The SAP Reference model had a huge impact with several researchers referring to it in their publications (see [473, 235, 127, 362, 281, 427, 415]) as well as motivating the creation of EPC reference models in further domains including computer integrated manufacturing [377, 379], logistics [229] or retail [52]. The wide-spread application of EPCs in business process modeling theory and practice is supported by their coverage in seminal text books for business process management and information systems in general (see [378, 380, 49, 384, 167, 240]). EPCs are frequently used in practice due to a high user acceptance [376] and extensive tool support. Some examples of tools that support EPCs are ARIS Toolset by IDS Scheer AG, AENEIS by ATOSS Software AG, ADONIS by BOC GmbH, Visio by Microsoft Corp., Nautilus by Gedilan Consulting GmbH, and Bonapart by Pikos GmbH. In order to facilitate the interchange of EPC business process models between these tools, there is a tool neutral interchange format called EPC Markup Language (EPML) [283, 285, 286, 287, 289, 290, 291].

Modeling of an integrated fermentation/membrane extraction process for the production of 2-phenylethanol and 2-phenylethylacetate.

PubMed

Adler, Philipp; Hugen, Thorsten; Wiewiora, Marzena; Kunz, Benno

2011-03-07

An unstructured model for an integrated fermentation/membrane extraction process for the production of the aroma compounds 2-phenylethanol and 2-phenylethylacetate by Kluyveromyces marxianus CBS 600 was developed. The extent to which this model, based only on data from the conventional fermentation and separation processes, provided an estimation of the integrated process was evaluated. The effect of product inhibition on specific growth rate and on biomass yield by both aroma compounds was approximated by multivariate regression. Simulations of the respective submodels for fermentation and the separation process matched well with experimental results. With respect to the in situ product removal (ISPR) process, the effect of reduced product inhibition due to product removal on specific growth rate and biomass yield was predicted adequately by the model simulations. Overall product yields were increased considerably in this process (4.0 g/L 2-PE+2-PEA vs. 1.4 g/L in conventional fermentation) and were even higher than predicted by the model. To describe the effect of product concentration on product formation itself, the model was extended using results from the conventional and the ISPR process, thus agreement between model and experimental data improved notably. Therefore, this model can be a useful tool for the development and optimization of an efficient integrated bioprocess. Copyright © 2010 Elsevier Inc. All rights reserved.

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study.

PubMed

Shukla, Nagesh; Keast, John E; Ceglarek, Darek

2014-10-01

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A dynamic dual process model of risky decision making.

PubMed

Diederich, Adele; Trueblood, Jennifer S

2018-03-01

Many phenomena in judgment and decision making are often attributed to the interaction of 2 systems of reasoning. Although these so-called dual process theories can explain many types of behavior, they are rarely formalized as mathematical or computational models. Rather, dual process models are typically verbal theories, which are difficult to conclusively evaluate or test. In the cases in which formal (i.e., mathematical) dual process models have been proposed, they have not been quantitatively fit to experimental data and are often silent when it comes to the timing of the 2 systems. In the current article, we present a dynamic dual process model framework of risky decision making that provides an account of the timing and interaction of the 2 systems and can explain both choice and response-time data. We outline several predictions of the model, including how changes in the timing of the 2 systems as well as time pressure can influence behavior. The framework also allows us to explore different assumptions about how preferences are constructed by the 2 systems as well as the dynamic interaction of the 2 systems. In particular, we examine 3 different possible functional forms of the 2 systems and 2 possible ways the systems can interact (simultaneously or serially). We compare these dual process models with 2 single process models using risky decision making data from Guo, Trueblood, and Diederich (2017). Using this data, we find that 1 of the dual process models significantly outperforms the other models in accounting for both choices and response times. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Development and application of an acceptance testing model

NASA Technical Reports Server (NTRS)

Pendley, Rex D.; Noonan, Caroline H.; Hall, Kenneth R.

1992-01-01

The process of acceptance testing large software systems for NASA has been analyzed, and an empirical planning model of the process constructed. This model gives managers accurate predictions of the staffing needed, the productivity of a test team, and the rate at which the system will pass. Applying the model to a new system shows a high level of agreement between the model and actual performance. The model also gives managers an objective measure of process improvement.

Optimal Estimation with Two Process Models and No Measurements

DTIC Science & Technology

2015-08-01

models will be lost if either of the models includes deterministic modeling errors. 12 5. References and Notes 1. Brown RG, Hwang PYC. Introduction to...independent process models when no measurements are present. The observer follows a derivation similar to that of the discrete time Kalman filter. A simulation...discrete time Kalman filter. A simulation example is provided in which a process model based on the dynamics of a ballistic projectile is blended with an

Flexible Environmental Modeling with Python and Open - GIS

NASA Astrophysics Data System (ADS)

Pryet, Alexandre; Atteia, Olivier; Delottier, Hugo; Cousquer, Yohann

2015-04-01

Numerical modeling now represents a prominent task of environmental studies. During the last decades, numerous commercial programs have been made available to environmental modelers. These software applications offer user-friendly graphical user interfaces that allow an efficient management of many case studies. However, they suffer from a lack of flexibility and closed-source policies impede source code reviewing and enhancement for original studies. Advanced modeling studies require flexible tools capable of managing thousands of model runs for parameter optimization, uncertainty and sensitivity analysis. In addition, there is a growing need for the coupling of various numerical models associating, for instance, groundwater flow modeling to multi-species geochemical reactions. Researchers have produced hundreds of open-source powerful command line programs. However, there is a need for a flexible graphical user interface allowing an efficient processing of geospatial data that comes along any environmental study. Here, we present the advantages of using the free and open-source Qgis platform and the Python scripting language for conducting environmental modeling studies. The interactive graphical user interface is first used for the visualization and pre-processing of input geospatial datasets. Python scripting language is then employed for further input data processing, call to one or several models, and post-processing of model outputs. Model results are eventually sent back to the GIS program, processed and visualized. This approach combines the advantages of interactive graphical interfaces and the flexibility of Python scripting language for data processing and model calls. The numerous python modules available facilitate geospatial data processing and numerical analysis of model outputs. Once input data has been prepared with the graphical user interface, models may be run thousands of times from the command line with sequential or parallel calls. We illustrate this approach with several case studies in groundwater hydrology and geochemistry and provide links to several python libraries that facilitate pre- and post-processing operations.

Modeling nuclear processes by Simulink

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rashid, Nahrul Khair Alang Md, E-mail: nahrul@iium.edu.my

2015-04-29

Modelling and simulation are essential parts in the study of dynamic systems behaviours. In nuclear engineering, modelling and simulation are important to assess the expected results of an experiment before the actual experiment is conducted or in the design of nuclear facilities. In education, modelling can give insight into the dynamic of systems and processes. Most nuclear processes can be described by ordinary or partial differential equations. Efforts expended to solve the equations using analytical or numerical solutions consume time and distract attention from the objectives of modelling itself. This paper presents the use of Simulink, a MATLAB toolbox softwaremore » that is widely used in control engineering, as a modelling platform for the study of nuclear processes including nuclear reactor behaviours. Starting from the describing equations, Simulink models for heat transfer, radionuclide decay process, delayed neutrons effect, reactor point kinetic equations with delayed neutron groups, and the effect of temperature feedback are used as examples.« less

A watershed model of individual differences in fluid intelligence.

PubMed

Kievit, Rogier A; Davis, Simon W; Griffiths, John; Correia, Marta M; Cam-Can; Henson, Richard N

2016-10-01

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Developmental Perspective on Peer Rejection, Deviant Peer Affiliation, and Conduct Problems Among Youth.

PubMed

Chen, Diane; Drabick, Deborah A G; Burgers, Darcy E

2015-12-01

Peer rejection and deviant peer affiliation are linked consistently to the development and maintenance of conduct problems. Two proposed models may account for longitudinal relations among these peer processes and conduct problems: the (a) sequential mediation model, in which peer rejection in childhood and deviant peer affiliation in adolescence mediate the link between early externalizing behaviors and more serious adolescent conduct problems; and (b) parallel process model, in which peer rejection and deviant peer affiliation are considered independent processes that operate simultaneously to increment risk for conduct problems. In this review, we evaluate theoretical models and evidence for associations among conduct problems and (a) peer rejection and (b) deviant peer affiliation. We then consider support for the sequential mediation and parallel process models. Next, we propose an integrated model incorporating both the sequential mediation and parallel process models. Future research directions and implications for prevention and intervention efforts are discussed.

Towards Automatic Processing of Virtual City Models for Simulations

NASA Astrophysics Data System (ADS)

Piepereit, R.; Schilling, A.; Alam, N.; Wewetzer, M.; Pries, M.; Coors, V.

2016-10-01

Especially in the field of numerical simulations, such as flow and acoustic simulations, the interest in using virtual 3D models to optimize urban systems is increasing. The few instances in which simulations were already carried out in practice have been associated with an extremely high manual and therefore uneconomical effort for the processing of models. Using different ways of capturing models in Geographic Information System (GIS) and Computer Aided Engineering (CAE), increases the already very high complexity of the processing. To obtain virtual 3D models suitable for simulation, we developed a tool for automatic processing with the goal to establish ties between the world of GIS and CAE. In this paper we introduce a way to use Coons surfaces for the automatic processing of building models in LoD2, and investigate ways to simplify LoD3 models in order to reduce unnecessary information for a numerical simulation.

Identification of Biokinetic Models Using the Concept of Extents.

PubMed

Mašić, Alma; Srinivasan, Sriniketh; Billeter, Julien; Bonvin, Dominique; Villez, Kris

2017-07-05

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

A Developmental Perspective on Peer Rejection, Deviant Peer Affiliation, and Conduct Problems among Youth

PubMed Central

Chen, Diane; Drabick, Deborah A. G.; Burgers, Darcy E.

2015-01-01

Peer rejection and deviant peer affiliation are linked consistently to the development and maintenance of conduct problems. Two proposed models may account for longitudinal relations among these peer processes and conduct problems: the (a) sequential mediation model, in which peer rejection in childhood and deviant peer affiliation in adolescence mediate the link between early externalizing behaviors and more serious adolescent conduct problems; and (b) parallel process model, in which peer rejection and deviant peer affiliation are considered independent processes that operate simultaneously to increment risk for conduct problems. In this review, we evaluate theoretical models and evidence for associations among conduct problems and (a) peer rejection and (b) deviant peer affiliation. We then consider support for the sequential mediation and parallel process models. Next, we propose an integrated model incorporating both the sequential mediation and parallel process models. Future research directions and implications for prevention and intervention efforts are discussed. PMID:25410430

Measurement-based reliability/performability models

NASA Technical Reports Server (NTRS)

Hsueh, Mei-Chen

1987-01-01

Measurement-based models based on real error-data collected on a multiprocessor system are described. Model development from the raw error-data to the estimation of cumulative reward is also described. A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.

Dynamic modeling and analyses of simultaneous saccharification and fermentation process to produce bio-ethanol from rice straw.

PubMed

Ko, Jordon; Su, Wen-Jun; Chien, I-Lung; Chang, Der-Ming; Chou, Sheng-Hsin; Zhan, Rui-Yu

2010-02-01

The rice straw, an agricultural waste from Asians' main provision, was collected as feedstock to convert cellulose into ethanol through the enzymatic hydrolysis and followed by the fermentation process. When the two process steps are performed sequentially, it is referred to as separate hydrolysis and fermentation (SHF). The steps can also be performed simultaneously, i.e., simultaneous saccharification and fermentation (SSF). In this research, the kinetic model parameters of the cellulose saccharification process step using the rice straw as feedstock is obtained from real experimental data of cellulase hydrolysis. Furthermore, this model can be combined with a fermentation model at high glucose and ethanol concentrations to form a SSF model. The fermentation model is based on cybernetic approach from a paper in the literature with an extension of including both the glucose and ethanol inhibition terms to approach more to the actual plants. Dynamic effects of the operating variables in the enzymatic hydrolysis and the fermentation models will be analyzed. The operation of the SSF process will be compared to the SHF process. It is shown that the SSF process is better in reducing the processing time when the product (ethanol) concentration is high. The means to improve the productivity of the overall SSF process, by properly using aeration during the batch operation will also be discussed.

Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events.

PubMed

Chan, Kwun Chuen Gary; Wang, Mei-Cheng

2017-01-01

Recurrent event processes with marker measurements are mostly and largely studied with forward time models starting from an initial event. Interestingly, the processes could exhibit important terminal behavior during a time period before occurrence of the failure event. A natural and direct way to study recurrent events prior to a failure event is to align the processes using the failure event as the time origin and to examine the terminal behavior by a backward time model. This paper studies regression models for backward recurrent marker processes by counting time backward from the failure event. A three-level semiparametric regression model is proposed for jointly modeling the time to a failure event, the backward recurrent event process, and the marker observed at the time of each backward recurrent event. The first level is a proportional hazards model for the failure time, the second level is a proportional rate model for the recurrent events occurring before the failure event, and the third level is a proportional mean model for the marker given the occurrence of a recurrent event backward in time. By jointly modeling the three components, estimating equations can be constructed for marked counting processes to estimate the target parameters in the three-level regression models. Large sample properties of the proposed estimators are studied and established. The proposed models and methods are illustrated by a community-based AIDS clinical trial to examine the terminal behavior of frequencies and severities of opportunistic infections among HIV infected individuals in the last six months of life.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

PubMed Central

Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-01-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017 PMID:28786215

Modelling Of Flotation Processes By Classical Mathematical Methods - A Review

NASA Astrophysics Data System (ADS)

Jovanović, Ivana; Miljanović, Igor

2015-12-01

Flotation process modelling is not a simple task, mostly because of the process complexity, i.e. the presence of a large number of variables that (to a lesser or a greater extent) affect the final outcome of the mineral particles separation based on the differences in their surface properties. The attempts toward the development of the quantitative predictive model that would fully describe the operation of an industrial flotation plant started in the middle of past century and it lasts to this day. This paper gives a review of published research activities directed toward the development of flotation models based on the classical mathematical rules. The description and systematization of classical flotation models were performed according to the available references, with emphasize exclusively given to the flotation process modelling, regardless of the model application in a certain control system. In accordance with the contemporary considerations, models were classified as the empirical, probabilistic, kinetic and population balance types. Each model type is presented through the aspects of flotation modelling at the macro and micro process levels.

An Integrated Model of Emotion Processes and Cognition in Social Information Processing.

ERIC Educational Resources Information Center

Lemerise, Elizabeth A.; Arsenio, William F.

2000-01-01

Interprets literature on contributions of social cognitive and emotion processes to children's social competence in the context of an integrated model of emotion processes and cognition in social information processing. Provides neurophysiological and functional evidence for the centrality of emotion processes in personal-social decision making.…

Growing up and role modeling: a theory in Iranian nursing students' education.

PubMed

Mokhtari Nouri, Jamileh; Ebadi, Abbas; Alhani, Fatemeh; Rejeh, Nahid

2014-11-16

One of the key strategies in students' learning is being affected by models. Understanding the role-modeling process in education will help to make greater use of this training strategy. The aim of this grounded theory study was to explore Iranian nursing students and instructors' experiences about role modeling process. Data was analyzed by Glaserian's Grounded Theory methodology through semi-structured interviews with 7 faculty members, 2 nursing students; the three focus group discussions with 20 nursing students based on purposive and theoretical sampling was done for explaining role modeling process from four nursing faculties in Tehran. Through basic coding, an effort to comprehensive growth and excellence was made with the basic social process consisting the core category and through selective coding three phases were identified as: realizing and exposure to inadequate human and professional growth, facilitating human and professional growth and evolution. The role modeling process is taking place unconscious, involuntary, dynamic and with positive progressive process in order to facilitate overall growth in nursing student. Accordingly, the design and implementation of the designed model can be used to make this unconscious to conscious, active and voluntarily processes a process to help education administrators of nursing colleges and supra organization to prevent threats to human and professional in nursing students' education and promote nursing students' growth.

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

NASA Astrophysics Data System (ADS)

Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif; Laine, Marko; Makela, Jarmo; Peltola, Olli; Vesala, Timo; Aalto, Tuula

2018-03-01

Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.

Fluorescence Spectroscopy and Chemometric Modeling for Bioprocess Monitoring

PubMed Central

Faassen, Saskia M.; Hitzmann, Bernd

2015-01-01

On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables. PMID:25942644

Milestones of mathematical model for business process management related to cost estimate documentation in petroleum industry

NASA Astrophysics Data System (ADS)

Khamidullin, R. I.

2018-05-01

The paper is devoted to milestones of the optimal mathematical model for a business process related to cost estimate documentation compiled during construction and reconstruction of oil and gas facilities. It describes the study and analysis of fundamental issues in petroleum industry, which are caused by economic instability and deterioration of a business strategy. Business process management is presented as business process modeling aimed at the improvement of the studied business process, namely main criteria of optimization and recommendations for the improvement of the above-mentioned business model.

NON-HOMOGENEOUS POISSON PROCESS MODEL FOR GENETIC CROSSOVER INTERFERENCE.

PubMed

Leu, Szu-Yun; Sen, Pranab K

2014-01-01

The genetic crossover interference is usually modeled with a stationary renewal process to construct the genetic map. We propose two non-homogeneous, also dependent, Poisson process models applied to the known physical map. The crossover process is assumed to start from an origin and to occur sequentially along the chromosome. The increment rate depends on the position of the markers and the number of crossover events occurring between the origin and the markers. We show how to obtain parameter estimates for the process and use simulation studies and real Drosophila data to examine the performance of the proposed models.

The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes

ERIC Educational Resources Information Center

Robusto, Egidio; Stefanutti, Luca; Anselmi, Pasquale

2010-01-01

Within the theoretical framework of knowledge space theory, a probabilistic skill multimap model for assessing learning processes is proposed. The learning process of a student is modeled as a function of the student's knowledge and of an educational intervention on the attainment of specific skills required to solve problems in a knowledge…

Stochastic Processes as True-Score Models for Highly Speeded Mental Tests.

ERIC Educational Resources Information Center

Moore, William E.

The previous theoretical development of the Poisson process as a strong model for the true-score theory of mental tests is discussed, and additional theoretical properties of the model from the standpoint of individual examinees are developed. The paper introduces the Erlang process as a family of test theory models and shows in the context of…

Testing Signal-Detection Models of Yes/No and Two-Alternative Forced-Choice Recognition Memory

ERIC Educational Resources Information Center

Jang, Yoonhee; Wixted, John T.; Huber, David E.

2009-01-01

The current study compared 3 models of recognition memory in their ability to generalize across yes/no and 2-alternative forced-choice (2AFC) testing. The unequal-variance signal-detection model assumes a continuous memory strength process. The dual-process signal-detection model adds a thresholdlike recollection process to a continuous…

Forest forming process and dynamic vegetation models under global change

Treesearch

A. Shvidenko; E. Gustafson

2009-01-01

The paper analyzes mathematical models that are used to project the dynamics of forest ecosystems on different spatial and temporal scales. Landscape disturbance and succession models (LDSMs) are of a particular interest for studying the forest forming process in Northern Eurasia. They have a solid empirical background and are able to model ecological processes under...

Conceptualization of Approaches and Thought Processes Emerging in Validating of Model in Mathematical Modeling in Technology Aided Environment

ERIC Educational Resources Information Center

Hidiroglu, Çaglar Naci; Bukova Güzel, Esra

2013-01-01

The aim of the present study is to conceptualize the approaches displayed for validation of model and thought processes provided in mathematical modeling process performed in technology-aided learning environment. The participants of this grounded theory study were nineteen secondary school mathematics student teachers. The data gathered from the…

Modeling Processes of 4th-Year Middle-School Students and the Difficulties Encountered

ERIC Educational Resources Information Center

Eraslan, Ali; Kant, Sinem

2015-01-01

Mathematics teachers have recently begun to stress the need for teaching models and modeling approaches that encompass cognitive and meta-cognitive thought processes for every level of schooling, starting from primary school through to higher education. The objective of this study is to examine modeling processes with the help of modeling…

The Difficult Process of Scientific Modelling: An Analysis Of Novices' Reasoning During Computer-Based Modelling

ERIC Educational Resources Information Center

Sins, Patrick H. M.; Savelsbergh, Elwin R.; van Joolingen, Wouter R.

2005-01-01

Although computer modelling is widely advocated as a way to offer students a deeper understanding of complex phenomena, the process of modelling is rather complex itself and needs scaffolding. In order to offer adequate support, a thorough understanding of the reasoning processes students employ and of difficulties they encounter during a…

Availability Control for Means of Transport in Decisive Semi-Markov Models of Exploitation Process

NASA Astrophysics Data System (ADS)

Migawa, Klaudiusz

2012-12-01

The issues presented in this research paper refer to problems connected with the control process for exploitation implemented in the complex systems of exploitation for technical objects. The article presents the description of the method concerning the control availability for technical objects (means of transport) on the basis of the mathematical model of the exploitation process with the implementation of the decisive processes by semi-Markov. The presented method means focused on the preparing the decisive for the exploitation process for technical objects (semi-Markov model) and after that specifying the best control strategy (optimal strategy) from among possible decisive variants in accordance with the approved criterion (criteria) of the activity evaluation of the system of exploitation for technical objects. In the presented method specifying the optimal strategy for control availability in the technical objects means a choice of a sequence of control decisions made in individual states of modelled exploitation process for which the function being a criterion of evaluation reaches the extreme value. In order to choose the optimal control strategy the implementation of the genetic algorithm was chosen. The opinions were presented on the example of the exploitation process of the means of transport implemented in the real system of the bus municipal transport. The model of the exploitation process for the means of transports was prepared on the basis of the results implemented in the real transport system. The mathematical model of the exploitation process was built taking into consideration the fact that the model of the process constitutes the homogenous semi-Markov process.

Predictive modeling capabilities from incident powder and laser to mechanical properties for laser directed energy deposition

NASA Astrophysics Data System (ADS)

Shin, Yung C.; Bailey, Neil; Katinas, Christopher; Tan, Wenda

2018-05-01

This paper presents an overview of vertically integrated comprehensive predictive modeling capabilities for directed energy deposition processes, which have been developed at Purdue University. The overall predictive models consist of vertically integrated several modules, including powder flow model, molten pool model, microstructure prediction model and residual stress model, which can be used for predicting mechanical properties of additively manufactured parts by directed energy deposition processes with blown powder as well as other additive manufacturing processes. Critical governing equations of each model and how various modules are connected are illustrated. Various illustrative results along with corresponding experimental validation results are presented to illustrate the capabilities and fidelity of the models. The good correlations with experimental results prove the integrated models can be used to design the metal additive manufacturing processes and predict the resultant microstructure and mechanical properties.

Predictive modeling capabilities from incident powder and laser to mechanical properties for laser directed energy deposition

NASA Astrophysics Data System (ADS)

Shin, Yung C.; Bailey, Neil; Katinas, Christopher; Tan, Wenda

2018-01-01

This paper presents an overview of vertically integrated comprehensive predictive modeling capabilities for directed energy deposition processes, which have been developed at Purdue University. The overall predictive models consist of vertically integrated several modules, including powder flow model, molten pool model, microstructure prediction model and residual stress model, which can be used for predicting mechanical properties of additively manufactured parts by directed energy deposition processes with blown powder as well as other additive manufacturing processes. Critical governing equations of each model and how various modules are connected are illustrated. Various illustrative results along with corresponding experimental validation results are presented to illustrate the capabilities and fidelity of the models. The good correlations with experimental results prove the integrated models can be used to design the metal additive manufacturing processes and predict the resultant microstructure and mechanical properties.

Improved compliance by BPM-driven workflow automation.

PubMed

Holzmüller-Laue, Silke; Göde, Bernd; Fleischer, Heidi; Thurow, Kerstin

2014-12-01

Using methods and technologies of business process management (BPM) for the laboratory automation has important benefits (i.e., the agility of high-level automation processes, rapid interdisciplinary prototyping and implementation of laboratory tasks and procedures, and efficient real-time process documentation). A principal goal of the model-driven development is the improved transparency of processes and the alignment of process diagrams and technical code. First experiences of using the business process model and notation (BPMN) show that easy-to-read graphical process models can achieve and provide standardization of laboratory workflows. The model-based development allows one to change processes quickly and an easy adaption to changing requirements. The process models are able to host work procedures and their scheduling in compliance with predefined guidelines and policies. Finally, the process-controlled documentation of complex workflow results addresses modern laboratory needs of quality assurance. BPMN 2.0 as an automation language to control every kind of activity or subprocess is directed to complete workflows in end-to-end relationships. BPMN is applicable as a system-independent and cross-disciplinary graphical language to document all methods in laboratories (i.e., screening procedures or analytical processes). That means, with the BPM standard, a communication method of sharing process knowledge of laboratories is also available. © 2014 Society for Laboratory Automation and Screening.

Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Simpson, J.; Baker, D.; Braun, S.; Chou, M.-D.; Ferrier, B.; Johnson, D.; Khain, A.; Lang, S.; Lynn, B.

2001-01-01

The response of cloud systems to their environment is an important link in a chain of processes responsible for monsoons, frontal depression, El Nino Southern Oscillation (ENSO) episodes and other climate variations (e.g., 30-60 day intra-seasonal oscillations). Numerical models of cloud properties provide essential insights into the interactions of clouds with each other, with their surroundings, and with land and ocean surfaces. Significant advances are currently being made in the modeling of rainfall and rain-related cloud processes, ranging in scales from the very small up to the simulation of an extensive population of raining cumulus clouds in a tropical- or midlatitude-storm environment. The Goddard Cumulus Ensemble (GCE) model is a multi-dimensional nonhydrostatic dynamic/microphysical cloud resolving model. It has been used to simulate many different mesoscale convective systems that occurred in various geographic locations. In this paper, recent GCE model improvements (microphysics, radiation and surface processes) will be described as well as their impact on the development of precipitation events from various geographic locations. The performance of these new physical processes will be examined by comparing the model results with observations. In addition, the explicit interactive processes between cloud, radiation and surface processes will be discussed.

A first-principle model of 300 mm Czochralski single-crystal Si production process for predicting crystal radius and crystal growth rate

NASA Astrophysics Data System (ADS)

Zheng, Zhongchao; Seto, Tatsuru; Kim, Sanghong; Kano, Manabu; Fujiwara, Toshiyuki; Mizuta, Masahiko; Hasebe, Shinji

2018-06-01

The Czochralski (CZ) process is the dominant method for manufacturing large cylindrical single-crystal ingots for the electronics industry. Although many models and control methods for the CZ process have been proposed, they were only tested with small equipment and only a few industrial application were reported. In this research, we constructed a first-principle model for controlling industrial CZ processes that produce 300 mm single-crystal silicon ingots. The developed model, which consists of energy, mass balance, hydrodynamic, and geometrical equations, calculates the crystal radius and the crystal growth rate as output variables by using the heater input, the crystal pulling rate, and the crucible rise rate as input variables. To improve accuracy, we modeled the CZ process by considering factors such as changes in the positions of the crucible and the melt level. The model was validated with the operation data from an industrial 300 mm CZ process. We compared the calculated and actual values of the crystal radius and the crystal growth rate, and the results demonstrated that the developed model simulated the industrial process with high accuracy.

Guideline validation in multiple trauma care through business process modeling.

PubMed

Stausberg, Jürgen; Bilir, Hüseyin; Waydhas, Christian; Ruchholtz, Steffen

2003-07-01

Clinical guidelines can improve the quality of care in multiple trauma. In our Department of Trauma Surgery a specific guideline is available paper-based as a set of flowcharts. This format is appropriate for the use by experienced physicians but insufficient for electronic support of learning, workflow and process optimization. A formal and logically consistent version represented with a standardized meta-model is necessary for automatic processing. In our project we transferred the paper-based into an electronic format and analyzed the structure with respect to formal errors. Several errors were detected in seven error categories. The errors were corrected to reach a formally and logically consistent process model. In a second step the clinical content of the guideline was revised interactively using a process-modeling tool. Our study reveals that guideline development should be assisted by process modeling tools, which check the content in comparison to a meta-model. The meta-model itself could support the domain experts in formulating their knowledge systematically. To assure sustainability of guideline development a representation independent of specific applications or specific provider is necessary. Then, clinical guidelines could be used for eLearning, process optimization and workflow management additionally.

RFI and SCRIMP Model Development and Verification

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Sayre, Jay

2000-01-01

Vacuum-Assisted Resin Transfer Molding (VARTM) processes are becoming promising technologies in the manufacturing of primary composite structures in the aircraft industry as well as infrastructure. A great deal of work still needs to be done on efforts to reduce the costly trial-and-error methods of VARTM processing that are currently in practice today. A computer simulation model of the VARTM process would provide a cost-effective tool in the manufacturing of composites utilizing this technique. Therefore, the objective of this research was to modify an existing three-dimensional, Resin Film Infusion (RFI)/Resin Transfer Molding (RTM) model to include VARTM simulation capabilities and to verify this model with the fabrication of aircraft structural composites. An additional objective was to use the VARTM model as a process analysis tool, where this tool would enable the user to configure the best process for manufacturing quality composites. Experimental verification of the model was performed by processing several flat composite panels. The parameters verified included flow front patterns and infiltration times. The flow front patterns were determined to be qualitatively accurate, while the simulated infiltration times over predicted experimental times by 8 to 10%. Capillary and gravitational forces were incorporated into the existing RFI/RTM model in order to simulate VARTM processing physics more accurately. The theoretical capillary pressure showed the capability to reduce the simulated infiltration times by as great as 6%. The gravity, on the other hand, was found to be negligible for all cases. Finally, the VARTM model was used as a process analysis tool. This enabled the user to determine such important process constraints as the location and type of injection ports and the permeability and location of the high-permeable media. A process for a three-stiffener composite panel was proposed. This configuration evolved from the variation of the process constraints in the modeling of several different composite panels. The configuration was proposed by considering such factors as: infiltration time, the number of vacuum ports, and possible areas of void entrapment.

Exploring the Processes of Generating LOD (0-2) Citygml Models in Greater Municipality of Istanbul

NASA Astrophysics Data System (ADS)

Buyuksalih, I.; Isikdag, U.; Zlatanova, S.

2013-08-01

3D models of cities, visualised and exploded in 3D virtual environments have been available for several years. Currently a large number of impressive realistic 3D models have been regularly presented at scientific, professional and commercial events. One of the most promising developments is OGC standard CityGML. CityGML is object-oriented model that support 3D geometry and thematic semantics, attributes and relationships, and offers advanced options for realistic visualization. One of the very attractive characteristics of the model is the support of 5 levels of detail (LOD), starting from 2.5D less accurate model (LOD0) and ending with very detail indoor model (LOD4). Different local government offices and municipalities have different needs when utilizing the CityGML models, and the process of model generation depends on local and domain specific needs. Although the processes (i.e. the tasks and activities) for generating the models differs depending on its utilization purpose, there are also some common tasks (i.e. common denominator processes) in the model generation of City GML models. This paper focuses on defining the common tasks in generation of LOD (0-2) City GML models and representing them in a formal way with process modeling diagrams.

Evaluation of the energy efficiency of enzyme fermentation by mechanistic modeling.

PubMed

Albaek, Mads O; Gernaey, Krist V; Hansen, Morten S; Stocks, Stuart M

2012-04-01

Modeling biotechnological processes is key to obtaining increased productivity and efficiency. Particularly crucial to successful modeling of such systems is the coupling of the physical transport phenomena and the biological activity in one model. We have applied a model for the expression of cellulosic enzymes by the filamentous fungus Trichoderma reesei and found excellent agreement with experimental data. The most influential factor was demonstrated to be viscosity and its influence on mass transfer. Not surprisingly, the biological model is also shown to have high influence on the model prediction. At different rates of agitation and aeration as well as headspace pressure, we can predict the energy efficiency of oxygen transfer, a key process parameter for economical production of industrial enzymes. An inverse relationship between the productivity and energy efficiency of the process was found. This modeling approach can be used by manufacturers to evaluate the enzyme fermentation process for a range of different process conditions with regard to energy efficiency. Copyright © 2011 Wiley Periodicals, Inc.

Renewal processes based on generalized Mittag-Leffler waiting times

NASA Astrophysics Data System (ADS)

Cahoy, Dexter O.; Polito, Federico

2013-03-01

The fractional Poisson process has recently attracted experts from several fields of study. Its natural generalization of the ordinary Poisson process made the model more appealing for real-world applications. In this paper, we generalized the standard and fractional Poisson processes through the waiting time distribution, and showed their relations to an integral operator with a generalized Mittag-Leffler function in the kernel. The waiting times of the proposed renewal processes have the generalized Mittag-Leffler and stretched-squashed Mittag-Leffler distributions. Note that the generalizations naturally provide greater flexibility in modeling real-life renewal processes. Algorithms to simulate sample paths and to estimate the model parameters are derived. Note also that these procedures are necessary to make these models more usable in practice. State probabilities and other qualitative or quantitative features of the models are also discussed.

Data-driven process decomposition and robust online distributed modelling for large-scale processes

NASA Astrophysics Data System (ADS)

Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

2018-02-01

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

[On-line processing mechanisms in text comprehension: a theoretical review on constructing situation models].

PubMed

Iseki, Ryuta

2004-12-01

This article reviewed research on construction of situation models during reading. To position variety of research in overall process appropriately, an unitary framework was devised in terms of three theories for on-line processing: resonance process, event-indexing model, and constructionist theory. Resonance process was treated as a basic activation mechanism in the framework. Event-indexing model was regarded as a screening system which selected and encoded activated information in situation models along with situational dimensions. Constructionist theory was considered to have a supervisory role based on coherence and explanation. From a view of the unitary framework, some problems concerning each theory were examined and possible interpretations were given. Finally, it was pointed out that there were little theoretical arguments on associative processing at global level and encoding text- and inference-information into long-term memory.

Petri net based model of the body iron homeostasis.

PubMed

Formanowicz, Dorota; Sackmann, Andrea; Formanowicz, Piotr; Błazewicz, Jacek

2007-10-01

The body iron homeostasis is a not fully understood complex process. Despite the fact that some components of this process have been described in the literature, the complete model of the whole process has not been proposed. In this paper a Petri net based model of the body iron homeostasis is presented. Recently, Petri nets have been used for describing and analyzing various biological processes since they allow modeling the system under consideration very precisely. The main result presented in the paper is twofold, i.e., an informal description of the main part of the whole iron homeostasis process is described, and then it is also formulated in the formal language of Petri net theory. This model allows for a possible simulation of the process, since Petri net theory provides a lot of established analysis techniques.

Computer Models of Personality: Implications for Measurement

ERIC Educational Resources Information Center

Cranton, P. A.

1976-01-01

Current research on computer models of personality is reviewed and categorized under five headings: (1) models of belief systems; (2) models of interpersonal behavior; (3) models of decision-making processes; (4) prediction models; and (5) theory-based simulations of specific processes. The use of computer models in personality measurement is…

BPMN as a Communication Language for the Process- and Event-Oriented Perspectives in Fact-Oriented Conceptual Models

NASA Astrophysics Data System (ADS)

Bollen, Peter

In this paper we will show how the OMG specification of BPMN (Business Process Modeling Notation) can be used to model the process- and event-oriented perspectives of an application subject area. We will illustrate how the fact-oriented conceptual models for the information-, process- and event perspectives can be used in a 'bottom-up' approach for creating a BPMN model in combination with other approaches, e.g. the use of a textual description. We will use the common doctor's office example as a running example in this article.

The Process Communication Model: Understanding Ourselves and Others.

ERIC Educational Resources Information Center

Gilbert, Michael

1996-01-01

The Process Communication Model is based on personality types (reactors, persisters, workaholics, dreamers, rebels, and promoters) denoting different sets of behaviors, perceptions, and motivators that influence individual learning and teaching styles. The model is comprehensive and process-oriented, covering interaction styles, communication…

DEVELOPMENT OF CAPE-OPEN COMPLIANT PROCESS MODELING COMPONENTS IN MICROSOFT .NET

EPA Science Inventory

The CAPE-OPEN middleware standards were created to allow process modeling components (PMCs) developed by third parties to be used in any process modeling environment (PME) utilizing these standards. The CAPE-OPEN middleware specifications were based upon both Microsoft's Compone...

Integrated approaches to the application of advanced modeling technology in process development and optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allgor, R.J.; Feehery, W.F.; Tolsma, J.E.

The batch process development problem serves as good candidate to guide the development of process modeling environments. It demonstrates that very robust numerical techniques are required within an environment that can collect, organize, and maintain the data and models required to address the batch process development problem. This paper focuses on improving the robustness and efficiency of the numerical algorithms required in such a modeling environment through the development of hybrid numerical and symbolic strategies.

Modeling the Fluid Withdraw and Injection Induced Earthquakes

NASA Astrophysics Data System (ADS)

Meng, C.

2016-12-01

We present an open source numerical code, Defmod, that allows one to model the induced seismicity in an efficient and standalone manner. The fluid withdraw and injection induced earthquake has been a great concern to the industries including oil/gas, wastewater disposal and CO2 sequestration. Being able to numerically model the induced seismicity is long desired. To do that, one has to consider at lease two processes, a steady process that describes the inducing and aseismic stages before and in between the seismic events, and an abrupt process that describes the dynamic fault rupture accompanied by seismic energy radiations during the events. The steady process can be adequately modeled by a quasi-static model, while the abrupt process has to be modeled by a dynamic model. In most of the published modeling works, only one of these processes is considered. The geomechanicists and reservoir engineers are focused more on the quasi-static modeling, whereas the geophysicists and seismologists are focused more on the dynamic modeling. The finite element code Defmod combines these two models into a hybrid model that uses the failure criterion and frictional laws to adaptively switch between the (quasi-)static and dynamic states. The code is capable of modeling episodic fault rupture driven by quasi-static loading, e.g. due to reservoir fluid withdraw and/or injection, and by dynamic loading, e.g. due to the foregoing earthquakes. We demonstrate a case study for the 2013 Azle earthquake.

Single Plant Root System Modeling under Soil Moisture Variation

NASA Astrophysics Data System (ADS)

Yabusaki, S.; Fang, Y.; Chen, X.; Scheibe, T. D.

2016-12-01

A prognostic Virtual Plant-Atmosphere-Soil System (vPASS) model is being developed that integrates comprehensively detailed mechanistic single plant modeling with microbial, atmospheric, and soil system processes in its immediate environment. Three broad areas of process module development are targeted: Incorporating models for root growth and function, rhizosphere interactions with bacteria and other organisms, litter decomposition and soil respiration into established porous media flow and reactive transport models Incorporating root/shoot transport, growth, photosynthesis and carbon allocation process models into an integrated plant physiology model Incorporating transpiration, Volatile Organic Compounds (VOC) emission, particulate deposition and local atmospheric processes into a coupled plant/atmosphere model. The integrated plant ecosystem simulation capability is being developed as open source process modules and associated interfaces under a modeling framework. The initial focus addresses the coupling of root growth, vascular transport system, and soil under drought scenarios. Two types of root water uptake modeling approaches are tested: continuous root distribution and constitutive root system architecture. The continuous root distribution models are based on spatially averaged root development process parameters, which are relatively straightforward to accommodate in the continuum soil flow and reactive transport module. Conversely, the constitutive root system architecture models use root growth rates, root growth direction, and root branching to evolve explicit root geometries. The branching topologies require more complex data structures and additional input parameters. Preliminary results are presented for root model development and the vascular response to temporal and spatial variations in soil conditions.

Green Pea and Garlic Puree Model Food Development for Thermal Pasteurization Process Quality Evaluation.

PubMed

Bornhorst, Ellen R; Tang, Juming; Sablani, Shyam S; Barbosa-Cánovas, Gustavo V; Liu, Fang

2017-07-01

Development and selection of model foods is a critical part of microwave thermal process development, simulation validation, and optimization. Previously developed model foods for pasteurization process evaluation utilized Maillard reaction products as the time-temperature integrators, which resulted in similar temperature sensitivity among the models. The aim of this research was to develop additional model foods based on different time-temperature integrators, determine their dielectric properties and color change kinetics, and validate the optimal model food in hot water and microwave-assisted pasteurization processes. Color, quantified using a * value, was selected as the time-temperature indicator for green pea and garlic puree model foods. Results showed 915 MHz microwaves had a greater penetration depth into the green pea model food than the garlic. a * value reaction rates for the green pea model were approximately 4 times slower than in the garlic model food; slower reaction rates were preferred for the application of model food in this study, that is quality evaluation for a target process of 90 °C for 10 min at the cold spot. Pasteurization validation used the green pea model food and results showed that there were quantifiable differences between the color of the unheated control, hot water pasteurization, and microwave-assisted thermal pasteurization system. Both model foods developed in this research could be utilized for quality assessment and optimization of various thermal pasteurization processes. © 2017 Institute of Food Technologists®.

Active vs. Passive Television Viewing: A Model of the Development of Television Information Processing by Children.

ERIC Educational Resources Information Center

Wright, John C.; And Others

A conceptual model of how children process televised information was developed with the goal of identifying those parameters of the process that are both measurable and manipulable in research settings. The model presented accommodates the nature of information processing both by the child and by the presentation by the medium. Presentation is…

Cost model relationships between textile manufacturing processes and design details for transport fuselage elements

NASA Technical Reports Server (NTRS)

Metschan, Stephen L.; Wilden, Kurtis S.; Sharpless, Garrett C.; Andelman, Rich M.

1993-01-01

Textile manufacturing processes offer potential cost and weight advantages over traditional composite materials and processes for transport fuselage elements. In the current study, design cost modeling relationships between textile processes and element design details were developed. Such relationships are expected to help future aircraft designers to make timely decisions on the effect of design details and overall configurations on textile fabrication costs. The fundamental advantage of a design cost model is to insure that the element design is cost effective for the intended process. Trade studies on the effects of processing parameters also help to optimize the manufacturing steps for a particular structural element. Two methods of analyzing design detail/process cost relationships developed for the design cost model were pursued in the current study. The first makes use of existing databases and alternative cost modeling methods (e.g. detailed estimating). The second compares design cost model predictions with data collected during the fabrication of seven foot circumferential frames for ATCAS crown test panels. The process used in this case involves 2D dry braiding and resin transfer molding of curved 'J' cross section frame members having design details characteristic of the baseline ATCAS crown design.

[Monitoring method for macroporous resin column chromatography process of salvianolic acids based on near infrared spectroscopy].

PubMed

Hou, Xiang-Mei; Zhang, Lei; Yue, Hong-Shui; Ju, Ai-Chun; Ye, Zheng-Liang

2016-07-01

To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing. Copyright© by the Chinese Pharmaceutical Association.

The Context, Process, and Outcome Evaluation Model for Organisational Health Interventions

PubMed Central

Fridrich, Annemarie; Jenny, Gregor J.; Bauer, Georg F.

2015-01-01

To facilitate evaluation of complex, organisational health interventions (OHIs), this paper aims at developing a context, process, and outcome (CPO) evaluation model. It builds on previous model developments in the field and advances them by clearly defining and relating generic evaluation categories for OHIs. Context is defined as the underlying frame that influences and is influenced by an OHI. It is further differentiated into the omnibus and discrete contexts. Process is differentiated into the implementation process, as the time-limited enactment of the original intervention plan, and the change process of individual and collective dynamics triggered by the implementation process. These processes lead to proximate, intermediate, and distal outcomes, as all results of the change process that are meaningful for various stakeholders. Research questions that might guide the evaluation of an OHI according to the CPO categories and a list of concrete themes/indicators and methods/sources applied within the evaluation of an OHI project at a hospital in Switzerland illustrate the model's applicability in structuring evaluations of complex OHIs. In conclusion, the model supplies a common language and a shared mental model for improving communication between researchers and company members and will improve the comparability and aggregation of evaluation study results. PMID:26557665

Red mud flocculation process in alumina production

NASA Astrophysics Data System (ADS)

Fedorova, E. R.; Firsov, A. Yu

2018-05-01

The process of thickening and washing red mud is a gooseneck of alumina production. The existing automated systems of the thickening process control involve stabilizing the parameters of the primary technological circuits of the thickener. The actual direction of scientific research is the creation and improvement of models and systems of the thickening process control by model. But the known models do not fully consider the presence of perturbing effects, in particular the particle size distribution in the feed process, distribution of floccules by size after the aggregation process in the feed barrel. The article is devoted to the basic concepts and terms used in writing the population balance algorithm. The population balance model is implemented in the MatLab environment. The result of the simulation is the particle size distribution after the flocculation process. This model allows one to foreseen the distribution range of floccules after the process of aggregation of red mud in the feed barrel. The mud of Jamaican bauxite was acting as an industrial sample of red mud; Cytec Industries of HX-3000 series with a concentration of 0.5% was acting as a flocculant. When simulating, model constants obtained in a tubular tank in the laboratories of CSIRO (Australia) were used.

Cost Models for MMC Manufacturing Processes

NASA Technical Reports Server (NTRS)

Elzey, Dana M.; Wadley, Haydn N. G.

1996-01-01

Processes for the manufacture of advanced metal matrix composites are rapidly approaching maturity in the research laboratory and there is growing interest in their transition to industrial production. However, research conducted to date has almost exclusively focused on overcoming the technical barriers to producing high-quality material and little attention has been given to the economical feasibility of these laboratory approaches and process cost issues. A quantitative cost modeling (QCM) approach was developed to address these issues. QCM are cost analysis tools based on predictive process models relating process conditions to the attributes of the final product. An important attribute, of the QCM approach is the ability to predict the sensitivity of material production costs to product quality and to quantitatively explore trade-offs between cost and quality. Applications of the cost models allow more efficient direction of future MMC process technology development and a more accurate assessment of MMC market potential. Cost models were developed for two state-of-the art metal matrix composite (MMC) manufacturing processes: tape casting and plasma spray deposition. Quality and Cost models are presented for both processes and the resulting predicted quality-cost curves are presented and discussed.

The Context, Process, and Outcome Evaluation Model for Organisational Health Interventions.

PubMed

Fridrich, Annemarie; Jenny, Gregor J; Bauer, Georg F

2015-01-01

To facilitate evaluation of complex, organisational health interventions (OHIs), this paper aims at developing a context, process, and outcome (CPO) evaluation model. It builds on previous model developments in the field and advances them by clearly defining and relating generic evaluation categories for OHIs. Context is defined as the underlying frame that influences and is influenced by an OHI. It is further differentiated into the omnibus and discrete contexts. Process is differentiated into the implementation process, as the time-limited enactment of the original intervention plan, and the change process of individual and collective dynamics triggered by the implementation process. These processes lead to proximate, intermediate, and distal outcomes, as all results of the change process that are meaningful for various stakeholders. Research questions that might guide the evaluation of an OHI according to the CPO categories and a list of concrete themes/indicators and methods/sources applied within the evaluation of an OHI project at a hospital in Switzerland illustrate the model's applicability in structuring evaluations of complex OHIs. In conclusion, the model supplies a common language and a shared mental model for improving communication between researchers and company members and will improve the comparability and aggregation of evaluation study results.

Advancing representation of hydrologic processes in the Soil and Water Assessment Tool (SWAT) through integration of the TOPographic MODEL (TOPMODEL) features

USGS Publications Warehouse

Chen, J.; Wu, Y.

2012-01-01

This paper presents a study of the integration of the Soil and Water Assessment Tool (SWAT) model and the TOPographic MODEL (TOPMODEL) features for enhancing the physical representation of hydrologic processes. In SWAT, four hydrologic processes, which are surface runoff, baseflow, groundwater re-evaporation and deep aquifer percolation, are modeled by using a group of empirical equations. The empirical equations usually constrain the simulation capability of relevant processes. To replace these equations and to model the influences of topography and water table variation on streamflow generation, the TOPMODEL features are integrated into SWAT, and a new model, the so-called SWAT-TOP, is developed. In the new model, the process of deep aquifer percolation is removed, the concept of groundwater re-evaporation is refined, and the processes of surface runoff and baseflow are remodeled. Consequently, three parameters in SWAT are discarded, and two new parameters to reflect the TOPMODEL features are introduced. SWAT-TOP and SWAT are applied to the East River basin in South China, and the results reveal that, compared with SWAT, the new model can provide a more reasonable simulation of the hydrologic processes of surface runoff, groundwater re-evaporation, and baseflow. This study evidences that an established hydrologic model can be further improved by integrating the features of another model, which is a possible way to enhance our understanding of the workings of catchments.

Modelling of Two-Stage Methane Digestion With Pretreatment of Biomass

NASA Astrophysics Data System (ADS)

Dychko, A.; Remez, N.; Opolinskyi, I.; Kraychuk, S.; Ostapchuk, N.; Yevtieieva, L.

2018-04-01

Systems of anaerobic digestion should be used for processing of organic waste. Managing the process of anaerobic recycling of organic waste requires reliable predicting of biogas production. Development of mathematical model of process of organic waste digestion allows determining the rate of biogas output at the two-stage process of anaerobic digestion considering the first stage. Verification of Konto's model, based on the studied anaerobic processing of organic waste, is implemented. The dependencies of biogas output and its rate from time are set and may be used to predict the process of anaerobic processing of organic waste.

A Software Development Simulation Model of a Spiral Process

NASA Technical Reports Server (NTRS)

Mizell, Carolyn; Malone, Linda

2007-01-01

There is a need for simulation models of software development processes other than the waterfall because processes such as spiral development are becoming more and more popular. The use of a spiral process can make the inherently difficult job of cost and schedule estimation even more challenging due to its evolutionary nature, but this allows for a more flexible process that can better meet customers' needs. This paper will present a discrete event simulation model of spiral development that can be used to analyze cost and schedule effects of using such a process in comparison to a waterfall process.

A generic biogeochemical module for Earth system models: Next Generation BioGeoChemical Module (NGBGC), version 1.0

NASA Astrophysics Data System (ADS)

Fang, Y.; Huang, M.; Liu, C.; Li, H.; Leung, L. R.

2013-11-01

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from the atmosphere, influencing global climate changes. Integration of these processes into Earth system models (e.g., community land models (CLMs)), however, currently faces three major challenges: (1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, (2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and (3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not impossible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module, Next Generation BioGeoChemical Module (NGBGC), version 1.0, with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter, and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into CLM. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems. The method presented here could in theory be applied to simulate biogeochemical cycles in other Earth system models.

The Cox proportional Hazard model on duration of birth process

NASA Astrophysics Data System (ADS)

Wuryandari, Triastuti; Haryatmi Kartiko, Sri; Danardono

2018-05-01

The duration of birth process, which is measured from the birth sign until baby born, is one important factor to the whole outcome of delivery process. There is a method of birth process that given relaxing and gentle treatment to the mother caled as gentlebirth. Gentlebirth is a method of birth process that combines brain science, birth science and technology to empower positive birth without pain. However the effect of method to the duration of birth process is still need empirical investigations. Therefore, the objective of this paper is to analyze the duration of birth process using the appropriate statistical methods for durational data, survival data or time to event data. Since there are many variables or factor that may affect the duration, a regression model is considerated. The flexibility of the Cox Proportional Hazard Model in the sense that there is no distributional assumption required, makes the Cox Model as the appropriate model and method to analyze the duration birth process. It is concluded that the Gentlebirth method affects on duration of birth process, with Hazard Ratio of 2.073, showing that the duration of birth process with gentlebirth method is faster than the other method.

Computational modeling of the pressurization process in a NASP vehicle propellant tank experimental simulation

NASA Technical Reports Server (NTRS)

Sasmal, G. P.; Hochstein, J. I.; Wendl, M. C.; Hardy, T. L.

1991-01-01

A multidimensional computational model of the pressurization process in a slush hydrogen propellant storage tank was developed and its accuracy evaluated by comparison to experimental data measured for a 5 ft diameter spherical tank. The fluid mechanic, thermodynamic, and heat transfer processes within the ullage are represented by a finite-volume model. The model was shown to be in reasonable agreement with the experiment data. A parameter study was undertaken to examine the dependence of the pressurization process on initial ullage temperature distribution and pressurant mass flow rate. It is shown that for a given heat flux rate at the ullage boundary, the pressurization process is nearly independent of initial temperature distribution. Significant differences were identified between the ullage temperature and velocity fields predicted for pressurization of slush and those predicted for pressurization of liquid hydrogen. A simplified model of the pressurization process was constructed in search of a dimensionless characterization of the pressurization process. It is shown that the relationship derived from this simplified model collapses all of the pressure history data generated during this study into a single curve.

Pain management: a review of organisation models with integrated processes for the management of pain in adult cancer patients.

PubMed

Brink-Huis, Anita; van Achterberg, Theo; Schoonhoven, Lisette

2008-08-01

This paper reports a review of the literature conducted to identify organisation models in cancer pain management that contain integrated care processes and describe their effectiveness. Pain is experienced by 30-50% of cancer patients receiving treatment and by 70-90% of those with advanced disease. Efforts to improve pain management have been made through the development and dissemination of clinical guidelines. Early improvements in pain management were focussed on just one or two single processes such as pain assessment and patient education. Little is known about organisational models with multiple integrated processes throughout the course of the disease trajectory and concerning all stages of the care process. Systematic review. The review involved a systematic search of the literature, published between 1986-2006. Subject-specific keywords used to describe patients, disease, pain management interventions and integrated care processes, relevant for this review were selected using the thesaurus of the databases. Institutional models, clinical pathways and consultation services are three alternative models for the integration of care processes in cancer pain management. A clinical pathway is a comprehensive institutionalisation model, whereas a pain consultation service is a 'stand-alone' model that can be integrated in a clinical pathway. Positive patient and process outcomes have been described for all three models, although the level of evidence is generally low. Evaluation of the quality of pain management must involve standardised measurements of both patient and process outcomes. We recommend the development of policies for referrals to a pain consultation service. These policies can be integrated within a clinical pathway. To evaluate the effectiveness of pain management models standardised outcome measures are needed.

Conceptual-level workflow modeling of scientific experiments using NMR as a case study

PubMed Central

Verdi, Kacy K; Ellis, Heidi JC; Gryk, Michael R

2007-01-01

Background Scientific workflows improve the process of scientific experiments by making computations explicit, underscoring data flow, and emphasizing the participation of humans in the process when intuition and human reasoning are required. Workflows for experiments also highlight transitions among experimental phases, allowing intermediate results to be verified and supporting the proper handling of semantic mismatches and different file formats among the various tools used in the scientific process. Thus, scientific workflows are important for the modeling and subsequent capture of bioinformatics-related data. While much research has been conducted on the implementation of scientific workflows, the initial process of actually designing and generating the workflow at the conceptual level has received little consideration. Results We propose a structured process to capture scientific workflows at the conceptual level that allows workflows to be documented efficiently, results in concise models of the workflow and more-correct workflow implementations, and provides insight into the scientific process itself. The approach uses three modeling techniques to model the structural, data flow, and control flow aspects of the workflow. The domain of biomolecular structure determination using Nuclear Magnetic Resonance spectroscopy is used to demonstrate the process. Specifically, we show the application of the approach to capture the workflow for the process of conducting biomolecular analysis using Nuclear Magnetic Resonance (NMR) spectroscopy. Conclusion Using the approach, we were able to accurately document, in a short amount of time, numerous steps in the process of conducting an experiment using NMR spectroscopy. The resulting models are correct and precise, as outside validation of the models identified only minor omissions in the models. In addition, the models provide an accurate visual description of the control flow for conducting biomolecular analysis using NMR spectroscopy experiment. PMID:17263870

Conceptual-level workflow modeling of scientific experiments using NMR as a case study.

PubMed

Verdi, Kacy K; Ellis, Heidi Jc; Gryk, Michael R

2007-01-30

Scientific workflows improve the process of scientific experiments by making computations explicit, underscoring data flow, and emphasizing the participation of humans in the process when intuition and human reasoning are required. Workflows for experiments also highlight transitions among experimental phases, allowing intermediate results to be verified and supporting the proper handling of semantic mismatches and different file formats among the various tools used in the scientific process. Thus, scientific workflows are important for the modeling and subsequent capture of bioinformatics-related data. While much research has been conducted on the implementation of scientific workflows, the initial process of actually designing and generating the workflow at the conceptual level has received little consideration. We propose a structured process to capture scientific workflows at the conceptual level that allows workflows to be documented efficiently, results in concise models of the workflow and more-correct workflow implementations, and provides insight into the scientific process itself. The approach uses three modeling techniques to model the structural, data flow, and control flow aspects of the workflow. The domain of biomolecular structure determination using Nuclear Magnetic Resonance spectroscopy is used to demonstrate the process. Specifically, we show the application of the approach to capture the workflow for the process of conducting biomolecular analysis using Nuclear Magnetic Resonance (NMR) spectroscopy. Using the approach, we were able to accurately document, in a short amount of time, numerous steps in the process of conducting an experiment using NMR spectroscopy. The resulting models are correct and precise, as outside validation of the models identified only minor omissions in the models. In addition, the models provide an accurate visual description of the control flow for conducting biomolecular analysis using NMR spectroscopy experiment.

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Graphical Technique to Support the Teaching/Learning Process of Software Process Reference Models

NASA Astrophysics Data System (ADS)

Espinosa-Curiel, Ismael Edrein; Rodríguez-Jacobo, Josefina; Fernández-Zepeda, José Alberto

In this paper, we propose a set of diagrams to visualize software process reference models (PRM). The diagrams, called dimods, are the combination of some visual and process modeling techniques such as rich pictures, mind maps, IDEF and RAD diagrams. We show the use of this technique by designing a set of dimods for the Mexican Software Industry Process Model (MoProSoft). Additionally, we perform an evaluation of the usefulness of dimods. The result of the evaluation shows that dimods may be a support tool that facilitates the understanding, memorization, and learning of software PRMs in both, software development organizations and universities. The results also show that dimods may have advantages over the traditional description methods for these types of models.

Launch Site Computer Simulation and its Application to Processes

NASA Technical Reports Server (NTRS)

Sham, Michael D.

1995-01-01

This paper provides an overview of computer simulation, the Lockheed developed STS Processing Model, and the application of computer simulation to a wide range of processes. The STS Processing Model is an icon driven model that uses commercial off the shelf software and a Macintosh personal computer. While it usually takes one year to process and launch 8 space shuttles, with the STS Processing Model this process is computer simulated in about 5 minutes. Facilities, orbiters, or ground support equipment can be added or deleted and the impact on launch rate, facility utilization, or other factors measured as desired. This same computer simulation technology can be used to simulate manufacturing, engineering, commercial, or business processes. The technology does not require an 'army' of software engineers to develop and operate, but instead can be used by the layman with only a minimal amount of training. Instead of making changes to a process and realizing the results after the fact, with computer simulation, changes can be made and processes perfected before they are implemented.

Enabling model checking for collaborative process analysis: from BPMN to `Network of Timed Automata'

NASA Astrophysics Data System (ADS)

Mallek, Sihem; Daclin, Nicolas; Chapurlat, Vincent; Vallespir, Bruno

2015-04-01

Interoperability is a prerequisite for partners involved in performing collaboration. As a consequence, the lack of interoperability is now considered a major obstacle. The research work presented in this paper aims to develop an approach that allows specifying and verifying a set of interoperability requirements to be satisfied by each partner in the collaborative process prior to process implementation. To enable the verification of these interoperability requirements, it is necessary first and foremost to generate a model of the targeted collaborative process; for this research effort, the standardised language BPMN 2.0 is used. Afterwards, a verification technique must be introduced, and model checking is the preferred option herein. This paper focuses on application of the model checker UPPAAL in order to verify interoperability requirements for the given collaborative process model. At first, this step entails translating the collaborative process model from BPMN into a UPPAAL modelling language called 'Network of Timed Automata'. Second, it becomes necessary to formalise interoperability requirements into properties with the dedicated UPPAAL language, i.e. the temporal logic TCTL.

Analysis and modeling of wafer-level process variability in 28 nm FD-SOI using split C-V measurements

NASA Astrophysics Data System (ADS)

Pradeep, Krishna; Poiroux, Thierry; Scheer, Patrick; Juge, André; Gouget, Gilles; Ghibaudo, Gérard

2018-07-01

This work details the analysis of wafer level global process variability in 28 nm FD-SOI using split C-V measurements. The proposed approach initially evaluates the native on wafer process variability using efficient extraction methods on split C-V measurements. The on-wafer threshold voltage (VT) variability is first studied and modeled using a simple analytical model. Then, a statistical model based on the Leti-UTSOI compact model is proposed to describe the total C-V variability in different bias conditions. This statistical model is finally used to study the contribution of each process parameter to the total C-V variability.

Cascade process modeling with mechanism-based hierarchical neural networks.

PubMed

Cong, Qiumei; Yu, Wen; Chai, Tianyou

2010-02-01

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

Processing (Non)Compositional Expressions: Mistakes and Recovery

ERIC Educational Resources Information Center

Holsinger, Edward; Kaiser, Elsi

2013-01-01

Current models of idiom representation and processing differ with respect to the role of literal processing during the interpretation of idiomatic expressions. Word-like models (Bobrow & Bell, 1973; Swinney & Cutler, 1979) propose that idiomatic meaning can be accessed directly, whereas structural models (Cacciari & Tabossi, 1988;…

77 FR 61307 - New Postal Product

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-09

...: Transfer Mail Processing Cost Model for Machinable and Irregular Standard Mail Parcels to the Mail Processing Cost Model for Parcel Select/Parcel Return Service. The Postal Service proposes to move the machinable and irregular cost worksheets contained in the Standard Mail parcel mail processing cost model to...

An Extended Petri-Net Based Approach for Supply Chain Process Enactment in Resource-Centric Web Service Environment

NASA Astrophysics Data System (ADS)

Wang, Xiaodong; Zhang, Xiaoyu; Cai, Hongming; Xu, Boyi

Enacting a supply-chain process involves variant partners and different IT systems. REST receives increasing attention for distributed systems with loosely coupled resources. Nevertheless, resource model incompatibilities and conflicts prevent effective process modeling and deployment in resource-centric Web service environment. In this paper, a Petri-net based framework for supply-chain process integration is proposed. A resource meta-model is constructed to represent the basic information of resources. Then based on resource meta-model, XML schemas and documents are derived, which represent resources and their states in Petri-net. Thereafter, XML-net, a high level Petri-net, is employed for modeling control and data flow of process. From process model in XML-net, RESTful services and choreography descriptions are deduced. Therefore, unified resource representation and RESTful services description are proposed for cross-system integration in a more effective way. A case study is given to illustrate the approach and the desirable features of the approach are discussed.

Modelling tidewater glacier calving: from detailed process models to simple calving laws

NASA Astrophysics Data System (ADS)

Benn, Doug; Åström, Jan; Zwinger, Thomas; Todd, Joe; Nick, Faezeh

2017-04-01

The simple calving laws currently used in ice sheet models do not adequately reflect the complexity and diversity of calving processes. To be effective, calving laws must be grounded in a sound understanding of how calving actually works. We have developed a new approach to formulating calving laws, using a) the Helsinki Discrete Element Model (HiDEM) to explicitly model fracture and calving processes, and b) the full-Stokes continuum model Elmer/Ice to identify critical stress states associated with HiDEM calving events. A range of observed calving processes emerges spontaneously from HiDEM in response to variations in ice-front buoyancy and the size of subaqueous undercuts, and we show that HiDEM calving events are associated with characteristic stress patterns simulated in Elmer/Ice. Our results open the way to developing calving laws that properly reflect the diversity of calving processes, and provide a framework for a unified theory of the calving process continuum.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race.

PubMed

Warnke, Tom; Reinhardt, Oliver; Klabunde, Anna; Willekens, Frans; Uhrmacher, Adelinde M

2017-10-01

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

Computational Modeling in Structural Materials Processing

NASA Technical Reports Server (NTRS)

Meyyappan, Meyya; Arnold, James O. (Technical Monitor)

1997-01-01

High temperature materials such as silicon carbide, a variety of nitrides, and ceramic matrix composites find use in aerospace, automotive, machine tool industries and in high speed civil transport applications. Chemical vapor deposition (CVD) is widely used in processing such structural materials. Variations of CVD include deposition on substrates, coating of fibers, inside cavities and on complex objects, and infiltration within preforms called chemical vapor infiltration (CVI). Our current knowledge of the process mechanisms, ability to optimize processes, and scale-up for large scale manufacturing is limited. In this regard, computational modeling of the processes is valuable since a validated model can be used as a design tool. The effort is similar to traditional chemically reacting flow modeling with emphasis on multicomponent diffusion, thermal diffusion, large sets of homogeneous reactions, and surface chemistry. In the case of CVI, models for pore infiltration are needed. In the present talk, examples of SiC nitride, and Boron deposition from the author's past work will be used to illustrate the utility of computational process modeling.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Experimental research on mathematical modelling and unconventional control of clinker kiln in cement plants

NASA Astrophysics Data System (ADS)

Rusu-Anghel, S.

2017-01-01

Analytical modeling of the flow of manufacturing process of the cement is difficult because of their complexity and has not resulted in sufficiently precise mathematical models. In this paper, based on a statistical model of the process and using the knowledge of human experts, was designed a fuzzy system for automatic control of clinkering process.

Multiphase porous media modelling: A novel approach to predicting food processing performance.

PubMed

Khan, Md Imran H; Joardder, M U H; Kumar, Chandan; Karim, M A

2018-03-04

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Granularity as a Cognitive Factor in the Effectiveness of Business Process Model Reuse

NASA Astrophysics Data System (ADS)

Holschke, Oliver; Rake, Jannis; Levina, Olga

Reusing design models is an attractive approach in business process modeling as modeling efficiency and quality of design outcomes may be significantly improved. However, reusing conceptual models is not a cost-free effort, but has to be carefully designed. While factors such as psychological anchoring and task-adequacy in reuse-based modeling tasks have been investigated, information granularity as a cognitive concept has not been at the center of empirical research yet. We hypothesize that business process granularity as a factor in design tasks under reuse has a significant impact on the effectiveness of resulting business process models. We test our hypothesis in a comparative study employing high and low granularities. The reusable processes provided were taken from widely accessible reference models for the telecommunication industry (enhanced Telecom Operations Map). First experimental results show that Recall in tasks involving coarser granularity is lower than in cases of finer granularity. These findings suggest that decision makers in business process management should be considerate with regard to the implementation of reuse mechanisms of different granularities. We realize that due to our small sample size results are not statistically significant, but this preliminary run shows that it is ready for running on a larger scale.

A functional-dynamic reflection on participatory processes in modeling projects.

PubMed

Seidl, Roman

2015-12-01

The participation of nonscientists in modeling projects/studies is increasingly employed to fulfill different functions. However, it is not well investigated if and how explicitly these functions and the dynamics of a participatory process are reflected by modeling projects in particular. In this review study, I explore participatory modeling projects from a functional-dynamic process perspective. The main differences among projects relate to the functions of participation-most often, more than one per project can be identified, along with the degree of explicit reflection (i.e., awareness and anticipation) on the dynamic process perspective. Moreover, two main approaches are revealed: participatory modeling covering diverse approaches and companion modeling. It becomes apparent that the degree of reflection on the participatory process itself is not always explicit and perfectly visible in the descriptions of the modeling projects. Thus, the use of common protocols or templates is discussed to facilitate project planning, as well as the publication of project results. A generic template may help, not in providing details of a project or model development, but in explicitly reflecting on the participatory process. It can serve to systematize the particular project's approach to stakeholder collaboration, and thus quality management.

Thermal analysis of fused deposition modeling process using infrared thermography imaging and finite element modeling

NASA Astrophysics Data System (ADS)

Zhou, Xunfei; Hsieh, Sheng-Jen

2017-05-01

After years of development, Fused Deposition Modeling (FDM) has become the most popular technique in commercial 3D printing due to its cost effectiveness and easy-to-operate fabrication process. Mechanical strength and dimensional accuracy are two of the most important factors for reliability of FDM products. However, the solid-liquid-solid state changes of material in the FDM process make it difficult to monitor and model. In this paper, an experimental model was developed to apply cost-effective infrared thermography imaging method to acquire temperature history of filaments at the interface and their corresponding cooling mechanism. A three-dimensional finite element model was constructed to simulate the same process using element "birth and death" feature and validated with the thermal response from the experimental model. In 6 of 9 experimental conditions, a maximum of 13% difference existed between the experimental and numerical models. This work suggests that numerical modeling of FDM process is reliable and can facilitate better understanding of bead spreading and road-to-road bonding mechanics during fabrication.

Does lake size matter? Combining morphology and process modeling to examine the contribution of lake classes to population-scale processes

USGS Publications Warehouse

Winslow, Luke A.; Read, Jordan S.; Hanson, Paul C.; Stanley, Emily H.

2014-01-01

With lake abundances in the thousands to millions, creating an intuitive understanding of the distribution of morphology and processes in lakes is challenging. To improve researchers’ understanding of large-scale lake processes, we developed a parsimonious mathematical model based on the Pareto distribution to describe the distribution of lake morphology (area, perimeter and volume). While debate continues over which mathematical representation best fits any one distribution of lake morphometric characteristics, we recognize the need for a simple, flexible model to advance understanding of how the interaction between morphometry and function dictates scaling across large populations of lakes. These models make clear the relative contribution of lakes to the total amount of lake surface area, volume, and perimeter. They also highlight the critical thresholds at which total perimeter, area and volume would be evenly distributed across lake size-classes have Pareto slopes of 0.63, 1 and 1.12, respectively. These models of morphology can be used in combination with models of process to create overarching “lake population” level models of process. To illustrate this potential, we combine the model of surface area distribution with a model of carbon mass accumulation rate. We found that even if smaller lakes contribute relatively less to total surface area than larger lakes, the increasing carbon accumulation rate with decreasing lake size is strong enough to bias the distribution of carbon mass accumulation towards smaller lakes. This analytical framework provides a relatively simple approach to upscaling morphology and process that is easily generalizable to other ecosystem processes.

Statistical and engineering methods for model enhancement

NASA Astrophysics Data System (ADS)

Chang, Chia-Jung

Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as “Minimal Adjustment”, which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

Model-Driven Useware Engineering

NASA Astrophysics Data System (ADS)

Meixner, Gerrit; Seissler, Marc; Breiner, Kai

User-oriented hardware and software development relies on a systematic development process based on a comprehensive analysis focusing on the users' requirements and preferences. Such a development process calls for the integration of numerous disciplines, from psychology and ergonomics to computer sciences and mechanical engineering. Hence, a correspondingly interdisciplinary team must be equipped with suitable software tools to allow it to handle the complexity of a multimodal and multi-device user interface development approach. An abstract, model-based development approach seems to be adequate for handling this complexity. This approach comprises different levels of abstraction requiring adequate tool support. Thus, in this chapter, we present the current state of our model-based software tool chain. We introduce the use model as the core model of our model-based process, transformation processes, and a model-based architecture, and we present different software tools that provide support for creating and maintaining the models or performing the necessary model transformations.

One-dimensional biomass fast pyrolysis model with reaction kinetics integrated in an Aspen Plus Biorefinery Process Model

DOE PAGES

Humbird, David; Trendewicz, Anna; Braun, Robert; ...

2017-01-12

A biomass fast pyrolysis reactor model with detailed reaction kinetics and one-dimensional fluid dynamics was implemented in an equation-oriented modeling environment (Aspen Custom Modeler). Portions of this work were detailed in previous publications; further modifications have been made here to improve stability and reduce execution time of the model to make it compatible for use in large process flowsheets. The detailed reactor model was integrated into a larger process simulation in Aspen Plus and was stable for different feedstocks over a range of reactor temperatures. Sample results are presented that indicate general agreement with experimental results, but with higher gasmore » losses caused by stripping of the bio-oil by the fluidizing gas in the simulated absorber/condenser. Lastly, this integrated modeling approach can be extended to other well-defined, predictive reactor models for fast pyrolysis, catalytic fast pyrolysis, as well as other processes.« less

One-dimensional biomass fast pyrolysis model with reaction kinetics integrated in an Aspen Plus Biorefinery Process Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humbird, David; Trendewicz, Anna; Braun, Robert

A biomass fast pyrolysis reactor model with detailed reaction kinetics and one-dimensional fluid dynamics was implemented in an equation-oriented modeling environment (Aspen Custom Modeler). Portions of this work were detailed in previous publications; further modifications have been made here to improve stability and reduce execution time of the model to make it compatible for use in large process flowsheets. The detailed reactor model was integrated into a larger process simulation in Aspen Plus and was stable for different feedstocks over a range of reactor temperatures. Sample results are presented that indicate general agreement with experimental results, but with higher gasmore » losses caused by stripping of the bio-oil by the fluidizing gas in the simulated absorber/condenser. Lastly, this integrated modeling approach can be extended to other well-defined, predictive reactor models for fast pyrolysis, catalytic fast pyrolysis, as well as other processes.« less

Elementary teachers' mental models of engineering design processes: A comparison of two communities of practice

NASA Astrophysics Data System (ADS)

McMahon, Ann P.

Educating K-12 students in the processes of design engineering is gaining popularity in public schools. Several states have adopted standards for engineering design despite the fact that no common agreement exists on what should be included in the K-12 engineering design process. Furthermore, little pre-service and in-service professional development exists that will prepare teachers to teach a design process that is fundamentally different from the science teaching process found in typical public schools. This study provides a glimpse into what teachers think happens in engineering design compared to articulated best practices in engineering design. Wenger's communities of practice work and van Dijk's multidisciplinary theory of mental models provide the theoretical bases for comparing the mental models of two groups of elementary teachers (one group that teaches engineering and one that does not) to the mental models of design engineers (including this engineer/researcher/educator and professionals described elsewhere). The elementary school teachers and this engineer/researcher/educator observed the design engineering process enacted by professionals, then answered questions designed to elicit their mental models of the process they saw in terms of how they would teach it to elementary students. The key finding is this: Both groups of teachers embedded the cognitive steps of the design process into the matrix of the social and emotional roles and skills of students. Conversely, the engineers embedded the social and emotional aspects of the design process into the matrix of the cognitive steps of the design process. In other words, teachers' mental models show that they perceive that students' social and emotional communicative roles and skills in the classroom drive their cognitive understandings of the engineering process, while the mental models of this engineer/researcher/educator and the engineers in the video show that we perceive that cognitive understandings of the engineering process drive the social and emotional roles and skills used in that process. This comparison of mental models with the process that professional designers use defines a problem space for future studies that investigate how to incorporate engineering practices into elementary classrooms. Recommendations for engineering curriculum development and teacher professional development based on this study are presented.

SEIPS-based process modeling in primary care.

PubMed

Wooldridge, Abigail R; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter L T

2017-04-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. Copyright © 2016 Elsevier Ltd. All rights reserved.

SEIPS-Based Process Modeling in Primary Care

PubMed Central

Wooldridge, Abigail R.; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter

2016-01-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. PMID:28166883

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2016-04-01

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integratemore » expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.« less

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

PubMed

Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

2017-11-01

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

Meta-control of combustion performance with a data mining approach

NASA Astrophysics Data System (ADS)

Song, Zhe

Large scale combustion process is complex and proposes challenges of optimizing its performance. Traditional approaches based on thermal dynamics have limitations on finding optimal operational regions due to time-shift nature of the process. Recent advances in information technology enable people collect large volumes of process data easily and continuously. The collected process data contains rich information about the process and, to some extent, represents a digital copy of the process over time. Although large volumes of data exist in industrial combustion processes, they are not fully utilized to the level where the process can be optimized. Data mining is an emerging science which finds patterns or models from large data sets. It has found many successful applications in business marketing, medical and manufacturing domains The focus of this dissertation is on applying data mining to industrial combustion processes, and ultimately optimizing the combustion performance. However the philosophy, methods and frameworks discussed in this research can also be applied to other industrial processes. Optimizing an industrial combustion process has two major challenges. One is the underlying process model changes over time and obtaining an accurate process model is nontrivial. The other is that a process model with high fidelity is usually highly nonlinear, solving the optimization problem needs efficient heuristics. This dissertation is set to solve these two major challenges. The major contribution of this 4-year research is the data-driven solution to optimize the combustion process, where process model or knowledge is identified based on the process data, then optimization is executed by evolutionary algorithms to search for optimal operating regions.

Process Optimization of Dual-Laser Beam Welding of Advanced Al-Li Alloys Through Hot Cracking Susceptibility Modeling

NASA Astrophysics Data System (ADS)

Tian, Yingtao; Robson, Joseph D.; Riekehr, Stefan; Kashaev, Nikolai; Wang, Li; Lowe, Tristan; Karanika, Alexandra

2016-07-01

Laser welding of advanced Al-Li alloys has been developed to meet the increasing demand for light-weight and high-strength aerospace structures. However, welding of high-strength Al-Li alloys can be problematic due to the tendency for hot cracking. Finding suitable welding parameters and filler material for this combination currently requires extensive and costly trial and error experimentation. The present work describes a novel coupled model to predict hot crack susceptibility (HCS) in Al-Li welds. Such a model can be used to shortcut the weld development process. The coupled model combines finite element process simulation with a two-level HCS model. The finite element process model predicts thermal field data for the subsequent HCS hot cracking prediction. The model can be used to predict the influences of filler wire composition and welding parameters on HCS. The modeling results have been validated by comparing predictions with results from fully instrumented laser welds performed under a range of process parameters and analyzed using high-resolution X-ray tomography to identify weld defects. It is shown that the model is capable of accurately predicting the thermal field around the weld and the trend of HCS as a function of process parameters.

The Algebra of Sleepiness: Investigating the Interaction of Homeostatic (S) and Circadian (C) Processes in Sleepiness Using Linear Metrics"

ERIC Educational Resources Information Center

Mairesse, Olivier; Hofmans, Joeri; Neu, Daniel; Dinis Monica de Oliveira, Armando Luis; Cluydts, Raymond; Theuns, Peter

2010-01-01

The present studies were conducted to contribute to the debate on the interaction between circadian (C) and homeostatic (S) processes in models of sleep regulation. The Two-Process Model of Sleep Regulation assumes a linear relationship between processes S and C. However, recent elaborations of the model, based on data from forced desynchrony…

Operator Performance Measures for Assessing Voice Communication Effectiveness

DTIC Science & Technology

1989-07-01

performance and work- load assessment techniques have been based.I Broadbent (1958) described a limited capacity filter model of human information...INFORMATION PROCESSING 20 3.1.1. Auditory Attention 20 3.1.2. Auditory Memory 24 3.2. MODELS OF INFORMATION PROCESSING 24 3.2.1. Capacity Theories 25...Learning 0 Attention * Language Specialization • Decision Making• Problem Solving Auditory Information Processing Models of Processing Ooemtor

[The dual process model of addiction. Towards an integrated model?].

PubMed

Vandermeeren, R; Hebbrecht, M

2012-01-01

Neurobiology and cognitive psychology have provided us with a dual process model of addiction. According to this model, behavior is considered to be the dynamic result of a combination of automatic and controlling processes. In cases of addiction the balance between these two processes is severely disturbed. Automated processes will continue to produce impulses that ensure the continuance of addictive behavior. Weak, reflective or controlling processes are both the reason for and the result of the inability to forgo addiction. To identify features that are common to current neurocognitive insights into addiction and psychodynamic views on addiction. The picture that emerges from research is not clear. There is some evidence that attentional bias has a causal effect on addiction. There is no evidence that automatic associations have a causal effect, but there is some evidence that automatic action-tendencies do have a causal effect. Current neurocognitive views on the dual process model of addiction can be integrated with an evidence-based approach to addiction and with psychodynamic views on addiction.

From conceptual modeling to a map

NASA Astrophysics Data System (ADS)

Gotlib, Dariusz; Olszewski, Robert

2018-05-01

Nowadays almost every map is a component of the information system. Design and production of maps requires the use of specific rules for modeling information systems: conceptual, application and data modelling. While analyzing various stages of cartographic modeling the authors ask the question: at what stage of this process a map occurs. Can we say that the "life of the map" begins even before someone define its form of presentation? This question is particularly important at the time of exponentially increasing number of new geoinformation products. During the analysis of the theory of cartography and relations of the discipline to other fields of knowledge it has been attempted to define a few properties of cartographic modeling which distinguish the process from other methods of spatial modeling. Assuming that the map is a model of reality (created in the process of cartographic modeling supported by domain-modeling) the article proposes an analogy of the process of cartographic modeling to the scheme of conceptual modeling presented in ISO 19101 standard.

Branching processes in disease epidemics

NASA Astrophysics Data System (ADS)

Singh, Sarabjeet

Branching processes have served as a model for chemical reactions, biological growth processes and contagion (of disease, information or fads). Through this connection, these seemingly different physical processes share some common universalities that can be elucidated by analyzing the underlying branching process. In this thesis, we focus on branching processes as a model for infectious diseases spreading between individuals belonging to different populations. The distinction between populations can arise from species separation (as in the case of diseases which jump across species) or spatial separation (as in the case of disease spreading between farms, cities, urban centers, etc). A prominent example of the former is zoonoses -- infectious diseases that spill from animals to humans -- whose specific examples include Nipah virus, monkeypox, HIV and avian influenza. A prominent example of the latter is infectious diseases of animals such as foot and mouth disease and bovine tuberculosis that spread between farms or cattle herds. Another example of the latter is infectious diseases of humans such as H1N1 that spread from one city to another through migration of infectious hosts. This thesis consists of three main chapters, an introduction and an appendix. The introduction gives a brief history of mathematics in modeling the spread of infectious diseases along with a detailed description of the most commonly used disease model -- the Susceptible-Infectious-Recovered (SIR) model. The introduction also describes how the stochastic formulation of the model reduces to a branching process in the limit of large population which is analyzed in detail. The second chapter describes a two species model of zoonoses with coupled SIR processes and proceeds into the calculation of statistics pertinent to cross species infection using multitype branching processes. The third chapter describes an SIR process driven by a Poisson process of infection spillovers. This is posed as a model of infectious diseases where a `reservoir' of infection exists that infects a susceptible host population at a constant rate. The final chapter of the thesis describes a general framework of modeling infectious diseases in a network of populations using multitype branching processes.

A discrimination-association model for decomposing component processes of the implicit association test.

PubMed

Stefanutti, Luca; Robusto, Egidio; Vianello, Michelangelo; Anselmi, Pasquale

2013-06-01

A formal model is proposed that decomposes the implicit association test (IAT) effect into three process components: stimuli discrimination, automatic association, and termination criterion. Both response accuracy and reaction time are considered. Four independent and parallel Poisson processes, one for each of the four label categories of the IAT, are assumed. The model parameters are the rate at which information accrues on the counter of each process and the amount of information that is needed before a response is given. The aim of this study is to present the model and an illustrative application in which the process components of a Coca-Pepsi IAT are decomposed.

Linguistic Ambiguity in a Connectionist Model for Grammatical Studies.

ERIC Educational Resources Information Center

Angelica, Julia; Ney, James W.

1995-01-01

Discusses the evolution of the connectionist model of language processing, focusing on the parallel distributed processing (PDP) model proposed by Rumelhart and others (1986) that explains the microstructure of cognition in terms of interactive activation between elementary input, output, and intermediate processing units linked by weighted…

Parent Management Training-Oregon Model (PMTO™) in Mexico City: Integrating Cultural Adaptation Activities in an Implementation Model

PubMed Central

Baumann, Ana A.; Domenech Rodríguez, Melanie M.; Amador, Nancy G.; Forgatch, Marion S.; Parra-Cardona, J. Rubén

2015-01-01

This article describes the process of cultural adaptation at the start of the implementation of the Parent Management Training intervention-Oregon model (PMTO) in Mexico City. The implementation process was guided by the model, and the cultural adaptation of PMTO was theoretically guided by the cultural adaptation process (CAP) model. During the process of the adaptation, we uncovered the potential for the CAP to be embedded in the implementation process, taking into account broader training and economic challenges and opportunities. We discuss how cultural adaptation and implementation processes are inextricably linked and iterative and how maintaining a collaborative relationship with the treatment developer has guided our work and has helped expand our research efforts, and how building human capital to implement PMTO in Mexico supported the implementation efforts of PMTO in other places in the United States. PMID:26052184

Advances in multi-scale modeling of solidification and casting processes

NASA Astrophysics Data System (ADS)

Liu, Baicheng; Xu, Qingyan; Jing, Tao; Shen, Houfa; Han, Zhiqiang

2011-04-01

The development of the aviation, energy and automobile industries requires an advanced integrated product/process R&D systems which could optimize the product and the process design as well. Integrated computational materials engineering (ICME) is a promising approach to fulfill this requirement and make the product and process development efficient, economic, and environmentally friendly. Advances in multi-scale modeling of solidification and casting processes, including mathematical models as well as engineering applications are presented in the paper. Dendrite morphology of magnesium and aluminum alloy of solidification process by using phase field and cellular automaton methods, mathematical models of segregation of large steel ingot, and microstructure models of unidirectionally solidified turbine blade casting are studied and discussed. In addition, some engineering case studies, including microstructure simulation of aluminum casting for automobile industry, segregation of large steel ingot for energy industry, and microstructure simulation of unidirectionally solidified turbine blade castings for aviation industry are discussed.

Preform Characterization in VARTM Process Model Development

NASA Technical Reports Server (NTRS)

Grimsley, Brian W.; Cano, Roberto J.; Hubert, Pascal; Loos, Alfred C.; Kellen, Charles B.; Jensen, Brian J.

2004-01-01

Vacuum-Assisted Resin Transfer Molding (VARTM) is a Liquid Composite Molding (LCM) process where both resin injection and fiber compaction are achieved under pressures of 101.3 kPa or less. Originally developed over a decade ago for marine composite fabrication, VARTM is now considered a viable process for the fabrication of aerospace composites (1,2). In order to optimize and further improve the process, a finite element analysis (FEA) process model is being developed to include the coupled phenomenon of resin flow, preform compaction and resin cure. The model input parameters are obtained from resin and fiber-preform characterization tests. In this study, the compaction behavior and the Darcy permeability of a commercially available carbon fabric are characterized. The resulting empirical model equations are input to the 3- Dimensional Infiltration, version 5 (3DINFILv.5) process model to simulate infiltration of a composite panel.

Parent Management Training-Oregon Model (PMTO™) in Mexico City: Integrating Cultural Adaptation Activities in an Implementation Model.

PubMed

Baumann, Ana A; Domenech Rodríguez, Melanie M; Amador, Nancy G; Forgatch, Marion S; Parra-Cardona, J Rubén

2014-03-01

This article describes the process of cultural adaptation at the start of the implementation of the Parent Management Training intervention-Oregon model (PMTO) in Mexico City. The implementation process was guided by the model, and the cultural adaptation of PMTO was theoretically guided by the cultural adaptation process (CAP) model. During the process of the adaptation, we uncovered the potential for the CAP to be embedded in the implementation process, taking into account broader training and economic challenges and opportunities. We discuss how cultural adaptation and implementation processes are inextricably linked and iterative and how maintaining a collaborative relationship with the treatment developer has guided our work and has helped expand our research efforts, and how building human capital to implement PMTO in Mexico supported the implementation efforts of PMTO in other places in the United States.

Modeling stroke rehabilitation processes using the Unified Modeling Language (UML).

PubMed

Ferrante, Simona; Bonacina, Stefano; Pinciroli, Francesco

2013-10-01

In organising and providing rehabilitation procedures for stroke patients, the usual need for many refinements makes it inappropriate to attempt rigid standardisation, but greater detail is required concerning workflow. The aim of this study was to build a model of the post-stroke rehabilitation process. The model, implemented in the Unified Modeling Language, was grounded on international guidelines and refined following the clinical pathway adopted at local level by a specialized rehabilitation centre. The model describes the organisation of the rehabilitation delivery and it facilitates the monitoring of recovery during the process. Indeed, a system software was developed and tested to support clinicians in the digital administration of clinical scales. The model flexibility assures easy updating after process evolution. Copyright © 2013 Elsevier Ltd. All rights reserved.

A new approach for handling longitudinal count data with zero-inflation and overdispersion: poisson geometric process model.

PubMed

Wan, Wai-Yin; Chan, Jennifer S K

2009-08-01

For time series of count data, correlated measurements, clustering as well as excessive zeros occur simultaneously in biomedical applications. Ignoring such effects might contribute to misleading treatment outcomes. A generalized mixture Poisson geometric process (GMPGP) model and a zero-altered mixture Poisson geometric process (ZMPGP) model are developed from the geometric process model, which was originally developed for modelling positive continuous data and was extended to handle count data. These models are motivated by evaluating the trend development of new tumour counts for bladder cancer patients as well as by identifying useful covariates which affect the count level. The models are implemented using Bayesian method with Markov chain Monte Carlo (MCMC) algorithms and are assessed using deviance information criterion (DIC).

Implementation of the nursing process in a health area: models and assessment structures used

PubMed Central

Huitzi-Egilegor, Joseba Xabier; Elorza-Puyadena, Maria Isabel; Urkia-Etxabe, Jose Maria; Asurabarrena-Iraola, Carmen

2014-01-01

OBJECTIVE: to analyze what nursing models and nursing assessment structures have been used in the implementation of the nursing process at the public and private centers in the health area Gipuzkoa (Basque Country). METHOD: a retrospective study was undertaken, based on the analysis of the nursing records used at the 158 centers studied. RESULTS: the Henderson model, Carpenito's bifocal structure, Gordon's assessment structure and the Resident Assessment Instrument Nursing Home 2.0 have been used as nursing models and assessment structures to implement the nursing process. At some centers, the selected model or assessment structure has varied over time. CONCLUSION: Henderson's model has been the most used to implement the nursing process. Furthermore, the trend is observed to complement or replace Henderson's model by nursing assessment structures. PMID:25493672

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

2010-01-01

The purpose of this paper is to present the model development process used to create a Functional Fault Model (FFM) of a liquid hydrogen (L H2) system that will be used for realtime fault isolation in a Fault Detection, Isolation and Recover (FDIR) system. The paper explains th e steps in the model development process and the data products required at each step, including examples of how the steps were performed fo r the LH2 system. It also shows the relationship between the FDIR req uirements and steps in the model development process. The paper concl udes with a description of a demonstration of the LH2 model developed using the process and future steps for integrating the model in a live operational environment.

A Decision Tool that Combines Discrete Event Software Process Models with System Dynamics Pieces for Software Development Cost Estimation and Analysis

NASA Technical Reports Server (NTRS)

Mizell, Carolyn Barrett; Malone, Linda

2007-01-01

The development process for a large software development project is very complex and dependent on many variables that are dynamic and interrelated. Factors such as size, productivity and defect injection rates will have substantial impact on the project in terms of cost and schedule. These factors can be affected by the intricacies of the process itself as well as human behavior because the process is very labor intensive. The complex nature of the development process can be investigated with software development process models that utilize discrete event simulation to analyze the effects of process changes. The organizational environment and its effects on the workforce can be analyzed with system dynamics that utilizes continuous simulation. Each has unique strengths and the benefits of both types can be exploited by combining a system dynamics model and a discrete event process model. This paper will demonstrate how the two types of models can be combined to investigate the impacts of human resource interactions on productivity and ultimately on cost and schedule.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, L.

ITN Energy Systems, Inc., and Global Solar Energy, Inc., with the assistance of NREL's PV Manufacturing R&D program, have continued the advancement of CIGS production technology through the development of trajectory-oriented predictive/control models, fault-tolerance control, control-platform development, in-situ sensors, and process improvements. Modeling activities to date include the development of physics-based and empirical models for CIGS and sputter-deposition processing, implementation of model-based control, and application of predictive models to the construction of new evaporation sources and for control. Model-based control is enabled through implementation of reduced or empirical models into a control platform. Reliability improvement activities include implementation of preventivemore » maintenance schedules; detection of failed sensors/equipment and reconfiguration to continue processing; and systematic development of fault prevention and reconfiguration strategies for the full range of CIGS PV production deposition processes. In-situ sensor development activities have resulted in improved control and indicated the potential for enhanced process status monitoring and control of the deposition processes. Substantial process improvements have been made, including significant improvement in CIGS uniformity, thickness control, efficiency, yield, and throughput. In large measure, these gains have been driven by process optimization, which, in turn, have been enabled by control and reliability improvements due to this PV Manufacturing R&D program. This has resulted in substantial improvements of flexible CIGS PV module performance and efficiency.« less

Growing up and Role Modeling: A Theory in Iranian Nursing Students’ Education

PubMed Central

Nouri, Jamileh Mokhtari; Ebadi, Abbas; Alhani, Fatemeh; Rejeh, Nahid

2015-01-01

One of the key strategies in students’ learning is being affected by models. Understanding the role-modeling process in education will help to make greater use of this training strategy. The aim of this grounded theory study was to explore Iranian nursing students and instructors’ experiences about role modeling process. Data was analyzed by Glaserian’s Grounded Theory methodology through semi-structured interviews with 7 faculty members, 2 nursing students; the three focus group discussions with 20 nursing students based on purposive and theoretical sampling was done for explaining role modeling process from four nursing faculties in Tehran. Through basic coding, an effort to comprehensive growth and excellence was made with the basic social process consisting the core category and through selective coding three phases were identified as: realizing and exposure to inadequate human and professional growth, facilitating human and professional growth and evolution. The role modeling process is taking place unconscious, involuntary, dynamic and with positive progressive process in order to facilitate overall growth in nursing student. Accordingly, the design and implementation of the designed model can be used to make this unconscious to conscious, active and voluntarily processes a process to help education administrators of nursing colleges and supra organization to prevent threats to human and professional in nursing students’ education and promote nursing students’ growth. PMID:25716391

The drift diffusion model as the choice rule in reinforcement learning.

PubMed

Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

2017-08-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

The drift diffusion model as the choice rule in reinforcement learning

PubMed Central

Frank, Michael J.

2017-01-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups. PMID:27966103

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei--Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2010-01-01

In recent years, exponentially increasing computer power extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale models can be run in grid size similar to cloud resolving models through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model). (2) a regional scale model (a NASA unified weather research and forecast, W8F). (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling systems to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use the multi-satellite simulator to improve precipitation processes will be discussed.

Using Multi-Scale Modeling Systems to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2010-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.

The human body metabolism process mathematical simulation based on Lotka-Volterra model

NASA Astrophysics Data System (ADS)

Oliynyk, Andriy; Oliynyk, Eugene; Pyptiuk, Olexandr; DzierŻak, RóŻa; Szatkowska, Małgorzata; Uvaysova, Svetlana; Kozbekova, Ainur

2017-08-01

The mathematical model of metabolism process in human organism based on Lotka-Volterra model has beeng proposed, considering healing regime, nutrition system, features of insulin and sugar fragmentation process in the organism. The numerical algorithm of the model using IV-order Runge-Kutta method has been realized. After the result of calculations the conclusions have been made, recommendations about using the modeling results have been showed, the vectors of the following researches are defined.

Kinetic Modeling of Microbiological Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Chongxuan; Fang, Yilin

Kinetic description of microbiological processes is vital for the design and control of microbe-based biotechnologies such as waste water treatment, petroleum oil recovery, and contaminant attenuation and remediation. Various models have been proposed to describe microbiological processes. This editorial article discusses the advantages and limiation of these modeling approaches in cluding tranditional, Monod-type models and derivatives, and recently developed constraint-based approaches. The article also offers the future direction of modeling researches that best suit for petroleum and environmental biotechnologies.

Event-based hydrological modeling for detecting dominant hydrological process and suitable model strategy for semi-arid catchments

NASA Astrophysics Data System (ADS)

Huang, Pengnian; Li, Zhijia; Chen, Ji; Li, Qiaoling; Yao, Cheng

2016-11-01

To simulate the hydrological processes in semi-arid areas properly is still challenging. This study assesses the impact of different modeling strategies on simulating flood processes in semi-arid catchments. Four classic hydrological models, TOPMODEL, XINANJIANG (XAJ), SAC-SMA and TANK, were selected and applied to three semi-arid catchments in North China. Based on analysis and comparison of the simulation results of these classic models, four new flexible models were constructed and used to further investigate the suitability of various modeling strategies for semi-arid environments. Numerical experiments were also designed to examine the performances of the models. The results show that in semi-arid catchments a suitable model needs to include at least one nonlinear component to simulate the main process of surface runoff generation. If there are more than two nonlinear components in the hydrological model, they should be arranged in parallel, rather than in series. In addition, the results show that the parallel nonlinear components should be combined by multiplication rather than addition. Moreover, this study reveals that the key hydrological process over semi-arid catchments is the infiltration excess surface runoff, a non-linear component.

Smart Frameworks and Self-Describing Models: Model Metadata for Automated Coupling of Hydrologic Process Components (Invited)

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2013-12-01

Model coupling frameworks like CSDMS (Community Surface Dynamics Modeling System) and ESMF (Earth System Modeling Framework) have developed mechanisms that allow heterogeneous sets of process models to be assembled in a plug-and-play manner to create composite "system models". These mechanisms facilitate code reuse, but must simultaneously satisfy many different design criteria. They must be able to mediate or compensate for differences between the process models, such as their different programming languages, computational grids, time-stepping schemes, variable names and variable units. However, they must achieve this interoperability in a way that: (1) is noninvasive, requiring only relatively small and isolated changes to the original source code, (2) does not significantly reduce performance, (3) is not time-consuming or confusing for a model developer to implement, (4) can very easily be updated to accommodate new versions of a given process model and (5) does not shift the burden of providing model interoperability to the model developers, e.g. by requiring them to provide their output in specific forms that meet the input requirements of other models. In tackling these design challenges, model framework developers have learned that the best solution is to provide each model with a simple, standardized interface, i.e. a set of standardized functions that make the model: (1) fully-controllable by a caller (e.g. a model framework) and (2) self-describing. Model control functions are separate functions that allow a caller to initialize the model, advance the model's state variables in time and finalize the model. Model description functions allow a caller to retrieve detailed information on the model's input and output variables, its computational grid and its timestepping scheme. If the caller is a modeling framework, it can compare the answers to these queries with similar answers from other process models in a collection and then automatically call framework service components as necessary to mediate the differences between the coupled models. This talk will first review two key products of the CSDMS project, namely a standardized model interface called the Basic Model Interface (BMI) and the CSDMS Standard Names. The standard names are used in conjunction with BMI to provide a semantic matching mechanism that allows output variables from one process model to be reliably used as input variables to other process models in a collection. They include not just a standardized naming scheme for model variables, but also a standardized set of terms for describing the attributes and assumptions of a given model. To illustrate the power of standardized model interfaces and metadata, a smart, light-weight modeling framework written in Python will be introduced that can automatically (without user intervention) couple a set of BMI-enabled hydrologic process components together to create a spatial hydrologic model. The same mechanisms could also be used to provide seamless integration (import/export) of data and models.

A Microsoft Project-Based Planning, Tracking, and Management Tool for the National Transonic Facility's Model Changeover Process

NASA Technical Reports Server (NTRS)

Vairo, Daniel M.

1998-01-01

The removal and installation of sting-mounted wind tunnel models in the National Transonic Facility (NTF) is a multi-task process having a large impact on the annual throughput of the facility. Approximately ten model removal and installation cycles occur annually at the NTF with each cycle requiring slightly over five days to complete. The various tasks of the model changeover process were modeled in Microsoft Project as a template to provide a planning, tracking, and management tool. The template can also be used as a tool to evaluate improvements to this process. This document describes the development of the template and provides step-by-step instructions on its use and as a planning and tracking tool. A secondary role of this document is to provide an overview of the model changeover process and briefly describe the tasks associated with it.

Characterizing and Assessing a Large-Scale Software Maintenance Organization

NASA Technical Reports Server (NTRS)

Briand, Lionel; Melo, Walcelio; Seaman, Carolyn; Basili, Victor

1995-01-01

One important component of a software process is the organizational context in which the process is enacted. This component is often missing or incomplete in current process modeling approaches. One technique for modeling this perspective is the Actor-Dependency (AD) Model. This paper reports on a case study which used this approach to analyze and assess a large software maintenance organization. Our goal was to identify the approach's strengths and weaknesses while providing practical recommendations for improvement and research directions. The AD model was found to be very useful in capturing the important properties of the organizational context of the maintenance process, and aided in the understanding of the flaws found in this process. However, a number of opportunities for extending and improving the AD model were identified. Among others, there is a need to incorporate quantitative information to complement the qualitative model.

The enhanced Software Life Cyle Support Environment (ProSLCSE): Automation for enterprise and process modeling

NASA Technical Reports Server (NTRS)

Milligan, James R.; Dutton, James E.

1993-01-01

In this paper, we have introduced a comprehensive method for enterprise modeling that addresses the three important aspects of how an organization goes about its business. FirstEP includes infrastructure modeling, information modeling, and process modeling notations that are intended to be easy to learn and use. The notations stress the use of straightforward visual languages that are intuitive, syntactically simple, and semantically rich. ProSLCSE will be developed with automated tools and services to facilitate enterprise modeling and process enactment. In the spirit of FirstEP, ProSLCSE tools will also be seductively easy to use. Achieving fully managed, optimized software development and support processes will be long and arduous for most software organizations, and many serious problems will have to be solved along the way. ProSLCSE will provide the ability to document, communicate, and modify existing processes, which is the necessary first step.

Complex Networks in Psychological Models

NASA Astrophysics Data System (ADS)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Climbing the ladder: capability maturity model integration level 3

NASA Astrophysics Data System (ADS)

Day, Bryce; Lutteroth, Christof

2011-02-01

This article details the attempt to form a complete workflow model for an information and communication technologies (ICT) company in order to achieve a capability maturity model integration (CMMI) maturity rating of 3. During this project, business processes across the company's core and auxiliary sectors were documented and extended using modern enterprise modelling tools and a The Open Group Architectural Framework (TOGAF) methodology. Different challenges were encountered with regard to process customisation and tool support for enterprise modelling. In particular, there were problems with the reuse of process models, the integration of different project management methodologies and the integration of the Rational Unified Process development process framework that had to be solved. We report on these challenges and the perceived effects of the project on the company. Finally, we point out research directions that could help to improve the situation in the future.

Simulating aerial gravitropism and posture control in plants: what has been done, what is missing

NASA Astrophysics Data System (ADS)

Coutand, Catherine; Pot, Guillaume; Bastien, R.; Badel, Eric; Moulia, Bruno

The gravitropic response requires a process of perception of the signal and a motor process to actuate the movements. Different models have been developed, some focuses on the perception process and some focuses on the motor process. The kinematics of the gravitropic response will be first detailed to set the phenomenology of gravi- and auto-tropism. A model of perception (AC model) will be first presented to demonstrate that sensing inclination is not sufficient to control the gravitropic movement, and that proprioception is also involved. Then, “motor models” will be reviewed. In herbaceous plants, differential growth is the main motor. Modelling tropic movements with simulating elongation raises some difficulties that will be explained. In woody structures the main motor process is the differentiation of reaction wood via cambial growth. We will first present the simplest biomechanical model developed to simulate gravitropism and its limits will be pointed out. Then a more sophisticated model (TWIG) will be presented with a special focus on the importance of wood viscoelasticity and the wood maturation process and its regulation by a mechanosensing process. The presentation will end by a balance sheet of what is done and what is missing for a complete modelling of gravitropism and will present first results of a running project dedicating to get the data required to include phototropism in the actual models.

NaturAnalogs for the Unsaturated Zone

DOE Office of Scientific and Technical Information (OSTI.GOV)

A. Simmons; A. Unger; M. Murrell

2000-03-08

The purpose of this Analysis/Model Report (AMR) is to document natural and anthropogenic (human-induced) analog sites and processes that are applicable to flow and transport processes expected to occur at the potential Yucca Mountain repository in order to build increased confidence in modeling processes of Unsaturated Zone (UZ) flow and transport. This AMR was prepared in accordance with ''AMR Development Plan for U0135, Natural Analogs for the UZ'' (CRWMS 1999a). Knowledge from analog sites and processes is used as corroborating information to test and build confidence in flow and transport models of Yucca Mountain, Nevada. This AMR supports the Unsaturatedmore » Zone (UZ) Flow and Transport Process Model Report (PMR) and the Yucca Mountain Site Description. The objectives of this AMR are to test and build confidence in the representation of UZ processes in numerical models utilized in the UZ Flow and Transport Model. This is accomplished by: (1) applying data from Boxy Canyon, Idaho in simulations of UZ flow using the same methodologies incorporated in the Yucca Mountain UZ Flow and Transport Model to assess the fracture-matrix interaction conceptual model; (2) Providing a preliminary basis for analysis of radionuclide transport at Pena Blanca, Mexico as an analog of radionuclide transport at Yucca Mountain; and (3) Synthesizing existing information from natural analog studies to provide corroborating evidence for representation of ambient and thermally coupled UZ flow and transport processes in the UZ Model.« less

Using CASE to Exploit Process Modeling in Technology Transfer

NASA Technical Reports Server (NTRS)

Renz-Olar, Cheryl

2003-01-01

A successful business will be one that has processes in place to run that business. Creating processes, reengineering processes, and continually improving processes can be accomplished through extensive modeling. Casewise(R) Corporate Modeler(TM) CASE is a computer aided software engineering tool that will enable the Technology Transfer Department (TT) at NASA Marshall Space Flight Center (MSFC) to capture these abilities. After successful implementation of CASE, it could then go on to be applied in other departments at MSFC and other centers at NASA. The success of a business process is dependent upon the players working as a team and continuously improving the process. A good process fosters customer satisfaction as well as internal satisfaction in the organizational infrastructure. CASE provides a method for business process success through functions consisting of systems and processes business models; specialized diagrams; matrix management; simulation; report generation and publishing; and, linking, importing, and exporting documents and files. The software has an underlying repository or database to support these functions. The Casewise. manual informs us that dynamics modeling is a technique used in business design and analysis. Feedback is used as a tool for the end users and generates different ways of dealing with the process. Feedback on this project resulted from collection of issues through a systems analyst interface approach of interviews with process coordinators and Technical Points of Contact (TPOCs).

The Role of Short-Term Memory in Operator Workload

DTIC Science & Technology

1988-04-01

Craik , F.I. and Lockhart , R.S., 1972, Levels of processing : A framework for memory research...examples of postcategorical selction models. Precategorical Selection Models Sperling’s Model. Unlike Craik and Lockhart’s levels -of- processing model...subdivided to contain a primary memory unit and a mechanism for the direction of conscious attention. Levels -of- Processing . Craik and Lockhart’s levels

Building Information Modeling (BIM) Primer. Report 1: Facility Life-Cycle Process and Technology Innovation

DTIC Science & Technology

2012-08-01

Building Information Modeling ( BIM ) Primer Report 1: Facility Life-cycle Process and Technology Innovation In fo...is unlimited. ERDC/ITL TR-12-2 August 2012 Building Information Modeling ( BIM ) Primer Report 1: Facility Life-cycle Process and Technology...and to enhance the quality of projects through the design, construction, and handover phases. Building Information Modeling ( BIM ) is a

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, J.; Moon, T.J.; Howell, J.R.

This paper presents an analysis of the heat transfer occurring during an in-situ curing process for which infrared energy is provided on the surface of polymer composite during winding. The material system is Hercules prepreg AS4/3501-6. Thermoset composites have an exothermic chemical reaction during the curing process. An Eulerian thermochemical model is developed for the heat transfer analysis of helical winding. The model incorporates heat generation due to the chemical reaction. Several assumptions are made leading to a two-dimensional, thermochemical model. For simplicity, 360{degree} heating around the mandrel is considered. In order to generate the appropriate process windows, the developedmore » heat transfer model is combined with a simple winding time model. The process windows allow for a proper selection of process variables such as infrared energy input and winding velocity to give a desired end-product state. Steady-state temperatures are found for each combination of the process variables. A regression analysis is carried out to relate the process variables to the resulting steady-state temperatures. Using regression equations, process windows for a wide range of cylinder diameters are found. A general procedure to find process windows for Hercules AS4/3501-6 prepreg tape is coded in a FORTRAN program.« less

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

USGS Publications Warehouse

Barker, Richard J.; Link, William A.

2015-01-01

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

Colored petri net modeling of small interfering RNA-mediated messenger RNA degradation.

PubMed

Nickaeen, Niloofar; Moein, Shiva; Heidary, Zarifeh; Ghaisari, Jafar

2016-01-01

Mathematical modeling of biological systems is an attractive way for studying complex biological systems and their behaviors. Petri Nets, due to their ability to model systems with various levels of qualitative information, have been wildly used in modeling biological systems in which enough qualitative data may not be at disposal. These nets have been used to answer questions regarding the dynamics of different cell behaviors including the translation process. In one stage of the translation process, the RNA sequence may be degraded. In the process of degradation of RNA sequence, small-noncoding RNA molecules known as small interfering RNA (siRNA) match the target RNA sequence. As a result of this matching, the target RNA sequence is destroyed. In this context, the process of matching and destruction is modeled using Colored Petri Nets (CPNs). The model is constructed using CPNs which allow tokens to have a value or type on them. Thus, CPN is a suitable tool to model string structures in which each element of the string has a different type. Using CPNs, long RNA, and siRNA strings are modeled with a finite set of colors. The model is simulated via CPN Tools. A CPN model of the matching between RNA and siRNA strings is constructed in CPN Tools environment. In previous studies, a network of stoichiometric equations was modeled. However, in this particular study, we modeled the mechanism behind the silencing process. Modeling this kind of mechanisms provides us with a tool to examine the effects of different factors such as mutation or drugs on the process.

Identification of the dominant hydrological process and appropriate model structure of a karst catchment through stepwise simplification of a complex conceptual model

NASA Astrophysics Data System (ADS)

Chang, Yong; Wu, Jichun; Jiang, Guanghui; Kang, Zhiqiang

2017-05-01

Conceptual models often suffer from the over-parameterization problem due to limited available data for the calibration. This leads to the problem of parameter nonuniqueness and equifinality, which may bring much uncertainty of the simulation result. How to find out the appropriate model structure supported by the available data to simulate the catchment is still a big challenge in the hydrological research. In this paper, we adopt a multi-model framework to identify the dominant hydrological process and appropriate model structure of a karst spring, located in Guilin city, China. For this catchment, the spring discharge is the only available data for the model calibration. This framework starts with a relative complex conceptual model according to the perception of the catchment and then this complex is simplified into several different models by gradually removing the model component. The multi-objective approach is used to compare the performance of these different models and the regional sensitivity analysis (RSA) is used to investigate the parameter identifiability. The results show this karst spring is mainly controlled by two different hydrological processes and one of the processes is threshold-driven which is consistent with the fieldwork investigation. However, the appropriate model structure to simulate the discharge of this spring is much simpler than the actual aquifer structure and hydrological processes understanding from the fieldwork investigation. A simple linear reservoir with two different outlets is enough to simulate this spring discharge. The detail runoff process in the catchment is not needed in the conceptual model to simulate the spring discharge. More complex model should need more other additional data to avoid serious deterioration of model predictions.

Clarification process: Resolution of decision-problem conditions

NASA Technical Reports Server (NTRS)

Dieterly, D. L.

1980-01-01

A model of a general process which occurs in both decisionmaking and problem-solving tasks is presented. It is called the clarification model and is highly dependent on information flow. The model addresses the possible constraints of individual indifferences and experience in achieving success in resolving decision-problem conditions. As indicated, the application of the clarification process model is only necessary for certain classes of the basic decision-problem condition. With less complex decision problem conditions, certain phases of the model may be omitted. The model may be applied across a wide range of decision problem conditions. The model consists of two major components: (1) the five-phase prescriptive sequence (based on previous approaches to both concepts) and (2) the information manipulation function (which draws upon current ideas in the areas of information processing, computer programming, memory, and thinking). The two components are linked together to provide a structure that assists in understanding the process of resolving problems and making decisions.

Generalised additive modelling approach to the fermentation process of glutamate.

PubMed

Liu, Chun-Bo; Li, Yun; Pan, Feng; Shi, Zhong-Ping

2011-03-01

In this work, generalised additive models (GAMs) were used for the first time to model the fermentation of glutamate (Glu). It was found that three fermentation parameters fermentation time (T), dissolved oxygen (DO) and oxygen uptake rate (OUR) could capture 97% variance of the production of Glu during the fermentation process through a GAM model calibrated using online data from 15 fermentation experiments. This model was applied to investigate the individual and combined effects of T, DO and OUR on the production of Glu. The conditions to optimize the fermentation process were proposed based on the simulation study from this model. Results suggested that the production of Glu can reach a high level by controlling concentration levels of DO and OUR to the proposed optimization conditions during the fermentation process. The GAM approach therefore provides an alternative way to model and optimize the fermentation process of Glu. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Cognitive Processing of Fear-Arousing Message Content.

ERIC Educational Resources Information Center

Hale, Jerold L.; And Others

1995-01-01

Investigates two models (the Elaboration Likelihood Model and the Heuristic-Systematic Model) of the cognitive processing of fear-arousing messages in undergraduate students. Finds in three of the four conditions (low fear, high fear, high trait anxiety) that cognitive processing appears to be antagonistic. Finds some evidence of concurrent…

Models of Quantitative Estimations: Rule-Based and Exemplar-Based Processes Compared

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model--the mapping model--that outperformed the…

76 FR 296 - Periodic Reporting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-04

... part would update the mail processing portion of the Parcel Select/Parcel Return Service cost models...) processing cost model that was filed as Proposal Seven on September 8, 2010. Proposal Thirteen at 1. These... develop the Standard Mail/non-flat machinable (NFM) mail processing cost model. It also proposes to use...

Are there two processes in reasoning? The dimensionality of inductive and deductive inferences.

PubMed

Stephens, Rachel G; Dunn, John C; Hayes, Brett K

2018-03-01

Single-process accounts of reasoning propose that the same cognitive mechanisms underlie inductive and deductive inferences. In contrast, dual-process accounts propose that these inferences depend upon 2 qualitatively different mechanisms. To distinguish between these accounts, we derived a set of single-process and dual-process models based on an overarching signal detection framework. We then used signed difference analysis to test each model against data from an argument evaluation task, in which induction and deduction judgments are elicited for sets of valid and invalid arguments. Three data sets were analyzed: data from Singmann and Klauer (2011), a database of argument evaluation studies, and the results of an experiment designed to test model predictions. Of the large set of testable models, we found that almost all could be rejected, including all 2-dimensional models. The only testable model able to account for all 3 data sets was a model with 1 dimension of argument strength and independent decision criteria for induction and deduction judgments. We conclude that despite the popularity of dual-process accounts, current results from the argument evaluation task are best explained by a single-process account that incorporates separate decision thresholds for inductive and deductive inferences. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Phylogenetic mixtures and linear invariants for equal input models.

PubMed

Casanellas, Marta; Steel, Mike

2017-04-01

The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).

A simulation study on garment manufacturing process

NASA Astrophysics Data System (ADS)

Liong, Choong-Yeun; Rahim, Nur Azreen Abdul

2015-02-01

Garment industry is an important industry and continues to evolve in order to meet the consumers' high demands. Therefore, elements of innovation and improvement are important. In this work, research studies were conducted at a local company in order to model the sewing process of clothes manufacturing by using simulation modeling. Clothes manufacturing at the company involves 14 main processes, which are connecting the pattern, center sewing and side neating, pockets sewing, backside-sewing, attaching the front and back, sleeves preparation, attaching the sleeves and over lock, collar preparation, collar sewing, bottomedge sewing, buttonholing sewing, removing excess thread, marking button, and button cross sewing. Those fourteen processes are operated by six tailors only. The last four sets of processes are done by a single tailor. Data collection was conducted by on site observation and the probability distribution of processing time for each of the processes is determined by using @Risk's Bestfit. Then a simulation model is developed using Arena Software based on the data collected. Animated simulation model is developed in order to facilitate understanding and verifying that the model represents the actual system. With such model, what if analysis and different scenarios of operations can be experimented with virtually. The animation and improvement models will be presented in further work.

Modelacio de sedimentadors en plantes de tractament d'aigues residuals. Aplicacio al proces de fermentacio - elutracio de fangs primaris

NASA Astrophysics Data System (ADS)

Ribes Bertomeu, Josep

Wastewater treatments require the execution of many conversion processes simultaneously and/or consecutively, making them a tricky object of study. Furthermore, complexity of treatment processes is increasing not only for the more stringent effluent standards required, but also for the new trends towards sustainable development, which in this process are mainly focused on energy saving and nutrient recovery from wastewaters in order to improve their life cycle. For this reason it becomes necessary to use simulation tools which are able to represent all these processes by means of a suitable mathematical model. They can help in determining and predicting the behaviour of the different treatment schemes. These simulators have become essential for the design, control and optimization of wastewater treatment plants (WWTP). Settling processes have a significant role in the accomplishment of effluent standards and the correct operation of the plant. However, many models that are currently employed for WWTP design and simulation do not take into account settling processes or they are handled in a very simple way, by neglecting the biochemical processes that can occur during sedimentation. People of CALAGUA research group have focussed their efforts towards a new philosophy of simulating treatment plants, which is based on the use of a unique model to represent all physical, chemical and biological processes taking place in WWTPs. In this research topic, they have worked on the development of a general quality model that considers biological conversion processes carried out by different microorganism groups, acid base chemical interactions affecting the pH value in the system, and gas-liquid transfer processes. However, a generalized use of such a quality model requires its combination with a flux model, principally for those processes where completely mixture can not be assumed, as for instance, settlers and thickeners in WWTPs. The main objective of this work has been the development and validation of a general settling model that allows simulating the main settling operations taking place in a WWTP, considering both primary and secondary settlers and thickeners. It consists in a one-dimensional model based on the flux theory of Kynch and the double-exponential settling function of Takacs that takes into account flocculation, hindered settling and compression processes. The model has been applied to simulation of settlers and thickeners by means of splitting the system into several horizontal layers, all of them considered as completely mixed reactors which are interconnected by mass flux obtained from the settling model. In order to simulate the conversion processes taking place during sedimentation, the general quality model BNRM1 has been added, and it has been proposed an iterative procedure for solving the equations for each layer in which the settler has been divided. The settling flux model validation, along with the quality model, has been carried out by applying them to a simulation of primary sludge fermentation - elutriation process. This process has been studied on a pilot plant located in the Carraixet WWTP in Alboraia (Valencia). In order to simulate the observed decrease in solids separation efficiency in the studied fermentation - elutriation process, the quality model has been modified with the addition of a new process called "disintegration of complex particulate material". This process influences the settleability of the sludge because it is considered that the disintegrated solids become non-settleable solids. This modification implies the addition of two new kinetic parameters (the specific disintegration velocity for volatile particulate material and the specific disintegration velocity for non volatile particulate material). However, the settling parameter that represents the non-settleable fraction of total suspended solids is eliminated from the model and it has been transformed into an experimental variable which is quite easy to analyze. The result of this modification is a more general model, which is applicable to fermentation - elutriation process working at any operating condition. Finally, the behaviour and capabilities of the developed model have been tested by simulating a complete WWTP on the DESASS simulation software, developed by the research group. This example includes the most important processes that can be used in a WWTP: biological nutrient removal, primary sludge fermentation and sludge digestion. The model allows considering both settling processes and biochemical processes as a whole (denitrification in secondary settlers, primary sludge fermentation and VFA elutriation, phosphorus release in thickeners because of the PAO decay, etc.). The developed model implies an important advance in study of new wastewater treatment processes because it allows dealing with global process optimization problems, by means of full plants simulation. It is very useful for studying the effects of a modification in operation conditions of one element over the operation of the rest of the elements of the WWTP. (Abstract shortened by UMI.).

A new generic plant growth model framework (PMF): Simulating distributed dynamic interaction of biomass production and its interaction with water and nutrients fluxes

NASA Astrophysics Data System (ADS)

Multsch, Sebastian; Kraft, Philipp; Frede, Hans-Georg; Breuer, Lutz

2010-05-01

Today, crop models have a widespread application in natural sciences, because plant growth interacts and modifies the environment. Transport processes involve water and nutrient uptake from the saturated and unsaturated zone in the pedosphere. Turnover processes include the conversion of dead root biomass into organic matter. Transpiration and the interception of radiation influence the energy exchange between atmosphere and biosphere. But many more feedback mechanisms might be of interest, including erosion, soil compaction or trace gas exchanges. Most of the existing crop models have a closed structure and do not provide interfaces or code design elements for easy data transfer or process exchange with other models during runtime. Changes in the model structure, the inclusion of alternative process descriptions or the implementation of additional functionalities requires a lot of coding. The same is true if models are being upscaled from field to landscape or catchment scale. We therefore conclude that future integrated model developments would benefit from a model structure that has the following requirements: replaceability, expandability and independency. In addition to these requirements we also propose the interactivity of models, which means that models that are being coupled are highly interacting and depending on each other, i.e. the model should be open for influences from other independent models and react on influences directly. Hence, a model which consists of building blocks seems to be reasonable. The aim of the study is the presentation of the new crop model type, the plant growth model framework, PMF. The software concept refers to an object-oriented approach, which is developed with the Unified Modeling Language (UML). The model is implemented with Python, a high level object-oriented programming language. The integration of the models with a setup code enables the data transfer on the computer memory level and direct exchange of information about changing boundary conditions. The crop model concept refers to two main elements. A plant model, which represents an abstract network of plant organs and processes and a process library, which holds mathematical solutions for the growth processes. Growth processes were mainly taken from existing, well known crop models such as SUCROS and CERES. The crop specific properties of root architecture are described based on a maximum rooting depth and a vertical growth rate. The biomass distribution depends on an interactive allocation process due to the soil layers with a daily time step. In order to show the performance and capabilities of PMF, the model is coupled with the Catchment Modeling Framework (CMF) and the simple nitrogen mineralization model DeComp. The main feature of the integrated model set up is the interaction between root growth, water uptake and nitrogen supply of the soil. We show a virtual case study on the hillslope scale and spatially dependence of water and nitrogen stress based on topographic position and seasonal development.

Nonlinear Growth Curves in Developmental Research

PubMed Central

Grimm, Kevin J.; Ram, Nilam; Hamagami, Fumiaki

2011-01-01

Developmentalists are often interested in understanding change processes and growth models are the most common analytic tool for examining such processes. Nonlinear growth curves are especially valuable to developmentalists because the defining characteristics of the growth process such as initial levels, rates of change during growth spurts, and asymptotic levels can be estimated. A variety of growth models are described beginning with the linear growth model and moving to nonlinear models of varying complexity. A detailed discussion of nonlinear models is provided, highlighting the added insights into complex developmental processes associated with their use. A collection of growth models are fit to repeated measures of height from participants of the Berkeley Growth and Guidance Studies from early childhood through adulthood. PMID:21824131

Architectural Improvements and New Processing Tools for the Open XAL Online Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allen, Christopher K; Pelaia II, Tom; Freed, Jonathan M

The online model is the component of Open XAL providing accelerator modeling, simulation, and dynamic synchronization to live hardware. Significant architectural changes and feature additions have been recently made in two separate areas: 1) the managing and processing of simulation data, and 2) the modeling of RF cavities. Simulation data and data processing have been completely decoupled. A single class manages all simulation data while standard tools were developed for processing the simulation results. RF accelerating cavities are now modeled as composite structures where parameter and dynamics computations are distributed. The beam and hardware models both maintain their relative phasemore » information, which allows for dynamic phase slip and elapsed time computation.« less

Processing Speed in Children: Examination of the Structure in Middle Childhood and Its Impact on Reading

ERIC Educational Resources Information Center

Gerst, Elyssa H.

2017-01-01

The primary aim of this study was to examine the structure of processing speed (PS) in middle childhood by comparing five theoretically driven models of PS. The models consisted of two conceptual models (a unitary model, a complexity model) and three methodological models (a stimulus material model, an output modality model, and a timing modality…

Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

PubMed

Ding, Jinliang; Chai, Tianyou; Wang, Hong

2011-03-01

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Applying a Qualitative Modeling Shell to Process Diagnosis: The Caster System.

DTIC Science & Technology

1986-03-01

Process Diagnosis: The Caster System by Timothy F. Thompson and William J. Clancey Department of Computer Science Stanford University Stanford, CA 94303...MODELING SHELL TO PROCESS DIAGNOSIS: THE CASTER SYSTEM 12 PERSONAL AUTHOR(S) TIMOTHY F. THOMPSON. WESTINGHOUSE R&D CENTER, WILLIAM CLANCEY, STANFORD...editions are obsolete. Applying a Qualitative Modeling Shell to Process Diagnosis: The Caster System by Timothy F. Thompson, Westinghouse R&D Center

Airport security inspection process model and optimization based on GSPN

NASA Astrophysics Data System (ADS)

Mao, Shuainan

2018-04-01

Aiming at the efficiency of airport security inspection process, Generalized Stochastic Petri Net is used to establish the security inspection process model. The model is used to analyze the bottleneck problem of airport security inspection process. The solution to the bottleneck is given, which can significantly improve the efficiency and reduce the waiting time by adding the place for people to remove their clothes and the X-ray detector.

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

Simulation based analysis of laser beam brazing

NASA Astrophysics Data System (ADS)

Dobler, Michael; Wiethop, Philipp; Schmid, Daniel; Schmidt, Michael

2016-03-01

Laser beam brazing is a well-established joining technology in car body manufacturing with main applications in the joining of divided tailgates and the joining of roof and side panels. A key advantage of laser brazed joints is the seam's visual quality which satisfies highest requirements. However, the laser beam brazing process is very complex and process dynamics are only partially understood. In order to gain deeper knowledge of the laser beam brazing process, to determine optimal process parameters and to test process variants, a transient three-dimensional simulation model of laser beam brazing is developed. This model takes into account energy input, heat transfer as well as fluid and wetting dynamics that lead to the formation of the brazing seam. A validation of the simulation model is performed by metallographic analysis and thermocouple measurements for different parameter sets of the brazing process. These results show that the multi-physical simulation model not only can be used to gain insight into the laser brazing process but also offers the possibility of process optimization in industrial applications. The model's capabilities in determining optimal process parameters are exemplarily shown for the laser power. Small deviations in the energy input can affect the brazing results significantly. Therefore, the simulation model is used to analyze the effect of the lateral laser beam position on the energy input and the resulting brazing seam.

A New Biogeochemical Computational Framework Integrated within the Community Land Model

NASA Astrophysics Data System (ADS)

Fang, Y.; Li, H.; Liu, C.; Huang, M.; Leung, L.

2012-12-01

Terrestrial biogeochemical processes, particularly carbon cycle dynamics, have been shown to significantly influence regional and global climate changes. Modeling terrestrial biogeochemical processes within the land component of Earth System Models such as the Community Land model (CLM), however, faces three major challenges: 1) extensive efforts in modifying modeling structures and rewriting computer programs to incorporate biogeochemical processes with increasing complexity, 2) expensive computational cost to solve the governing equations due to numerical stiffness inherited from large variations in the rates of biogeochemical processes, and 3) lack of an efficient framework to systematically evaluate various mathematical representations of biogeochemical processes. To address these challenges, we introduce a new computational framework to incorporate biogeochemical processes into CLM, which consists of a new biogeochemical module with a generic algorithm and reaction database. New and updated biogeochemical processes can be incorporated into CLM without significant code modification. To address the stiffness issue, algorithms and criteria will be developed to identify fast processes, which will be replaced with algebraic equations and decoupled from slow processes. This framework can serve as a generic and user-friendly platform to test out different mechanistic process representations and datasets and gain new insight on the behavior of the terrestrial ecosystems in response to climate change in a systematic way.

Erosion and Sediment Transport Modelling in Shallow Waters: A Review on Approaches, Models and Applications.

PubMed

Hajigholizadeh, Mohammad; Melesse, Assefa M; Fuentes, Hector R

2018-03-14

The erosion and sediment transport processes in shallow waters, which are discussed in this paper, begin when water droplets hit the soil surface. The transport mechanism caused by the consequent rainfall-runoff process determines the amount of generated sediment that can be transferred downslope. Many significant studies and models are performed to investigate these processes, which differ in terms of their effecting factors, approaches, inputs and outputs, model structure and the manner that these processes represent. This paper attempts to review the related literature concerning sediment transport modelling in shallow waters. A classification based on the representational processes of the soil erosion and sediment transport models (empirical, conceptual, physical and hybrid) is adopted, and the commonly-used models and their characteristics are listed. This review is expected to be of interest to researchers and soil and water conservation managers who are working on erosion and sediment transport phenomena in shallow waters. The paper format should be helpful for practitioners to identify and generally characterize the types of available models, their strengths and their basic scope of applicability.

Erosion and Sediment Transport Modelling in Shallow Waters: A Review on Approaches, Models and Applications

PubMed Central

Fuentes, Hector R.

2018-01-01

The erosion and sediment transport processes in shallow waters, which are discussed in this paper, begin when water droplets hit the soil surface. The transport mechanism caused by the consequent rainfall-runoff process determines the amount of generated sediment that can be transferred downslope. Many significant studies and models are performed to investigate these processes, which differ in terms of their effecting factors, approaches, inputs and outputs, model structure and the manner that these processes represent. This paper attempts to review the related literature concerning sediment transport modelling in shallow waters. A classification based on the representational processes of the soil erosion and sediment transport models (empirical, conceptual, physical and hybrid) is adopted, and the commonly-used models and their characteristics are listed. This review is expected to be of interest to researchers and soil and water conservation managers who are working on erosion and sediment transport phenomena in shallow waters. The paper format should be helpful for practitioners to identify and generally characterize the types of available models, their strengths and their basic scope of applicability. PMID:29538335

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

PubMed

da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G

2016-07-08

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.

Modeling of the flow stress for AISI H13 Tool Steel during Hard Machining Processes

NASA Astrophysics Data System (ADS)

Umbrello, Domenico; Rizzuti, Stefania; Outeiro, José C.; Shivpuri, Rajiv

2007-04-01

In general, the flow stress models used in computer simulation of machining processes are a function of effective strain, effective strain rate and temperature developed during the cutting process. However, these models do not adequately describe the material behavior in hard machining, where a range of material hardness between 45 and 60 HRC are used. Thus, depending on the specific material hardness different material models must be used in modeling the cutting process. This paper describes the development of a hardness-based flow stress and fracture models for the AISI H13 tool steel, which can be applied for range of material hardness mentioned above. These models were implemented in a non-isothermal viscoplastic numerical model to simulate the machining process for AISI H13 with various hardness values and applying different cutting regime parameters. Predicted results are validated by comparing them with experimental results found in the literature. They are found to predict reasonably well the cutting forces as well as the change in chip morphology from continuous to segmented chip as the material hardness change.

Research environments that promote integrity.

PubMed

Jeffers, Brenda Recchia; Whittemore, Robin

2005-01-01

The body of empirical knowledge about research integrity and the factors that promote research integrity in nursing research environments remains small. To propose an internal control model as an innovative framework for the design and structure of nursing research environments that promote integrity. An internal control model is adapted to illustrate its use for conceptualizing and designing research environments that promote integrity. The internal control model integrates both the organizational elements necessary to promote research integrity and the processes needed to assess research environments. The model provides five interrelated process components within which any number of research integrity variables and processes may be used and studied: internal control environment, risk assessment, internal control activities, monitoring, and information and communication. The components of the proposed research integrity internal control model proposed comprise an integrated conceptualization of the processes that provide reasonable assurance that research integrity will be promoted within the nursing research environment. Schools of nursing can use the model to design, implement, and evaluate systems that promote research integrity. The model process components need further exploration to substantiate the use of the model in nursing research environments.

Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale

NASA Astrophysics Data System (ADS)

Epelde, Ane Miren; Antiguedad, Iñaki; Brito, David; Jauch, Eduardo; Neves, Ramiro; Garneau, Cyril; Sauvage, Sabine; Sánchez-Pérez, José Miguel

2016-08-01

This study presents the simulation of hydrological processes and nutrient transport and turnover processes using two integrated numerical models: Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998), an empirical and semi-distributed numerical model; and Modelo Hidrodinâmico (MOHID) (Neves, 1985), a physics-based and fully distributed numerical model. This work shows that both models reproduce satisfactorily water and nitrate exportation at the watershed scale at annual and daily basis, MOHID providing slightly better results. At the watershed scale, both SWAT and MOHID simulated similarly and satisfactorily the denitrification amount. However, as MOHID numerical model was the only one able to reproduce adequately the spatial variation of the soil hydrological conditions and water table level fluctuation, it proved to be the only model able of reproducing the spatial variation of the nutrient cycling processes that are dependent to the soil hydrological conditions such as the denitrification process. This evidences the strength of the fully distributed and physics-based models to simulate the spatial variability of nutrient cycling processes that are dependent to the hydrological conditions of the soils.

Nitrogen cycling models and their application to forest harvesting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, D.W.; Dale, V.H.

1986-01-01

The characterization of forest nitrogen- (N-) cycling processes by several N-cycling models (FORCYTE, NITCOMP, FORTNITE, and LINKAGES) is briefly reviewed and evaluated against current knowledge of N cycling in forests. Some important processes (e.g., translocation within trees, N dynamics in decaying leaf litter) appear to be well characterized, whereas others (e.g., N mineralization from soil organic matter, N fixation, N dynamics in decaying wood, nitrification, and nitrate leaching) are poorly characterized, primarily because of a lack of knowledge rather than an oversight by model developers. It is remarkable how well the forest models do work in the absence of datamore » on some key processes. For those systems in which the poorly understood processes could cause major changes in N availability or productivity, the accuracy of model predictions should be examined. However, the development of N-cycling models represents a major step beyond the much simpler, classic conceptual models of forest nutrient cycling developed by early investigators. The new generation of computer models will surely improve as research reveals how key nutrient-cycling processes operate.« less

Electrophysiological models of neural processing.

PubMed

Nelson, Mark E

2011-01-01

The brain is an amazing information processing system that allows organisms to adaptively monitor and control complex dynamic interactions with their environment across multiple spatial and temporal scales. Mathematical modeling and computer simulation techniques have become essential tools in understanding diverse aspects of neural processing ranging from sub-millisecond temporal coding in the sound localization circuity of barn owls to long-term memory storage and retrieval in humans that can span decades. The processing capabilities of individual neurons lie at the core of these models, with the emphasis shifting upward and downward across different levels of biological organization depending on the nature of the questions being addressed. This review provides an introduction to the techniques for constructing biophysically based models of individual neurons and local networks. Topics include Hodgkin-Huxley-type models of macroscopic membrane currents, Markov models of individual ion-channel currents, compartmental models of neuronal morphology, and network models involving synaptic interactions among multiple neurons.

Dynamic modeling of sludge compaction and consolidation processes in wastewater secondary settling tanks.

PubMed

Abusam, A; Keesman, K J

2009-01-01

The double exponential settling model is the widely accepted model for wastewater secondary settling tanks. However, this model does not estimate accurately solids concentrations in the settler underflow stream, mainly because sludge compression and consolidation processes are not considered. In activated sludge systems, accurate estimation of the solids in the underflow stream will facilitate the calibration process and can lead to correct estimates of particularly kinetic parameters related to biomass growth. Using principles of compaction and consolidation, as in soil mechanics, a dynamic model of the sludge consolidation processes taking place in the secondary settling tanks is developed and incorporated to the commonly used double exponential settling model. The modified double exponential model is calibrated and validated using data obtained from a full-scale wastewater treatment plant. Good agreement between predicted and measured data confirmed the validity of the modified model.

Conducting requirements analyses for research using routinely collected health data: a model driven approach.

PubMed

de Lusignan, Simon; Cashman, Josephine; Poh, Norman; Michalakidis, Georgios; Mason, Aaron; Desombre, Terry; Krause, Paul

2012-01-01

Medical research increasingly requires the linkage of data from different sources. Conducting a requirements analysis for a new application is an established part of software engineering, but rarely reported in the biomedical literature; and no generic approaches have been published as to how to link heterogeneous health data. Literature review, followed by a consensus process to define how requirements for research, using, multiple data sources might be modeled. We have developed a requirements analysis: i-ScheDULEs - The first components of the modeling process are indexing and create a rich picture of the research study. Secondly, we developed a series of reference models of progressive complexity: Data flow diagrams (DFD) to define data requirements; unified modeling language (UML) use case diagrams to capture study specific and governance requirements; and finally, business process models, using business process modeling notation (BPMN). These requirements and their associated models should become part of research study protocols.

Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

PubMed

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

The use of discrete-event simulation modelling to improve radiation therapy planning processes.

PubMed

Werker, Greg; Sauré, Antoine; French, John; Shechter, Steven

2009-07-01

The planning portion of the radiation therapy treatment process at the British Columbia Cancer Agency is efficient but nevertheless contains room for improvement. The purpose of this study is to show how a discrete-event simulation (DES) model can be used to represent this complex process and to suggest improvements that may reduce the planning time and ultimately reduce overall waiting times. A simulation model of the radiation therapy (RT) planning process was constructed using the Arena simulation software, representing the complexities of the system. Several types of inputs feed into the model; these inputs come from historical data, a staff survey, and interviews with planners. The simulation model was validated against historical data and then used to test various scenarios to identify and quantify potential improvements to the RT planning process. Simulation modelling is an attractive tool for describing complex systems, and can be used to identify improvements to the processes involved. It is possible to use this technique in the area of radiation therapy planning with the intent of reducing process times and subsequent delays for patient treatment. In this particular system, reducing the variability and length of oncologist-related delays contributes most to improving the planning time.

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

PubMed Central

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797

Basin infilling of a schematic 1D estuary using two different approaches: an aggregate diffusive type model and a processed based model.

NASA Astrophysics Data System (ADS)

Laginha Silva, Patricia; Martins, Flávio A.; Boski, Tomász; Sampath, Dissanayake M. R.

2010-05-01

Fluvial sediment transport creates great challenges for river scientists and engineers. The interaction between the fluid (water) and the solid (dispersed sediment particles) phases is crucial in morphodynamics. The process of sediment transport and the resulting morphological evolution of rivers get more complex with the exposure of the fluvial systems to the natural and variable environment (climatic, geological, ecological and social, etc.). The earlier efforts in mathematical river modelling were almost exclusively built on traditional fluvial hydraulics. The last half century has seen more and more developments and applications of mathematical models for fluvial flow, sediment transport and morphological evolution. The first attempts for a quantitative description and simulation of basin filling in geological time scales started in the late 60´s of the last century (eg. Schwarzacher, 1966; Briggs & Pollack, 1967). However, the quality of this modelling practice has emerged as a crucial issue for concern, which is widely viewed as the key that could unlock the full potential of computational fluvial hydraulics. Most of the models presently used to study fluvial basin filling are of the "diffusion type" (Flemmings and Jordan, 1989). It must be noted that this type of models do not assume that the sediment transport is performed by a physical diffusive process. Rather they are synthetic models based on mass conservation. In the "synthesist" viewpoint (Tipper, 1992; Goldenfeld & Kadanoff, 1999; Werner, 1999 in Paola, 2000) the dynamics of complex systems may occur on many levels (time or space scales) and the dynamics of higher levels may be more or less independent of that at lower levels. In this type of models the low frequency dynamics is controlled by only a few important processes and the high frequency processes are not included. In opposition to this is the "reductionist" viewpoint that states that there is no objective reason to discard high frequency processes. In this viewpoint the system is broken down into its fundamental components and processes and the model is build up by selecting the important processes regardless of its time and space scale. This viewpoint was only possible to pursue in the recent years due to improvement in system knowledge and computer power (Paola, 2000). The primary aim of this paper is to demonstrate that it is possible to simulate the evolution of the sediment river bed, traditionally studied with synthetic models, with a process-based hydrodynamic, sediment transport and morphodynamic model, solving explicitly the mass and momentum conservation equations. With this objective, a comparison between two mathematical models for alluvial rivers is made to simulate the evolution of the sediment river bed of a conceptual 1D embayment for periods in the order of a thousand years: the traditional synthetic basin infilling aggregate diffusive type model based on the diffusion equation (Paola, 2000), used in the "synthesist" viewpoint and the process-based model MOHID (Miranda et al., 2000). The simulation of the sediment river bed evolution achieved by the process-based model MOHID is very similar to those obtained by the diffusive type model, but more complete due to the complexity of the process-based model. In the MOHID results it is possible to observe a more comprehensive and realistic results because this type of model include processes that is impossible to a synthetic model to describe. At last the combined effect of tide, sea level rise and river discharges was investigated in the process based model. These effects cannot be simulated using the diffusive type model. The results demonstrate the feasibility of using process based models to perform studies in scales of 10000 years. This is an advance relative to the use of synthetic models, enabling the use of variable forcing. REFERENCES • Briggs, L.I. and Pollack, H.N., 1967. Digital model of evaporate sedimentation. Science, 155, 453-456. • Flemmings, P.B. and Jordan, T.E., 1989. A synthetic stratigraphic model of foreland basin development. J. Geophys. Res., 94, 3851-3866. • Miranda, R., Braunschweig, F., Leitão, P., Neves, R., Martins, F. & Santos A., 2000. MOHID 2000 - A coastal integrated object oriented model. Proc. Hydraulic Engineering Software VIII, Lisbon, 2000, 393-401, Ed. W.R. Blain & C.A. Brebbia, WITpress. • Paola, C., 2000. Quantitative models of sedimentary basin filing. Sedimentology, 47, 121-178. • Schwarzacher, W., 1966. Sedimentation in a subsiding basin. Nature, 5043, 1349-1350. ACKNOWLEDGMENTS This work was supported by the EVEDUS PTDC/CLI/68488/2006 Research Project

A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Komasi, Mehdi

2013-05-01

This paper demonstrates the potential use of Artificial Intelligence (AI) techniques for predicting daily runoff at multiple gauging stations. Uncertainty and complexity of the rainfall-runoff process due to its variability in space and time in one hand and lack of historical data on the other hand, cause difficulties in the spatiotemporal modeling of the process. In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy Inference System (IGANFIS) model conjugated with C-means clustering algorithm was used for rainfall-runoff modeling at multiple stations of the Eel River watershed, California. The proposed model could be used for predicting runoff in the stations with lack of data or any sub-basin within the watershed because of employing the spatial and temporal variables of the sub-basins as the model inputs. This ability of the integrated model for spatiotemporal modeling of the process was examined through the cross validation technique for a station. In this way, different ANFIS structures were trained using Sugeno algorithm in order to estimate daily discharge values at different stations. In order to improve the model efficiency, the input data were then classified into some clusters by the means of fuzzy C-means (FCMs) method. The goodness-of-fit measures support the gainful use of the IGANFIS and FCM methods in spatiotemporal modeling of hydrological processes.

Upscaling from research watersheds: an essential stage of trustworthy general-purpose hydrologic model building

NASA Astrophysics Data System (ADS)

McNamara, J. P.; Semenova, O.; Restrepo, P. J.

2011-12-01

Highly instrumented research watersheds provide excellent opportunities for investigating hydrologic processes. A danger, however, is that the processes observed at a particular research watershed are too specific to the watershed and not representative even of the larger scale watershed that contains that particular research watershed. Thus, models developed based on those partial observations may not be suitable for general hydrologic use. Therefore demonstrating the upscaling of hydrologic process from research watersheds to larger watersheds is essential to validate concepts and test model structure. The Hydrograph model has been developed as a general-purpose process-based hydrologic distributed system. In its applications and further development we evaluate the scaling of model concepts and parameters in a wide range of hydrologic landscapes. All models, either lumped or distributed, are based on a discretization concept. It is common practice that watersheds are discretized into so called hydrologic units or hydrologic landscapes possessing assumed homogeneous hydrologic functioning. If a model structure is fixed, the difference in hydrologic functioning (difference in hydrologic landscapes) should be reflected by a specific set of model parameters. Research watersheds provide the possibility for reasonable detailed combining of processes into some typical hydrologic concept such as hydrologic units, hydrologic forms, and runoff formation complexes in the Hydrograph model. And here by upscaling we imply not the upscaling of a single process but upscaling of such unified hydrologic functioning. The simulation of runoff processes for the Dry Creek research watershed, Idaho, USA (27 km2) was undertaken using the Hydrograph model. The information on the watershed was provided by Boise State University and included a GIS database of watershed characteristics and a detailed hydrometeorological observational dataset. The model provided good simulation results in terms of runoff and variable states of soil and snow over a simulation period 2000 - 2009. The parameters of the model were hand-adjusted based on rational sense, observational data and available understanding of underlying processes. For the first run some processes as riparian vegetation impact on runoff and streamflow/groundwater interaction were handled in a conceptual way. It was shown that the use of Hydrograph model which requires modest amount of parameter calibration may serve also as a quality control for observations. Based on the obtained parameters values and process understanding at the research watershed the model was applied to the larger scale watersheds located in similar environment - the Boise River at South Fork (1660 km2) and Twin Springs (2155 km2). The evaluation of the results of such upscaling will be presented.

Modeling of thermal processes arising during shaping gears with internal non-involute teeth

NASA Astrophysics Data System (ADS)

Kanatnikov, N. V.; Kanatnikova, P. A.; Vlasov, V. V.; Pashmentova, A. S.

2018-03-01

The paper presents a model for predicting the thermal processes arising during shaping gears with internal non-involute teeth. The kinematics of cutting is modeled due to the analytical model. Chipping is modeled using the finite element method. The experiment is based on the method of infrared photography of the cutting process. The simulation results showed that the maximum temperatures and heat flows in the tool vary by more than 10% when the rake and clearance angels of the cutting are changed.

Semantic Service Design for Collaborative Business Processes in Internetworked Enterprises

NASA Astrophysics Data System (ADS)

Bianchini, Devis; Cappiello, Cinzia; de Antonellis, Valeria; Pernici, Barbara

Modern collaborating enterprises can be seen as borderless organizations whose processes are dynamically transformed and integrated with the ones of their partners (Internetworked Enterprises, IE), thus enabling the design of collaborative business processes. The adoption of Semantic Web and service-oriented technologies for implementing collaboration in such distributed and heterogeneous environments promises significant benefits. IE can model their own processes independently by using the Software as a Service paradigm (SaaS). Each enterprise maintains a catalog of available services and these can be shared across IE and reused to build up complex collaborative processes. Moreover, each enterprise can adopt its own terminology and concepts to describe business processes and component services. This brings requirements to manage semantic heterogeneity in process descriptions which are distributed across different enterprise systems. To enable effective service-based collaboration, IEs have to standardize their process descriptions and model them through component services using the same approach and principles. For enabling collaborative business processes across IE, services should be designed following an homogeneous approach, possibly maintaining a uniform level of granularity. In the paper we propose an ontology-based semantic modeling approach apt to enrich and reconcile semantics of process descriptions to facilitate process knowledge management and to enable semantic service design (by discovery, reuse and integration of process elements/constructs). The approach brings together Semantic Web technologies, techniques in process modeling, ontology building and semantic matching in order to provide a comprehensive semantic modeling framework.

Corrective emotional experience in an integrative affect-focused therapy: Building a preliminary model using task analysis.

PubMed

Nakamura, Kaori; Iwakabe, Shigeru

2018-03-01

The present study constructed a preliminary process model of corrective emotional experience (CEE) in an integrative affect-focused therapy. Task analysis was used to analyse 6 in-session events taken from 6 Japanese clients who worked with an integrative affect-focused therapist. The 6 events included 3 successful CEEs and 3 partially successful CEEs for comparison. A rational-empirical model of CEE was generated, which consisted of two parallel client change processes, intrapersonal change and interpersonal change, and the therapist interventions corresponding to each process. Therapist experiential interventions and therapist affirmation facilitated both intrapersonal and interpersonal change processes, whereas his relational interventions were associated with the interpersonal change process. The partially successful CEEs were differentiated by the absence of the component of core painful emotions or negative beliefs in intrapersonal change process, which seemed crucial for the interpersonal change process to develop. CEE is best represented by a preliminary model that depicts two parallel yet interacting change processes. Intrapersonal change process is similar to the sequence of change described by the emotional processing model (Pascual-Leone & Greenberg, ), whereas interpersonal change process is a unique contribution of this study. Interpersonal change process was facilitated when the therapist's active stance and use of immediacy responses to make their relational process explicit allowed a shared exploration. Therapist affirmation bridged intrapersonal change to interpersonal change by promoting an adaptive sense of self in clients and forging a deeper emotional connection between the two. Copyright © 2017 John Wiley & Sons, Ltd.

Parallelization of a hydrological model using the message passing interface

USGS Publications Warehouse

Wu, Yiping; Li, Tiejian; Sun, Liqun; Chen, Ji

2013-01-01

With the increasing knowledge about the natural processes, hydrological models such as the Soil and Water Assessment Tool (SWAT) are becoming larger and more complex with increasing computation time. Additionally, other procedures such as model calibration, which may require thousands of model iterations, can increase running time and thus further reduce rapid modeling and analysis. Using the widely-applied SWAT as an example, this study demonstrates how to parallelize a serial hydrological model in a Windows® environment using a parallel programing technology—Message Passing Interface (MPI). With a case study, we derived the optimal values for the two parameters (the number of processes and the corresponding percentage of work to be distributed to the master process) of the parallel SWAT (P-SWAT) on an ordinary personal computer and a work station. Our study indicates that model execution time can be reduced by 42%–70% (or a speedup of 1.74–3.36) using multiple processes (two to five) with a proper task-distribution scheme (between the master and slave processes). Although the computation time cost becomes lower with an increasing number of processes (from two to five), this enhancement becomes less due to the accompanied increase in demand for message passing procedures between the master and all slave processes. Our case study demonstrates that the P-SWAT with a five-process run may reach the maximum speedup, and the performance can be quite stable (fairly independent of a project size). Overall, the P-SWAT can help reduce the computation time substantially for an individual model run, manual and automatic calibration procedures, and optimization of best management practices. In particular, the parallelization method we used and the scheme for deriving the optimal parameters in this study can be valuable and easily applied to other hydrological or environmental models.

Integration of a three-dimensional process-based hydrological model into the Object Modeling System

USDA-ARS?s Scientific Manuscript database

The integration of a spatial process model into an environmental modelling framework can enhance the model’s capabilities. We present the integration of the GEOtop model into the Object Modeling System (OMS) version 3.0 and illustrate its application in a small watershed. GEOtop is a physically base...

Unified Modeling Language (UML) for hospital-based cancer registration processes.

PubMed

Shiki, Naomi; Ohno, Yuko; Fujii, Ayumi; Murata, Taizo; Matsumura, Yasushi

2008-01-01

Hospital-based cancer registry involves complex processing steps that span across multiple departments. In addition, management techniques and registration procedures differ depending on each medical facility. Establishing processes for hospital-based cancer registry requires clarifying specific functions and labor needed. In recent years, the business modeling technique, in which management evaluation is done by clearly spelling out processes and functions, has been applied to business process analysis. However, there are few analytical reports describing the applications of these concepts to medical-related work. In this study, we initially sought to model hospital-based cancer registration processes using the Unified Modeling Language (UML), to clarify functions. The object of this study was the cancer registry of Osaka University Hospital. We organized the hospital-based cancer registration processes based on interview and observational surveys, and produced an As-Is model using activity, use-case, and class diagrams. After drafting every UML model, it was fed-back to practitioners to check its validity and improved. We were able to define the workflow for each department using activity diagrams. In addition, by using use-case diagrams we were able to classify each department within the hospital as a system, and thereby specify the core processes and staff that were responsible for each department. The class diagrams were effective in systematically organizing the information to be used for hospital-based cancer registries. Using UML modeling, hospital-based cancer registration processes were broadly classified into three separate processes, namely, registration tasks, quality control, and filing data. An additional 14 functions were also extracted. Many tasks take place within the hospital-based cancer registry office, but the process of providing information spans across multiple departments. Moreover, additional tasks were required in comparison to using a standardized system because the hospital-based cancer registration system was constructed with the pre-existing computer system in Osaka University Hospital. Difficulty of utilization of useful information for cancer registration processes was shown to increase the task workload. By using UML, we were able to clarify functions and extract the typical processes for a hospital-based cancer registry. Modeling can provide a basis of process analysis for establishment of efficient hospital-based cancer registration processes in each institute.

An approach to developing an integrated pyroprocessing simulator

NASA Astrophysics Data System (ADS)

Lee, Hyo Jik; Ko, Won Il; Choi, Sung Yeol; Kim, Sung Ki; Kim, In Tae; Lee, Han Soo

2014-02-01

Pyroprocessing has been studied for a decade as one of the promising fuel recycling options in Korea. We have built a pyroprocessing integrated inactive demonstration facility (PRIDE) to assess the feasibility of integrated pyroprocessing technology and scale-up issues of the processing equipment. Even though such facility cannot be replaced with a real integrated facility using spent nuclear fuel (SF), many insights can be obtained in terms of the world's largest integrated pyroprocessing operation. In order to complement or overcome such limited test-based research, a pyroprocessing Modelling and simulation study began in 2011. The Korea Atomic Energy Research Institute (KAERI) suggested a Modelling architecture for the development of a multi-purpose pyroprocessing simulator consisting of three-tiered models: unit process, operation, and plant-level-model. The unit process model can be addressed using governing equations or empirical equations as a continuous system (CS). In contrast, the operation model describes the operational behaviors as a discrete event system (DES). The plant-level model is an integrated model of the unit process and an operation model with various analysis modules. An interface with different systems, the incorporation of different codes, a process-centered database design, and a dynamic material flow are discussed as necessary components for building a framework of the plant-level model. As a sample model that contains methods decoding the above engineering issues was thoroughly reviewed, the architecture for building the plant-level-model was verified. By analyzing a process and operation-combined model, we showed that the suggested approach is effective for comprehensively understanding an integrated dynamic material flow. This paper addressed the current status of the pyroprocessing Modelling and simulation activity at KAERI, and also predicted its path forward.

An approach to developing an integrated pyroprocessing simulator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Hyo Jik; Ko, Won Il; Choi, Sung Yeol

Pyroprocessing has been studied for a decade as one of the promising fuel recycling options in Korea. We have built a pyroprocessing integrated inactive demonstration facility (PRIDE) to assess the feasibility of integrated pyroprocessing technology and scale-up issues of the processing equipment. Even though such facility cannot be replaced with a real integrated facility using spent nuclear fuel (SF), many insights can be obtained in terms of the world's largest integrated pyroprocessing operation. In order to complement or overcome such limited test-based research, a pyroprocessing Modelling and simulation study began in 2011. The Korea Atomic Energy Research Institute (KAERI) suggestedmore » a Modelling architecture for the development of a multi-purpose pyroprocessing simulator consisting of three-tiered models: unit process, operation, and plant-level-model. The unit process model can be addressed using governing equations or empirical equations as a continuous system (CS). In contrast, the operation model describes the operational behaviors as a discrete event system (DES). The plant-level model is an integrated model of the unit process and an operation model with various analysis modules. An interface with different systems, the incorporation of different codes, a process-centered database design, and a dynamic material flow are discussed as necessary components for building a framework of the plant-level model. As a sample model that contains methods decoding the above engineering issues was thoroughly reviewed, the architecture for building the plant-level-model was verified. By analyzing a process and operation-combined model, we showed that the suggested approach is effective for comprehensively understanding an integrated dynamic material flow. This paper addressed the current status of the pyroprocessing Modelling and simulation activity at KAERI, and also predicted its path forward.« less

Using lab notebooks to examine students' engagement in modeling in an upper-division electronics lab course

NASA Astrophysics Data System (ADS)

Stanley, Jacob T.; Su, Weifeng; Lewandowski, H. J.

2017-12-01

We demonstrate how students' use of modeling can be examined and assessed using student notebooks collected from an upper-division electronics lab course. The use of models is a ubiquitous practice in undergraduate physics education, but the process of constructing, testing, and refining these models is much less common. We focus our attention on a lab course that has been transformed to engage students in this modeling process during lab activities. The design of the lab activities was guided by a framework that captures the different components of model-based reasoning, called the Modeling Framework for Experimental Physics. We demonstrate how this framework can be used to assess students' written work and to identify how students' model-based reasoning differed from activity to activity. Broadly speaking, we were able to identify the different steps of students' model-based reasoning and assess the completeness of their reasoning. Varying degrees of scaffolding present across the activities had an impact on how thoroughly students would engage in the full modeling process, with more scaffolded activities resulting in more thorough engagement with the process. Finally, we identified that the step in the process with which students had the most difficulty was the comparison between their interpreted data and their model prediction. Students did not use sufficiently sophisticated criteria in evaluating such comparisons, which had the effect of halting the modeling process. This may indicate that in order to engage students further in using model-based reasoning during lab activities, the instructor needs to provide further scaffolding for how students make these types of experimental comparisons. This is an important design consideration for other such courses attempting to incorporate modeling as a learning goal.

Collaborative Research: Process-resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Ming; Deng, Yi

2015-02-06

El Niño-Southern Oscillation (ENSO) and Annular Modes (AMs) represent respectively the most important modes of low frequency variability in the tropical and extratropical circulations. The future projection of the ENSO and AM variability, however, remains highly uncertain with the state-of-the-art coupled general circulation models. A comprehensive understanding of the factors responsible for the inter-model discrepancies in projecting future changes in the ENSO and AM variability, in terms of multiple feedback processes involved, has yet to be achieved. The proposed research aims to identify sources of such uncertainty and establish a set of process-resolving quantitative evaluations of the existing predictions ofmore » the future ENSO and AM variability. The proposed process-resolving evaluations are based on a feedback analysis method formulated in Lu and Cai (2009), which is capable of partitioning 3D temperature anomalies/perturbations into components linked to 1) radiation-related thermodynamic processes such as cloud and water vapor feedbacks, 2) local dynamical processes including convection and turbulent/diffusive energy transfer and 3) non-local dynamical processes such as the horizontal energy transport in the oceans and atmosphere. Taking advantage of the high-resolution, multi-model ensemble products from the Coupled Model Intercomparison Project Phase 5 (CMIP5) soon to be available at the Lawrence Livermore National Lab, we will conduct a process-resolving decomposition of the global three-dimensional (3D) temperature (including SST) response to the ENSO and AM variability in the preindustrial, historical and future climate simulated by these models. Specific research tasks include 1) identifying the model-observation discrepancies in the global temperature response to ENSO and AM variability and attributing such discrepancies to specific feedback processes, 2) delineating the influence of anthropogenic radiative forcing on the key feedback processes operating on ENSO and AM variability and quantifying their relative contributions to the changes in the temperature anomalies associated with different phases of ENSO and AMs, and 3) investigating the linkages between model feedback processes that lead to inter-model differences in time-mean temperature projection and model feedback processes that cause inter-model differences in the simulated ENSO and AM temperature response. Through a thorough model-observation and inter-model comparison of the multiple energetic processes associated with ENSO and AM variability, the proposed research serves to identify key uncertainties in model representation of ENSO and AM variability, and investigate how the model uncertainty in predicting time-mean response is related to the uncertainty in predicting response of the low-frequency modes. The proposal is thus a direct response to the first topical area of the solicitation: Interaction of Climate Change and Low Frequency Modes of Natural Climate Variability. It ultimately supports the accomplishment of the BER climate science activity Long Term Measure (LTM): "Deliver improved scientific data and models about the potential response of the Earth's climate and terrestrial biosphere to increased greenhouse gas levels for policy makers to determine safe levels of greenhouse gases in the atmosphere."« less

A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes

PubMed Central

2015-01-01

Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disregarding from literature, we present a mixed Generalized Renewal Processes approach involving Kijima Type I and II models, allowing one to infer the impact of distinct interventions on the performance of the system under study. The first and second theoretical moments of this model are introduced as well as its maximum likelihood estimation and random sampling approaches. In order to illustrate the usefulness of the proposed Weibull-based Generalized Renewal Processes model, some real data sets involving improving, stable, and deteriorating systems are used. PMID:26197222

Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

NASA Technical Reports Server (NTRS)

Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

1992-01-01

In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

Fractal modeling of fluidic leakage through metal sealing surfaces

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Chen, Xiaoqian; Huang, Yiyong; Chen, Yong

2018-04-01

This paper investigates the fluidic leak rate through metal sealing surfaces by developing fractal models for the contact process and leakage process. An improved model is established to describe the seal-contact interface of two metal rough surface. The contact model divides the deformed regions by classifying the asperities of different characteristic lengths into the elastic, elastic-plastic and plastic regimes. Using the improved contact model, the leakage channel under the contact surface is mathematically modeled based on the fractal theory. The leakage model obtains the leak rate using the fluid transport theory in porous media, considering that the pores-forming percolation channels can be treated as a combination of filled tortuous capillaries. The effects of fractal structure, surface material and gasket size on the contact process and leakage process are analyzed through numerical simulations for sealed ring gaskets.

Model Uncertainty Quantification Methods For Data Assimilation In Partially Observed Multi-Scale Systems

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; van Leeuwen, P. J.

2017-12-01

Model Uncertainty Quantification remains one of the central challenges of effective Data Assimilation (DA) in complex partially observed non-linear systems. Stochastic parameterization methods have been proposed in recent years as a means of capturing the uncertainty associated with unresolved sub-grid scale processes. Such approaches generally require some knowledge of the true sub-grid scale process or rely on full observations of the larger scale resolved process. We present a methodology for estimating the statistics of sub-grid scale processes using only partial observations of the resolved process. It finds model error realisations over a training period by minimizing their conditional variance, constrained by available observations. Special is that these realisations are binned conditioned on the previous model state during the minimization process, allowing for the recovery of complex error structures. The efficacy of the approach is demonstrated through numerical experiments on the multi-scale Lorenz 96' model. We consider different parameterizations of the model with both small and large time scale separations between slow and fast variables. Results are compared to two existing methods for accounting for model uncertainty in DA and shown to provide improved analyses and forecasts.

The Spectrum of Cinema.

ERIC Educational Resources Information Center

O'Grady, Gerald

Cinema emerged about 1900, and as the twentieth century ticked by, the cinematic process was recognized as a model for the thought processes of the human mind--of both the unconscious dream process and of the stream of conscious thought--and also as a model of the historical process. Film not only is an experiential process and a physical…

Conceptual IT model

NASA Astrophysics Data System (ADS)

Arnaoudova, Kristina; Stanchev, Peter

2015-11-01

The business processes are the key asset for every organization. The design of the business process models is the foremost concern and target among an organization's functions. Business processes and their proper management are intensely dependent on the performance of software applications and technology solutions. The paper is attempt for definition of new Conceptual model of IT service provider, it could be examined as IT focused Enterprise model, part of Enterprise Architecture (EA) school.

Variational estimation of process parameters in a simplified atmospheric general circulation model

NASA Astrophysics Data System (ADS)

Lv, Guokun; Koehl, Armin; Stammer, Detlef

2016-04-01

Parameterizations are used to simulate effects of unresolved sub-grid-scale processes in current state-of-the-art climate model. The values of the process parameters, which determine the model's climatology, are usually manually adjusted to reduce the difference of model mean state to the observed climatology. This process requires detailed knowledge of the model and its parameterizations. In this work, a variational method was used to estimate process parameters in the Planet Simulator (PlaSim). The adjoint code was generated using automatic differentiation of the source code. Some hydrological processes were switched off to remove the influence of zero-order discontinuities. In addition, the nonlinearity of the model limits the feasible assimilation window to about 1day, which is too short to tune the model's climatology. To extend the feasible assimilation window, nudging terms for all state variables were added to the model's equations, which essentially suppress all unstable directions. In identical twin experiments, we found that the feasible assimilation window could be extended to over 1-year and accurate parameters could be retrieved. Although the nudging terms transform to a damping of the adjoint variables and therefore tend to erases the information of the data over time, assimilating climatological information is shown to provide sufficient information on the parameters. Moreover, the mechanism of this regularization is discussed.

Measuring and modelling the structure of chocolate

NASA Astrophysics Data System (ADS)

Le Révérend, Benjamin J. D.; Fryer, Peter J.; Smart, Ian; Bakalis, Serafim

2015-01-01

The cocoa butter present in chocolate exists as six different polymorphs. To achieve the desired crystal form (βV), traditional chocolate manufacturers use relatively slow cooling (<2°C/min). A newer generation of rapid cooling systems has been suggested requiring further understanding of fat crystallisation. To allow better control and understanding of these processes and newer rapid cooling processes, it is necessary to understand both heat transfer and crystallization kinetics. The proposed model aims to predict the temperature in the chocolate products during processing as well as the crystal structure of cocoa butter throughout the process. A set of ordinary differential equations describes the kinetics of fat crystallisation. The parameters were obtained by fitting the model to a set of DSC curves. The heat transfer equations were coupled to the kinetic model and solved using commercially available CFD software. A method using single crystal XRD was developed using a novel subtraction method to quantify the cocoa butter structure in chocolate directly and results were compared to the ones predicted from the model. The model was proven to predict phase change temperature during processing accurately (±1°C). Furthermore, it was possible to correctly predict phase changes and polymorphous transitions. The good agreement between the model and experimental data on the model geometry allows a better design and control of industrial processes.

Arbitrage with fractional Gaussian processes

NASA Astrophysics Data System (ADS)

Zhang, Xili; Xiao, Weilin

2017-04-01

While the arbitrage opportunity in the Black-Scholes model driven by fractional Brownian motion has a long history, the arbitrage strategy in the Black-Scholes model driven by general fractional Gaussian processes is in its infancy. The development of stochastic calculus with respect to fractional Gaussian processes allowed us to study such models. In this paper, following the idea of Shiryaev (1998), an arbitrage strategy is constructed for the Black-Scholes model driven by fractional Gaussian processes, when the stochastic integral is interpreted in the Riemann-Stieltjes sense. Arbitrage opportunities in some fractional Gaussian processes, including fractional Brownian motion, sub-fractional Brownian motion, bi-fractional Brownian motion, weighted-fractional Brownian motion and tempered fractional Brownian motion, are also investigated.

LANDPLANER (LANDscape, Plants, LANdslide and ERosion): a model to describe the dynamic response of slopes (or basins) under different changing scenarios

NASA Astrophysics Data System (ADS)

Rossi, Mauro; Torri, Dino; Santi, Elisa; Bacaro, Giovanni; Marchesini, Ivan

2014-05-01

Landslide phenomena and erosion processes are widespread and cause every year extensive damages to the environment and sensible reduction of ecosystem services. These processes are in competition among them, and their complex interaction control the landscapes evolution. Landslide phenomena and erosion processes can be strongly influenced by land use, vegetation, soil characteristics and anthropic actions. Such type of phenomena are mainly model separately using empirical and physically based approaches. The former rely upon the identification of simple empirical laws correlating/relating the occurrence of instability processes to some of their potential causes. The latter are based on physical descriptions of the processes, and depending on the degree of complexity they can integrate different variables characterizing the process and their trigger. Those model often couple an hydrological model with an erosion or a landslide model. The spatial modeling schemas are heterogeneous, but mostly the raster (i.e. matrices of data) or the conceptual (i.e. cascading planes and channels) description of the terrain are used. The two model types are generally designed and applied at different scales. Empirical models, less demanding in terms of input data cannot consider explicitly the real process triggering mechanisms and commonly they are exploited to assess the potential occurrence of instability phenomena over large areas (small scale assessment). Physically-based models are high-demanding in term of input data, difficult to obtain over large areas if not with large uncertainty, and their applicability is often limited to small catchments or single slopes (large scale assessment). More those models, even if physically-based, are simplified description of the instability processes and can neglect significant issues of the real triggering mechanisms. For instance the influence of vegetation has been considered just partially. Although in the literature a variety of model approaches have been proposed to model separately landslide and erosion processes, only few attempts were made to model both jointly, mostly integrating pre-existing models. To overcome this limitation we develop a new model called LANDPLANER (LANDscape, Plants, LANdslide and ERosion), specifically design to describe the dynamic response of slopes (or basins) under different changing scenarios including: (i) changes of meteorological factors, (ii) changes of vegetation or land-use, (iii) and changes of slope morphology. The was applied in different study area in order to check its basic assumptions, and to test its general operability and applicability. Results show a reasonable model behaviors and confirm its easy applicability in real cases.

Evaluating Process Improvement Courses of Action Through Modeling and Simulation

DTIC Science & Technology

2017-09-16

changes to a process is time consuming and has potential to overlook stochastic effects. By modeling a process as a Numerical Design Structure Matrix...13 Methods to Evaluate Process Performance ................................................................15 The Design Structure...Matrix ......................................................................................16 Numerical Design Structure Matrix

An Information-Processing Model of Crisis Management.

ERIC Educational Resources Information Center

Egelhoff, William G.; Sen, Falguni

1992-01-01

Develops a contingency model for managing a variety of corporate crises. Views crisis management as an information-processing situation and organizations that must cope with crisis as information-processing systems. Attempts to fit appropriate information-processing mechanisms to different categories of crises. (PRA)

Service Oriented Architecture for Coast Guard Command and Control

DTIC Science & Technology

2007-03-01

Operations BPEL4WS The Business Process Execution Language for Web Services BPMN Business Process Modeling Notation CASP Computer Aided Search Planning...Business Process Modeling Notation ( BPMN ) provides a standardized graphical notation for drawing business processes in a workflow. Software tools

Case Studies in Modelling, Control in Food Processes.

PubMed

Glassey, J; Barone, A; Montague, G A; Sabou, V

This chapter discusses the importance of modelling and control in increasing food process efficiency and ensuring product quality. Various approaches to both modelling and control in food processing are set in the context of the specific challenges in this industrial sector and latest developments in each area are discussed. Three industrial case studies are used to demonstrate the benefits of advanced measurement, modelling and control in food processes. The first case study illustrates the use of knowledge elicitation from expert operators in the process for the manufacture of potato chips (French fries) and the consequent improvements in process control to increase the consistency of the resulting product. The second case study highlights the economic benefits of tighter control of an important process parameter, moisture content, in potato crisp (chips) manufacture. The final case study describes the use of NIR spectroscopy in ensuring effective mixing of dry multicomponent mixtures and pastes. Practical implementation tips and infrastructure requirements are also discussed.

Analytical and regression models of glass rod drawing process

NASA Astrophysics Data System (ADS)

Alekseeva, L. B.

2018-03-01

The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.

Managing the Drafting Process: Creating a New Model for the Workplace.

ERIC Educational Resources Information Center

Shwom, Barbara L.; Hirsch, Penny L.

1994-01-01

Discusses the development of a pragmatic model of the writing process in the workplace, focusing on the importance of "drafting" as part of that process. Discusses writers' attitudes about drafting and the structures of the workplace that drafting has to accommodate. Introduces a drafting model and discusses results of using this model…

Modeling Basic Writing Processes from Keystroke Logs

ERIC Educational Resources Information Center

Guo, Hongwen; Deane, Paul D.; van Rijn, Peter W.; Zhang, Mo; Bennett, Randy E.

2018-01-01

The goal of this study is to model pauses extracted from writing keystroke logs as a way of characterizing the processes students use in essay composition. Low-level timing data were modeled, the interkey interval and its subtype, the intraword duration, thought to reflect processes associated with keyboarding skills and composition fluency.…

A REVIEW AND COMPARISON OF MODELS FOR PREDICTING DYNAMIC CHEMICAL BIOCONCENTRATION IN FISH

EPA Science Inventory

Over the past 20 years, a variety of models have been developed to simulate the bioconcentration of hydrophobic organic chemicals by fish. These models differ not only in the processes they address but also in the way a given process is described. Processes described by these m...

Investigating the Representational Fluency of Pre-Service Mathematics Teachers in a Modelling Process

ERIC Educational Resources Information Center

Delice, Ali; Kertil, Mahmut

2015-01-01

This article reports the results of a study that investigated pre-service mathematics teachers' modelling processes in terms of representational fluency in a modelling activity related to a cassette player. A qualitative approach was used in the data collection process. Students' individual and group written responses to the mathematical modelling…

Modeling and Analysis of Power Processing Systems. [use of a digital computer for designing power plants

NASA Technical Reports Server (NTRS)

Fegley, K. A.; Hayden, J. H.; Rehmann, D. W.

1974-01-01

The feasibility of formulating a methodology for the modeling and analysis of aerospace electrical power processing systems is investigated. It is shown that a digital computer may be used in an interactive mode for the design, modeling, analysis, and comparison of power processing systems.

Motivation Monitoring and Assessment Extension for Input-Process-Outcome Game Model

ERIC Educational Resources Information Center

Ghergulescu, Ioana; Muntean, Cristina Hava

2014-01-01

This article proposes a Motivation Assessment-oriented Input-Process-Outcome Game Model (MotIPO), which extends the Input-Process-Outcome game model with game-centred and player-centred motivation assessments performed right from the beginning of the game-play. A feasibility case-study involving 67 participants playing an educational game and…

Software Engineering Laboratory (SEL) cleanroom process model

NASA Technical Reports Server (NTRS)

Green, Scott; Basili, Victor; Godfrey, Sally; Mcgarry, Frank; Pajerski, Rose; Waligora, Sharon

1991-01-01

The Software Engineering Laboratory (SEL) cleanroom process model is described. The term 'cleanroom' originates in the integrated circuit (IC) production process, where IC's are assembled in dust free 'clean rooms' to prevent the destructive effects of dust. When applying the clean room methodology to the development of software systems, the primary focus is on software defect prevention rather than defect removal. The model is based on data and analysis from previous cleanroom efforts within the SEL and is tailored to serve as a guideline in applying the methodology to future production software efforts. The phases that are part of the process model life cycle from the delivery of requirements to the start of acceptance testing are described. For each defined phase, a set of specific activities is discussed, and the appropriate data flow is described. Pertinent managerial issues, key similarities and differences between the SEL's cleanroom process model and the standard development approach used on SEL projects, and significant lessons learned from prior cleanroom projects are presented. It is intended that the process model described here will be further tailored as additional SEL cleanroom projects are analyzed.

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

NASA Astrophysics Data System (ADS)

Rolchigo, Matthew R.; Mendoza, Michael Y.; Samimi, Peyman; Brice, David A.; Martin, Brian; Collins, Peter C.; LeSar, Richard

2017-07-01

Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuum-level description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper.

Multilevel modeling of damage accumulation processes in metals

NASA Astrophysics Data System (ADS)

Kurmoiartseva, K. A.; Trusov, P. V.; Kotelnikova, N. V.

2017-12-01

To predict the behavior of components and constructions it is necessary to develop the methods and mathematical models which take into account the self-organization of microstructural processes and the strain localization. The damage accumulation processes and the evolution of material properties during deformation are important to take into account. The heterogeneity of the process of damage accumulation is due to the appropriate physical mechanisms at the scale levels, which are lower than the macro-level. The purpose of this work is to develop a mathematical model for analyzing the behavior of polycrystalline materials that allows describing the damage accumulation processes. Fracture is the multistage and multiscale process of the build-up of micro- and mesodefects over the wide range of loading rates. The formation of microcracks by mechanisms is caused by the interactions of the dislocations of different slip systems, barriers, boundaries and the inclusions of the secondary phase. This paper provides the description of some of the most well-known models of crack nucleation and also suggests the structure of a mathematical model based on crystal plasticity and dislocation models of crack nucleation.

The dual process model of coping with bereavement: rationale and description.

PubMed

Stroebe, M; Schut, H

1999-01-01

There are shortcomings in traditional theorizing about effective ways of coping with bereavement, most notably, with respect to the so-called "grief work hypothesis." Criticisms include imprecise definition, failure to represent dynamic processing that is characteristic of grieving, lack of empirical evidence and validation across cultures and historical periods, and a limited focus on intrapersonal processes and on health outcomes. Therefore, a revised model of coping with bereavement, the dual process model, is proposed. This model identifies two types of stressors, loss- and restoration-oriented, and a dynamic, regulatory coping process of oscillation, whereby the grieving individual at times confronts, at other times avoids, the different tasks of grieving. This model proposes that adaptive coping is composed of confrontation--avoidance of loss and restoration stressors. It also argues the need for dosage of grieving, that is, the need to take respite from dealing with either of these stressors, as an integral part of adaptive coping. Empirical research to support this conceptualization is discussed, and the model's relevance to the examination of complicated grief, analysis of subgroup phenomena, as well as interpersonal coping processes, is described.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

NASA Astrophysics Data System (ADS)

Clark, Martyn P.; Bierkens, Marc F. P.; Samaniego, Luis; Woods, Ross A.; Uijlenhoet, Remko; Bennett, Katrina E.; Pauwels, Valentijn R. N.; Cai, Xitian; Wood, Andrew W.; Peters-Lidard, Christa D.

2017-07-01

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

NASA Astrophysics Data System (ADS)

Clark, M. P.; Nijssen, B.; Wood, A.; Mizukami, N.; Newman, A. J.

2017-12-01

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

Microphysics in the Multi-Scale Modeling Systems with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the microphysics developments of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the heavy precipitation processes will be presented.

Design and Implementation of Hydrologic Process Knowledge-base Ontology: A case study for the Infiltration Process

NASA Astrophysics Data System (ADS)

Elag, M.; Goodall, J. L.

2013-12-01

Hydrologic modeling often requires the re-use and integration of models from different disciplines to simulate complex environmental systems. Component-based modeling introduces a flexible approach for integrating physical-based processes across disciplinary boundaries. Several hydrologic-related modeling communities have adopted the component-based approach for simulating complex physical systems by integrating model components across disciplinary boundaries in a workflow. However, it is not always straightforward to create these interdisciplinary models due to the lack of sufficient knowledge about a hydrologic process. This shortcoming is a result of using informal methods for organizing and sharing information about a hydrologic process. A knowledge-based ontology provides such standards and is considered the ideal approach for overcoming this challenge. The aims of this research are to present the methodology used in analyzing the basic hydrologic domain in order to identify hydrologic processes, the ontology itself, and how the proposed ontology is integrated with the Water Resources Component (WRC) ontology. The proposed ontology standardizes the definitions of a hydrologic process, the relationships between hydrologic processes, and their associated scientific equations. The objective of the proposed Hydrologic Process (HP) Ontology is to advance the idea of creating a unified knowledge framework for components' metadata by introducing a domain-level ontology for hydrologic processes. The HP ontology is a step toward an explicit and robust domain knowledge framework that can be evolved through the contribution of domain users. Analysis of the hydrologic domain is accomplished using the Formal Concept Approach (FCA), in which the infiltration process, an important hydrologic process, is examined. Two infiltration methods, the Green-Ampt and Philip's methods, were used to demonstrate the implementation of information in the HP ontology. Furthermore, a SPARQL service is provided for semantic-based querying of the ontology.

Identification of AR(I)MA processes for modelling temporal correlations of GPS observations

NASA Astrophysics Data System (ADS)

Luo, X.; Mayer, M.; Heck, B.

2009-04-01

In many geodetic applications observations of the Global Positioning System (GPS) are routinely processed by means of the least-squares method. However, this algorithm delivers reliable estimates of unknown parameters und realistic accuracy measures only if both the functional and stochastic models are appropriately defined within GPS data processing. One deficiency of the stochastic model used in many GPS software products consists in neglecting temporal correlations of GPS observations. In practice the knowledge of the temporal stochastic behaviour of GPS observations can be improved by analysing time series of residuals resulting from the least-squares evaluation. This paper presents an approach based on the theory of autoregressive (integrated) moving average (AR(I)MA) processes to model temporal correlations of GPS observations using time series of observation residuals. A practicable integration of AR(I)MA models in GPS data processing requires the determination of the order parameters of AR(I)MA processes at first. In case of GPS, the identification of AR(I)MA processes could be affected by various factors impacting GPS positioning results, e.g. baseline length, multipath effects, observation weighting, or weather variations. The influences of these factors on AR(I)MA identification are empirically analysed based on a large amount of representative residual time series resulting from differential GPS post-processing using 1-Hz observation data collected within the permanent SAPOS® (Satellite Positioning Service of the German State Survey) network. Both short and long time series are modelled by means of AR(I)MA processes. The final order parameters are determined based on the whole residual database; the corresponding empirical distribution functions illustrate that multipath and weather variations seem to affect the identification of AR(I)MA processes much more significantly than baseline length and observation weighting. Additionally, the modelling results of temporal correlations using high-order AR(I)MA processes are compared with those by means of first order autoregressive (AR(1)) processes and empirically estimated autocorrelation functions.

Mathematical modeling of heat treatment processes conserving biological activity of plant bioresources

NASA Astrophysics Data System (ADS)

Rodionova, N. S.; Popov, E. S.; Pozhidaeva, E. A.; Pynzar, S. S.; Ryaskina, L. O.

2018-05-01

The aim of this study is to develop a mathematical model of the heat exchange process of LT-processing to estimate the dynamics of temperature field changes and optimize the regime parameters, due to the non-stationarity process, the physicochemical and thermophysical properties of food systems. The application of LT-processing, based on the use of low-temperature modes in thermal culinary processing of raw materials with preliminary vacuum packaging in a polymer heat- resistant film is a promising trend in the development of technics and technology in the catering field. LT-processing application of food raw materials guarantees the preservation of biologically active substances in food environments, which are characterized by a certain thermolability, as well as extend the shelf life and high consumer characteristics of food systems that are capillary-porous bodies. When performing the mathematical modeling of the LT-processing process, the packet of symbolic mathematics “Maple” was used, as well as the mathematical packet flexPDE that uses the finite element method for modeling objects with distributed parameters. The processing of experimental results was evaluated with the help of the developed software in the programming language Python 3.4. To calculate and optimize the parameters of the LT processing process of polycomponent food systems, the differential equation of non-stationary thermal conductivity was used, the solution of which makes it possible to identify the temperature change at any point of the solid at different moments. The present study specifies data on the thermophysical characteristics of the polycomponent food system based on plant raw materials, with the help of which the physico-mathematical model of the LT- processing process has been developed. The obtained mathematical model allows defining of the dynamics of the temperature field in different sections of the LT-processed polycomponent food systems on the basis of calculating the evolution profiles of temperature fields, which enable one to analyze the efficiency of the regime parameters of heat treatment.

Figure-ground organization and object recognition processes: an interactive account.

PubMed

Vecera, S P; O'Reilly, R C

1998-04-01

Traditional bottom-up models of visual processing assume that figure-ground organization precedes object recognition. This assumption seems logically necessary: How can object recognition occur before a region is labeled as figure? However, some behavioral studies find that familiar regions are more likely to be labeled figure than less familiar regions, a problematic finding for bottom-up models. An interactive account is proposed in which figure-ground processes receive top-down input from object representations in a hierarchical system. A graded, interactive computational model is presented that accounts for behavioral results in which familiarity effects are found. The interactive model offers an alternative conception of visual processing to bottom-up models.

Modeling process of embolization arteriovenous malformation on the basis of two-phase filtration model

NASA Astrophysics Data System (ADS)

Cherevko, A. A.; Gologush, T. S.; Ostapenko, V. V.; Petrenko, I. A.; Chupakhin, A. P.

2016-06-01

Arteriovenous malformation is a chaotic disordered interlacement of very small diameter vessels, performing reset of blood from the artery into the vein. In this regard it can be adequately modeled using porous medium. In this model process of embolization described as penetration of non-adhesive substance ONYX into the porous medium, filled with blood, both of these fluids are not mixed with each other. In one-dimensional approximation such processes are well described by Buckley-Leverett equation. In this paper Buckley-Leverett equation is solved numerically by using a new modification of Cabaret scheme. The results of numerical modeling process of embolization of AVM are shown.

A simplified computational memory model from information processing.

PubMed

Zhang, Lanhua; Zhang, Dongsheng; Deng, Yuqin; Ding, Xiaoqian; Wang, Yan; Tang, Yiyuan; Sun, Baoliang

2016-11-23

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

A methodology proposal for collaborative business process elaboration using a model-driven approach

NASA Astrophysics Data System (ADS)

Mu, Wenxin; Bénaben, Frédérick; Pingaud, Hervé

2015-05-01

Business process management (BPM) principles are commonly used to improve processes within an organisation. But they can equally be applied to supporting the design of an Information System (IS). In a collaborative situation involving several partners, this type of BPM approach may be useful to support the design of a Mediation Information System (MIS), which would ensure interoperability between the partners' ISs (which are assumed to be service oriented). To achieve this objective, the first main task is to build a collaborative business process cartography. The aim of this article is to present a method for bringing together collaborative information and elaborating collaborative business processes from the information gathered (by using a collaborative situation framework, an organisational model, an informational model, a functional model and a metamodel and by using model transformation rules).

Physical Modeling of Contact Processes on the Cutting Tools Surfaces of STM When Turning

NASA Astrophysics Data System (ADS)

Belozerov, V. A.; Uteshev, M. H.

2016-08-01

This article describes how to create an optimization model of the process of fine turning of superalloys and steel tools from STM on CNC machines, flexible manufacturing units (GPM), machining centers. Creation of the optimization model allows you to link (unite) contact processes simultaneously on the front and back surfaces of the tool from STM to manage contact processes and the dynamic strength of the cutting tool at the top of the STM. Established optimization model of management of the dynamic strength of the incisors of the STM in the process of fine turning is based on a previously developed thermomechanical (physical, heat) model, which allows the system thermomechanical approach to choosing brands STM (domestic and foreign) for cutting tools from STM designed for fine turning of heat resistant alloys and steels.

A COMSOL-GEMS interface for modeling coupled reactive-transport geochemical processes

NASA Astrophysics Data System (ADS)

Azad, Vahid Jafari; Li, Chang; Verba, Circe; Ideker, Jason H.; Isgor, O. Burkan

2016-07-01

An interface was developed between COMSOL MultiphysicsTM finite element analysis software and (geo)chemical modeling platform, GEMS, for the reactive-transport modeling of (geo)chemical processes in variably saturated porous media. The two standalone software packages are managed from the interface that uses a non-iterative operator splitting technique to couple the transport (COMSOL) and reaction (GEMS) processes. The interface allows modeling media with complex chemistry (e.g. cement) using GEMS thermodynamic database formats. Benchmark comparisons show that the developed interface can be used to predict a variety of reactive-transport processes accurately. The full functionality of the interface was demonstrated to model transport processes, governed by extended Nernst-Plank equation, in Class H Portland cement samples in high pressure and temperature autoclaves simulating systems that are used to store captured carbon dioxide (CO2) in geological reservoirs.

Thorough specification of the neurophysiologic processes underlying behavior and of their manifestation in EEG - demonstration with the go/no-go task.

PubMed

Shahaf, Goded; Pratt, Hillel

2013-01-01

In this work we demonstrate the principles of a systematic modeling approach of the neurophysiologic processes underlying a behavioral function. The modeling is based upon a flexible simulation tool, which enables parametric specification of the underlying neurophysiologic characteristics. While the impact of selecting specific parameters is of interest, in this work we focus on the insights, which emerge from rather accepted assumptions regarding neuronal representation. We show that harnessing of even such simple assumptions enables the derivation of significant insights regarding the nature of the neurophysiologic processes underlying behavior. We demonstrate our approach in some detail by modeling the behavioral go/no-go task. We further demonstrate the practical significance of this simplified modeling approach in interpreting experimental data - the manifestation of these processes in the EEG and ERP literature of normal and abnormal (ADHD) function, as well as with comprehensive relevant ERP data analysis. In-fact we show that from the model-based spatiotemporal segregation of the processes, it is possible to derive simple and yet effective and theory-based EEG markers differentiating normal and ADHD subjects. We summarize by claiming that the neurophysiologic processes modeled for the go/no-go task are part of a limited set of neurophysiologic processes which underlie, in a variety of combinations, any behavioral function with measurable operational definition. Such neurophysiologic processes could be sampled directly from EEG on the basis of model-based spatiotemporal segregation.

Grief responses, coping processes, and social support of widows: research with Roy's model.

PubMed

Robinson, J H

1995-01-01

This ex post facto descriptive correlational design study of widows during their second year of bereavement utilizes Roy's adaptation model as a guiding framework. Contextual stimuli (social support, social network, income/education, spiritual beliefs) were related to the cognator function (coping process), which was related to adaptation outcome (grief response). Significant moderate positive relationships were found between social support and coping process, and between social network and coping process. A significant relationship was also found between coping process and grief response. The path model accounted for 18% explained variance.

Model-dependence of neutrino emissivities and neutrino luminosities of neutron stars from the direct Urca processes and the modified Urca processes

NASA Astrophysics Data System (ADS)

Yin, Peng; Fan, Xiaohua; Dong, Jianmin; Guo, Wenmei; Zuo, Wei

2017-05-01

The neutrino emissivities in β-stable neutron star matter from the direct Urca (DU) processes and the modified Urca (MU) processes have been investigated by adopting 26 Skyrme interactions. Several physical quantities related to the MU processes and the DU processes have been calculated and discussed. The model-dependence of the neutrino emissivities from the DU processes is found to stem mainly from the model-dependence of the effective mass, while the neutrino emissivities from the MU processes are determined by the competition between the effects of the symmetry energy and the effective mass. Besides, we have investigated the total neutrino luminosities of neutron stars, with the masses of 1.2 , 1.4 , 1.6 and 1.8M⊙, from the DU processes and the MU processes. The neutrino luminosity of a neutron star is found to be primarily determined by whether the electron DU process is allowed or not. As long as the electron DU process can occur, the total luminosity turns out to be 5 to 8 orders of magnitude larger as compared with the case that the DU process is forbidden, which indicates that the strongest model-dependence of the neutrino luminosity comes from that of the symmetry energy and the equation of state (EOS) of neutron star matter. In the case that the DU processes are allowed, the discrepancy of the calculated neutrino luminosity using various Skyrme interactions remains noticeable, which is essentially attributed to the model-dependence of the symmetry energy, the EOS of NS matter and the effective masses.

Multi-Hypothesis Modelling Capabilities for Robust Data-Model Integration

NASA Astrophysics Data System (ADS)

Walker, A. P.; De Kauwe, M. G.; Lu, D.; Medlyn, B.; Norby, R. J.; Ricciuto, D. M.; Rogers, A.; Serbin, S.; Weston, D. J.; Ye, M.; Zaehle, S.

2017-12-01

Large uncertainty is often inherent in model predictions due to imperfect knowledge of how to describe the mechanistic processes (hypotheses) that a model is intended to represent. Yet this model hypothesis uncertainty (MHU) is often overlooked or informally evaluated, as methods to quantify and evaluate MHU are limited. MHU is increased as models become more complex because each additional processes added to a model comes with inherent MHU as well as parametric unceratinty. With the current trend of adding more processes to Earth System Models (ESMs), we are adding uncertainty, which can be quantified for parameters but not MHU. Model inter-comparison projects do allow for some consideration of hypothesis uncertainty but in an ad hoc and non-independent fashion. This has stymied efforts to evaluate ecosystem models against data and intepret the results mechanistically because it is not simple to interpret exactly why a model is producing the results it does and identify which model assumptions are key as they combine models of many sub-systems and processes, each of which may be conceptualised and represented mathematically in various ways. We present a novel modelling framework—the multi-assumption architecture and testbed (MAAT)—that automates the combination, generation, and execution of a model ensemble built with different representations of process. We will present the argument that multi-hypothesis modelling needs to be considered in conjunction with other capabilities (e.g. the Predictive Ecosystem Analyser; PecAn) and statistical methods (e.g. sensitivity anaylsis, data assimilation) to aid efforts in robust data model integration to enhance our predictive understanding of biological systems.

A process-based model for cattle manure compost windrows: Model performance and application

USDA-ARS?s Scientific Manuscript database

A model was developed and incorporated in the Integrated Farm System Model (IFSM, v.4.3) that simulates important processes occurring during windrow composting of manure. The model, documented in an accompanying paper, predicts changes in windrow properties and conditions and the resulting emissions...

Turbulence Modeling: Progress and Future Outlook

NASA Technical Reports Server (NTRS)

Marvin, Joseph G.; Huang, George P.

1996-01-01

Progress in the development of the hierarchy of turbulence models for Reynolds-averaged Navier-Stokes codes used in aerodynamic applications is reviewed. Steady progress is demonstrated, but transfer of the modeling technology has not kept pace with the development and demands of the computational fluid dynamics (CFD) tools. An examination of the process of model development leads to recommendations for a mid-course correction involving close coordination between modelers, CFD developers, and application engineers. In instances where the old process is changed and cooperation enhanced, timely transfer is realized. A turbulence modeling information database is proposed to refine the process and open it to greater participation among modeling and CFD practitioners.

Process-oriented Observational Metrics for CMIP6 Climate Model Assessments

NASA Astrophysics Data System (ADS)

Jiang, J. H.; Su, H.

2016-12-01

Observational metrics based on satellite observations have been developed and effectively applied during post-CMIP5 model evaluation and improvement projects. As new physics and parameterizations continue to be included in models for the upcoming CMIP6, it is important to continue objective comparisons between observations and model results. This talk will summarize the process-oriented observational metrics and methodologies for constraining climate models with A-Train satellite observations and support CMIP6 model assessments. We target parameters and processes related to atmospheric clouds and water vapor, which are critically important for Earth's radiative budget, climate feedbacks, and water and energy cycles, and thus reduce uncertainties in climate models.

Modeling associations between latent event processes governing time series of pulsing hormones.

PubMed

Liu, Huayu; Carlson, Nichole E; Grunwald, Gary K; Polotsky, Alex J

2017-10-31

This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women. © 2017, The International Biometric Society.

Improving operational anodising process performance using simulation approach

NASA Astrophysics Data System (ADS)

Liong, Choong-Yeun; Ghazali, Syarah Syahidah

2015-10-01

The use of aluminium is very widespread, especially in transportation, electrical and electronics, architectural, automotive and engineering applications sectors. Therefore, the anodizing process is an important process for aluminium in order to make the aluminium durable, attractive and weather resistant. This research is focused on the anodizing process operations in manufacturing and supplying of aluminium extrusion. The data required for the development of the model is collected from the observations and interviews conducted in the study. To study the current system, the processes involved in the anodizing process are modeled by using Arena 14.5 simulation software. Those processes consist of five main processes, namely the degreasing process, the etching process, the desmut process, the anodizing process, the sealing process and 16 other processes. The results obtained were analyzed to identify the problems or bottlenecks that occurred and to propose improvement methods that can be implemented on the original model. Based on the comparisons that have been done between the improvement methods, the productivity could be increased by reallocating the workers and reducing loading time.

Modeling of the HiPco process for carbon nanotube production. II. Reactor-scale analysis

NASA Technical Reports Server (NTRS)

Gokcen, Tahir; Dateo, Christopher E.; Meyyappan, M.

2002-01-01

The high-pressure carbon monoxide (HiPco) process, developed at Rice University, has been reported to produce single-walled carbon nanotubes from gas-phase reactions of iron carbonyl in carbon monoxide at high pressures (10-100 atm). Computational modeling is used here to develop an understanding of the HiPco process. A detailed kinetic model of the HiPco process that includes of the precursor, decomposition metal cluster formation and growth, and carbon nanotube growth was developed in the previous article (Part I). Decomposition of precursor molecules is necessary to initiate metal cluster formation. The metal clusters serve as catalysts for carbon nanotube growth. The diameter of metal clusters and number of atoms in these clusters are some of the essential information for predicting carbon nanotube formation and growth, which is then modeled by the Boudouard reaction with metal catalysts. Based on the detailed model simulations, a reduced kinetic model was also developed in Part I for use in reactor-scale flowfield calculations. Here this reduced kinetic model is integrated with a two-dimensional axisymmetric reactor flow model to predict reactor performance. Carbon nanotube growth is examined with respect to several process variables (peripheral jet temperature, reactor pressure, and Fe(CO)5 concentration) with the use of the axisymmetric model, and the computed results are compared with existing experimental data. The model yields most of the qualitative trends observed in the experiments and helps to understanding the fundamental processes in HiPco carbon nanotube production.

Modelling of Sub-daily Hydrological Processes Using Daily Time-Step Models: A Distribution Function Approach to Temporal Scaling

NASA Astrophysics Data System (ADS)

Kandel, D. D.; Western, A. W.; Grayson, R. B.

2004-12-01

Mismatches in scale between the fundamental processes, the model and supporting data are a major limitation in hydrologic modelling. Surface runoff generation via infiltration excess and the process of soil erosion are fundamentally short time-scale phenomena and their average behaviour is mostly determined by the short time-scale peak intensities of rainfall. Ideally, these processes should be simulated using time-steps of the order of minutes to appropriately resolve the effect of rainfall intensity variations. However, sub-daily data support is often inadequate and the processes are usually simulated by calibrating daily (or even coarser) time-step models. Generally process descriptions are not modified but rather effective parameter values are used to account for the effect of temporal lumping, assuming that the effect of the scale mismatch can be counterbalanced by tuning the parameter values at the model time-step of interest. Often this results in parameter values that are difficult to interpret physically. A similar approach is often taken spatially. This is problematic as these processes generally operate or interact non-linearly. This indicates a need for better techniques to simulate sub-daily processes using daily time-step models while still using widely available daily information. A new method applicable to many rainfall-runoff-erosion models is presented. The method is based on temporal scaling using statistical distributions of rainfall intensity to represent sub-daily intensity variations in a daily time-step model. This allows the effect of short time-scale nonlinear processes to be captured while modelling at a daily time-step, which is often attractive due to the wide availability of daily forcing data. The approach relies on characterising the rainfall intensity variation within a day using a cumulative distribution function (cdf). This cdf is then modified by various linear and nonlinear processes typically represented in hydrological and erosion models. The statistical description of sub-daily variability is thus propagated through the model, allowing the effects of variability to be captured in the simulations. This results in cdfs of various fluxes, the integration of which over a day gives respective daily totals. Using 42-plot-years of surface runoff and soil erosion data from field studies in different environments from Australia and Nepal, simulation results from this cdf approach are compared with the sub-hourly (2-minute for Nepal and 6-minute for Australia) and daily models having similar process descriptions. Significant improvements in the simulation of surface runoff and erosion are achieved, compared with a daily model that uses average daily rainfall intensities. The cdf model compares well with a sub-hourly time-step model. This suggests that the approach captures the important effects of sub-daily variability while utilizing commonly available daily information. It is also found that the model parameters are more robustly defined using the cdf approach compared with the effective values obtained at the daily scale. This suggests that the cdf approach may offer improved model transferability spatially (to other areas) and temporally (to other periods).

Cross-disciplinary links in environmental systems science: Current state and claimed needs identified in a meta-review of process models.

PubMed

Ayllón, Daniel; Grimm, Volker; Attinger, Sabine; Hauhs, Michael; Simmer, Clemens; Vereecken, Harry; Lischeid, Gunnar

2018-05-01

Terrestrial environmental systems are characterised by numerous feedback links between their different compartments. However, scientific research is organized into disciplines that focus on processes within the respective compartments rather than on interdisciplinary links. Major feedback mechanisms between compartments might therefore have been systematically overlooked so far. Without identifying these gaps, initiatives on future comprehensive environmental monitoring schemes and experimental platforms might fail. We performed a comprehensive overview of feedbacks between compartments currently represented in environmental sciences and explores to what degree missing links have already been acknowledged in the literature. We focused on process models as they can be regarded as repositories of scientific knowledge that compile findings of numerous single studies. In total, 118 simulation models from 23 model types were analysed. Missing processes linking different environmental compartments were identified based on a meta-review of 346 published reviews, model intercomparison studies, and model descriptions. Eight disciplines of environmental sciences were considered and 396 linking processes were identified and ascribed to the physical, chemical or biological domain. There were significant differences between model types and scientific disciplines regarding implemented interdisciplinary links. The most wide-spread interdisciplinary links were between physical processes in meteorology, hydrology and soil science that drive or set the boundary conditions for other processes (e.g., ecological processes). In contrast, most chemical and biological processes were restricted to links within the same compartment. Integration of multiple environmental compartments and interdisciplinary knowledge was scarce in most model types. There was a strong bias of suggested future research foci and model extensions towards reinforcing existing interdisciplinary knowledge rather than to open up new interdisciplinary pathways. No clear pattern across disciplines exists with respect to suggested future research efforts. There is no evidence that environmental research would clearly converge towards more integrated approaches or towards an overarching environmental systems theory. Copyright © 2017 Elsevier B.V. All rights reserved.

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice.

PubMed

Towal, R Blythe; Mormann, Milica; Koch, Christof

2013-10-01

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift-diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions.

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

PubMed Central

Towal, R. Blythe; Mormann, Milica; Koch, Christof

2013-01-01

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift–diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions. PMID:24019496

The (Mathematical) Modeling Process in Biosciences.

PubMed

Torres, Nestor V; Santos, Guido

2015-01-01

In this communication, we introduce a general framework and discussion on the role of models and the modeling process in the field of biosciences. The objective is to sum up the common procedures during the formalization and analysis of a biological problem from the perspective of Systems Biology, which approaches the study of biological systems as a whole. We begin by presenting the definitions of (biological) system and model. Particular attention is given to the meaning of mathematical model within the context of biology. Then, we present the process of modeling and analysis of biological systems. Three stages are described in detail: conceptualization of the biological system into a model, mathematical formalization of the previous conceptual model and optimization and system management derived from the analysis of the mathematical model. All along this work the main features and shortcomings of the process are analyzed and a set of rules that could help in the task of modeling any biological system are presented. Special regard is given to the formative requirements and the interdisciplinary nature of this approach. We conclude with some general considerations on the challenges that modeling is posing to current biology.

Linking Goal-Oriented Requirements and Model-Driven Development

NASA Astrophysics Data System (ADS)

Pastor, Oscar; Giachetti, Giovanni

In the context of Goal-Oriented Requirement Engineering (GORE) there are interesting modeling approaches for the analysis of complex scenarios that are oriented to obtain and represent the relevant requirements for the development of software products. However, the way to use these GORE models in an automated Model-Driven Development (MDD) process is not clear, and, in general terms, the translation of these models into the final software products is still manually performed. Therefore, in this chapter, we show an approach to automatically link GORE models and MDD processes, which has been elaborated by considering the experience obtained from linking the i * framework with an industrially applied MDD approach. The linking approach proposed is formulated by means of a generic process that is based on current modeling standards and technologies in order to facilitate its application for different MDD and GORE approaches. Special attention is paid to how this process generates appropriate model transformation mechanisms to automatically obtain MDD conceptual models from GORE models, and how it can be used to specify validation mechanisms to assure the correct model transformations.

The Modular Modeling System (MMS): User's Manual

USGS Publications Warehouse

Leavesley, G.H.; Restrepo, Pedro J.; Markstrom, S.L.; Dixon, M.; Stannard, L.G.

1996-01-01

The Modular Modeling System (MMS) is an integrated system of computer software that has been developed to provide the research and operational framework needed to support development, testing, and evaluation of physical-process algorithms and to facilitate integration of user-selected sets of algorithms into operational physical-process models. MMS uses a module library that contains modules for simulating a variety of water, energy, and biogeochemical processes. A model is created by selectively coupling the most appropriate modules from the library to create a 'suitable' model for the desired application. Where existing modules do not provide appropriate process algorithms, new modules can be developed. The MMS user's manual provides installation instructions and a detailed discussion of system concepts, module development, and model development and application using the MMS graphical user interface.

Validation of a multi-phase plant-wide model for the description of the aeration process in a WWTP.

PubMed

Lizarralde, I; Fernández-Arévalo, T; Beltrán, S; Ayesa, E; Grau, P

2018-02-01

This paper introduces a new mathematical model built under the PC-PWM methodology to describe the aeration process in a full-scale WWTP. This methodology enables a systematic and rigorous incorporation of chemical and physico-chemical transformations into biochemical process models, particularly for the description of liquid-gas transfer to describe the aeration process. The mathematical model constructed is able to reproduce biological COD and nitrogen removal, liquid-gas transfer and chemical reactions. The capability of the model to describe the liquid-gas mass transfer has been tested by comparing simulated and experimental results in a full-scale WWTP. Finally, an exploration by simulation has been undertaken to show the potential of the mathematical model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Aligning observed and modelled behaviour based on workflow decomposition

NASA Astrophysics Data System (ADS)

Wang, Lu; Du, YuYue; Liu, Wei

2017-09-01

When business processes are mostly supported by information systems, the availability of event logs generated from these systems, as well as the requirement of appropriate process models are increasing. Business processes can be discovered, monitored and enhanced by extracting process-related information. However, some events cannot be correctly identified because of the explosion of the amount of event logs. Therefore, a new process mining technique is proposed based on a workflow decomposition method in this paper. Petri nets (PNs) are used to describe business processes, and then conformance checking of event logs and process models is investigated. A decomposition approach is proposed to divide large process models and event logs into several separate parts that can be analysed independently; while an alignment approach based on a state equation method in PN theory enhances the performance of conformance checking. Both approaches are implemented in programmable read-only memory (ProM). The correctness and effectiveness of the proposed methods are illustrated through experiments.

A Framework for Distributed Problem Solving

NASA Astrophysics Data System (ADS)

Leone, Joseph; Shin, Don G.

1989-03-01

This work explores a distributed problem solving (DPS) approach, namely the AM/AG model, to cooperative memory recall. The AM/AG model is a hierarchic social system metaphor for DPS based on the Mintzberg's model of organizations. At the core of the model are information flow mechanisms, named amplification and aggregation. Amplification is a process of expounding a given task, called an agenda, into a set of subtasks with magnified degree of specificity and distributing them to multiple processing units downward in the hierarchy. Aggregation is a process of combining the results reported from multiple processing units into a unified view, called a resolution, and promoting the conclusion upward in the hierarchy. The combination of amplification and aggregation can account for a memory recall process which primarily relies on the ability of making associations between vast amounts of related concepts, sorting out the combined results, and promoting the most plausible ones. The amplification process is discussed in detail. An implementation of the amplification process is presented. The process is illustrated by an example.

Lebedev acceleration and comparison of different photometric models in the inversion of lightcurves for asteroids

NASA Astrophysics Data System (ADS)

Lu, Xiao-Ping; Huang, Xiang-Jie; Ip, Wing-Huen; Hsia, Chi-Hao

2018-04-01

In the lightcurve inversion process where asteroid's physical parameters such as rotational period, pole orientation and overall shape are searched, the numerical calculations of the synthetic photometric brightness based on different shape models are frequently implemented. Lebedev quadrature is an efficient method to numerically calculate the surface integral on the unit sphere. By transforming the surface integral on the Cellinoid shape model to that on the unit sphere, the lightcurve inversion process based on the Cellinoid shape model can be remarkably accelerated. Furthermore, Matlab codes of the lightcurve inversion process based on the Cellinoid shape model are available on Github for free downloading. The photometric models, i.e., the scattering laws, also play an important role in the lightcurve inversion process, although the shape variations of asteroids dominate the morphologies of the lightcurves. Derived from the radiative transfer theory, the Hapke model can describe the light reflectance behaviors from the viewpoint of physics, while there are also many empirical models in numerical applications. Numerical simulations are implemented for the comparison of the Hapke model with the other three numerical models, including the Lommel-Seeliger, Minnaert, and Kaasalainen models. The results show that the numerical models with simple function expressions can fit well with the synthetic lightcurves generated based on the Hapke model; this good fit implies that they can be adopted in the lightcurve inversion process for asteroids to improve the numerical efficiency and derive similar results to those of the Hapke model.

Modelling and control for laser based welding processes: modern methods of process control to improve quality of laser-based joining methods

NASA Astrophysics Data System (ADS)

Zäh, Ralf-Kilian; Mosbach, Benedikt; Hollwich, Jan; Faupel, Benedikt

2017-02-01

To ensure the competitiveness of manufacturing companies it is indispensable to optimize their manufacturing processes. Slight variations of process parameters and machine settings have only marginally effects on the product quality. Therefore, the largest possible editing window is required. Such parameters are, for example, the movement of the laser beam across the component for the laser keyhole welding. That`s why it is necessary to keep the formation of welding seams within specified limits. Therefore, the quality of laser welding processes is ensured, by using post-process methods, like ultrasonic inspection, or special in-process methods. These in-process systems only achieve a simple evaluation which shows whether the weld seam is acceptable or not. Furthermore, in-process systems use no feedback for changing the control variables such as speed of the laser or adjustment of laser power. In this paper the research group presents current results of the research field of Online Monitoring, Online Controlling and Model predictive controlling in laser welding processes to increase the product quality. To record the characteristics of the welding process, tested online methods are used during the process. Based on the measurement data, a state space model is ascertained, which includes all the control variables of the system. Depending on simulation tools the model predictive controller (MPC) is designed for the model and integrated into an NI-Real-Time-System.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Towards Systematic Benchmarking of Climate Model Performance

NASA Astrophysics Data System (ADS)

Gleckler, P. J.

2014-12-01

The process by which climate models are evaluated has evolved substantially over the past decade, with the Coupled Model Intercomparison Project (CMIP) serving as a centralizing activity for coordinating model experimentation and enabling research. Scientists with a broad spectrum of expertise have contributed to the CMIP model evaluation process, resulting in many hundreds of publications that have served as a key resource for the IPCC process. For several reasons, efforts are now underway to further systematize some aspects of the model evaluation process. First, some model evaluation can now be considered routine and should not require "re-inventing the wheel" or a journal publication simply to update results with newer models. Second, the benefit of CMIP research to model development has not been optimal because the publication of results generally takes several years and is usually not reproducible for benchmarking newer model versions. And third, there are now hundreds of model versions and many thousands of simulations, but there is no community-based mechanism for routinely monitoring model performance changes. An important change in the design of CMIP6 can help address these limitations. CMIP6 will include a small set standardized experiments as an ongoing exercise (CMIP "DECK": ongoing Diagnostic, Evaluation and Characterization of Klima), so that modeling groups can submit them at any time and not be overly constrained by deadlines. In this presentation, efforts to establish routine benchmarking of existing and future CMIP simulations will be described. To date, some benchmarking tools have been made available to all CMIP modeling groups to enable them to readily compare with CMIP5 simulations during the model development process. A natural extension of this effort is to make results from all CMIP simulations widely available, including the results from newer models as soon as the simulations become available for research. Making the results from routine performance tests readily accessible will help advance a more transparent model evaluation process.

Comparative Analysis on Nonlinear Models for Ron Gasoline Blending Using Neural Networks

NASA Astrophysics Data System (ADS)

Aguilera, R. Carreño; Yu, Wen; Rodríguez, J. C. Tovar; Mosqueda, M. Elena Acevedo; Ortiz, M. Patiño; Juarez, J. J. Medel; Bautista, D. Pacheco

The blending process always being a nonlinear process is difficult to modeling, since it may change significantly depending on the components and the process variables of each refinery. Different components can be blended depending on the existing stock, and the chemical characteristics of each component are changing dynamically, they all are blended until getting the expected specification in different properties required by the customer. One of the most relevant properties is the Octane, which is difficult to control in line (without the component storage). Since each refinery process is quite different, a generic gasoline blending model is not useful when a blending in line wants to be done in a specific process. A mathematical gasoline blending model is presented in this paper for a given process described in state space as a basic gasoline blending process description. The objective is to adjust the parameters allowing the blending gasoline model to describe a signal in its trajectory, representing in neural networks extreme learning machine method and also for nonlinear autoregressive-moving average (NARMA) in neural networks method, such that a comparative work be developed.

Forest Canopy Processes in a Regional Chemical Transport Model

NASA Astrophysics Data System (ADS)

Makar, Paul; Staebler, Ralf; Akingunola, Ayodeji; Zhang, Junhua; McLinden, Chris; Kharol, Shailesh; Moran, Michael; Robichaud, Alain; Zhang, Leiming; Stroud, Craig; Pabla, Balbir; Cheung, Philip

2016-04-01

Forest canopies have typically been absent or highly parameterized in regional chemical transport models. Some forest-related processes are often considered - for example, biogenic emissions from the forests are included as a flux lower boundary condition on vertical diffusion, as is deposition to vegetation. However, real forest canopies comprise a much more complicated set of processes, at scales below the "transport model-resolved scale" of vertical levels usually employed in regional transport models. Advective and diffusive transport within the forest canopy typically scale with the height of the canopy, and the former process tends to dominate over the latter. Emissions of biogenic hydrocarbons arise from the foliage, which may be located tens of metres above the surface, while emissions of biogenic nitric oxide from decaying plant matter are located at the surface - in contrast to the surface flux boundary condition usually employed in chemical transport models. Deposition, similarly, is usually parameterized as a flux boundary condition, but may be differentiated between fluxes to vegetation and fluxes to the surface when the canopy scale is considered. The chemical environment also changes within forest canopies: shading, temperature, and relativity humidity changes with height within the canopy may influence chemical reaction rates. These processes have been observed in a host of measurement studies, and have been simulated using site-specific one-dimensional forest canopy models. Their influence on regional scale chemistry has been unknown, until now. In this work, we describe the results of the first attempt to include complex canopy processes within a regional chemical transport model (GEM-MACH). The original model core was subdivided into "canopy" and "non-canopy" subdomains. In the former, three additional near-surface layers based on spatially and seasonally varying satellite-derived canopy height and leaf area index were added to the original model structure. Process methodology for deposition, biogenic emissions, shading, vertical diffusion, advection, chemical reactive environment and particle microphysics were modified to account for expected conditions within the forest canopy and the additional layers. The revised and original models were compared for a 10km resolution domain covering North America, for a one-month duration simulation. The canopy processes were found to have a very significant impact on model results. We will present a comparison to network observations which suggests that forest canopy processes may account for previously unexplained local and regional biases in model ozone predictions noted in GEM-MACH and other models. The impact of the canopy processes on NO2, PM2.5, and SO2 performance will also be presented and discussed.

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stein, Michael

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead tomore » predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the Pacific Ocean, and annual temperature extremes at a site in New York City. In each of these applications, our theoretical and computational innovations were directly motivated by the challenges posed by analyzing these and similar types of data.« less