Relevant bibliographies by topics / Five factor model

Academic literature on the topic 'Five factor model'

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Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Five factor model"

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Paliienko, Oleksandr, Svitlana Naumenkova, and Svitlana Mishchenko. "An empirical investigation of the Fama-French five-factor model." Investment Management and Financial Innovations 17, no. 1 (March 10, 2020): 143–55. http://dx.doi.org/10.21511/imfi.17(1).2020.13.

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The article deals with evaluating the securities portfolios in the process of transition from the one-factor CAPM model to the Fama-French five-factor model (FF5F). It identifies the advantages of the latter and discusses the controversial issues regarding its use by portfolio investors in different countries, given the anomalies inherent in asset pricing. Besides, the peculiarities of the statistical stratification method used in the FF5F model to group stock portfolios are revealed, and attention is drawn to some of the debating points of the five-factor model. The proposals have been formulated, which offer broader avenues for taking advantage of the FF5F model and increase the validity of the portfolio analysis results. The article also gives recommendations on modifying the approaches to analyzing small-size portfolios versus big-size portfolios based on partial changes in RMW and CMA factors, threshold proportions, and the use of STARR for asymmetric portfolios. The study substantiates the use of these approaches in testing the Fama-French five-factor model with portfolios composed of blue chips.
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Blitz, David, Matthias X. Hanauer, Milan Vidojevic, and Pim van Vliet. "Five Concerns with the Five-Factor Model." Journal of Portfolio Management 44, no. 4 (March 25, 2018): 71–78. http://dx.doi.org/10.3905/jpm.2018.44.4.071.

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Mervielde, Ivan. "The Five-Factor Model: Quo Vadis?" Psychologica Belgica 34, no. 4 (January 1, 1994): 275. http://dx.doi.org/10.5334/pb.873.

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Fama, Eugene F., and Kenneth R. French. "A five-factor asset pricing model." Journal of Financial Economics 116, no. 1 (April 2015): 1–22. http://dx.doi.org/10.1016/j.jfineco.2014.10.010.

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Lee, Heung-Pyo, Paul Kyuman Chae, Hong-Seock Lee, and Yong-Ku Kim. "The five-factor gambling motivation model." Psychiatry Research 150, no. 1 (February 2007): 21–32. http://dx.doi.org/10.1016/j.psychres.2006.04.005.

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Walkshäusl, Christian. "Mispricing and the five-factor model." Economics Letters 147 (October 2016): 99–102. http://dx.doi.org/10.1016/j.econlet.2016.08.025.

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King, Laura A., Lori McKee Walker, and Sheri J. Broyles. "Creativity and the Five-Factor Model." Journal of Research in Personality 30, no. 2 (June 1996): 189–203. http://dx.doi.org/10.1006/jrpe.1996.0013.

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Fruyt, Filip De, Robert R. McCrae, Zsófia Szirmák, and János Nagy. "The Five-Factor Personality Inventory as a Measure of the Five-Factor Model." Assessment 11, no. 3 (September 2004): 207–15. http://dx.doi.org/10.1177/1073191104265800.

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Ashton, Michael C., and Kibeom Lee. "Honesty-Humility, the Big Five, and the Five-Factor Model." Journal of Personality 73, no. 5 (October 2005): 1321–54. http://dx.doi.org/10.1111/j.1467-6494.2005.00351.x.

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Roh, Sangchoong, and Yongwon Suh. "The Five-Factor Model of Workplace Spirituality." Korean Journal of Industrial and Organizational Psychology 27, no. 2 (May 31, 2014): 419–47. http://dx.doi.org/10.24230/kjiop.v27i2.419-447.

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The purpose of the present study is to propose comprehensive conceptual model of workplace spirituality(WS) and to develp a scale that can measure the WS. In study 1, the five-factor model of WS was proposed, and 39 items were developed for constructing workplace spirituality index(WSI) scale. The 5-factor model of WS included the followings: 1) ‘a sense of inner life’ toward oneself, 2) ‘a sense of calling’ toward one's work, 3) ‘a sense of empathy’ toward one's colleagues, 4) ‘a sense of community’ toward on's organization, and 5) ‘a sense of transcendence’ above and beyond one's ego. A sample of 239 participants from six global companies completed the scale, and an exploratory factor analysis showed 5 factor structure, and 23 items were selected to constuct a final WSI scale. In study 2, a cross-validation study was conducted with a sample of 161 participants. A confirmatory factor analysis indicated a satisfactory fit for the 5-factor model of WS, and a secondary factor analysis revealed that the five factors were well converged into the higher factor of the whole WS. In study 3, a discriminant validation study was conducted with a sample of 400 employees to show a discriminant validity between WS and subjective well-being (SWB), quality of working life (QWL), and perceived value of work (PVW). The results showed that WS was a different construct with the above related concepts. Finally, theoretical and practical implications were discussed, and limitations and future suggestions were described.
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Dissertations / Theses on the topic "Five factor model"

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Veltri, Carlo O. C. "Assessing Compulsivity with the Personality Psychopathology Five and the Five Factor Model." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1350082837.

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Knap, Malgorzata A. "The Five-Factor Model of personality and psychopathy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0002/NQ38312.pdf.

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Bildtgård, Marcus. "Applying the Five-Factor Model to Game Design." Thesis, Uppsala universitet, Institutionen för speldesign, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227628.

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What makes us like or dislike certain games? Is there relation between our tastes in games and our personalities and can it be measured? This dissertation examines gamer personalities and game attributes with the help of the Five-Factor Model, also called The Big Five. It treats an experiment on how to apply the Five-Factor Model to games and their players and what it may be used for. By interviewing gamers, recording their favored and unfavored games, letting the gamers take a Big Five personality test and then juxtapose their personalities with their games' attributes, those questions may be answered.
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Gore, Whitney L. "The DSM-5 Dimensional Trait Model and the Five Factor Model." UKnowledge, 2013. http://uknowledge.uky.edu/psychology_etds/12.

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The current thesis tests empirically the relationship of the dimensional trait model proposed for the fifth edition of the American Psychiatric Association’s (APA) Diagnostic and Statistical Manual of Mental Disorders with five-factor models (FFM) of personality disorder (PD). The DSM-5 Personality and Personality Disorders Work Group proposes to diagnose the disorders largely in terms of a 25 trait dimensional model organized within five broad domains (i.e., negative affectivity, detachment, antagonism, disinhibition, and psychoticism). Consistent with the authors of DSM-5, it was predicted that negative affectivity would align with FFM neuroticism, detachment with FFM introversion, antagonism with FFM antagonism, disinhibition with low FFM conscientiousness and, contrary to the authors of DSM-5, psychoticism would align with FFM openness. Suggested changes in trait placements according to FFM of PD research were also tested. Four measures of five factor models of general personality were administered to 445 undergraduates along with the Personality Inventory for DSM-5. The results of the present study provided support for the hypothesis that all five domains of the DSM-5 dimensional trait model are maladaptive variants of general personality structure, including the domain of psychoticism; however, the findings provided mixed support for suggested trait placement changes in the DSM-5 model.
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Crossman, Edward W. "Gambling behavior and the five factor model of personality." access full-text online access from Digital Dissertation Consortium, 2007. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?1443743.

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Smolik, Joyce M. "Exploring a five factor mentoring model within elementary science." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4576.

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The purpose of the study was to explore and describe the perceptions of several elementary science mentors and their mentees within one school district based on a five-factor mentoring model. Utilizing a qualitative methodology in the form of a case study, five mentors and three mentees were interviewed using a structured protocol. From verbatim interview data and field notes, three themes pertaining to the role of the mentor within elementary science emerged as emotional support, technical support, and educative support. Within the five-factor mentoring model, personal attributes suggested notions of support and expert status as critical elements for effective mentoring. The factors of system requirements, pedagogical knowledge, modeling, and feedback were found to be interrelated amongst themselves and with the factor of personal attributes. Effective mentors demonstrated a commitment to the role as well as a flexibility pertaining to role adjustment depending on the context of the mentoring relationship.
ID: 029051118; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ed.D.)--University of Central Florida, 2010.; Includes bibliographical references (p. 178-187).
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Crego, Cristina. "BIPOLARITY AND THE FIVE FACTOR MODEL OF PERSONALITY DISORDER." UKnowledge, 2018. https://uknowledge.uky.edu/psychology_etds/143.

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The predominant model of general personality structure is arguably the Five Factor Model (FFM), consisting of the five broad domains of neuroticism, extraversion, openness, agreeableness, and conscientiousness. The FFM of personality disorder (FFMPD) has proposed maladaptive variants at both poles of the FFM. The purpose of the current study was to identify a subset of FFMPD scales, utilizing factor analysis, that illustrate, and provide a potential measure of, the bipolarity present in the FFMPD. All of the FFMPD scales were administered to 443 community participants recruited from Amazon Mechanical Turk. Bipolarity was evident in a series of factor analyses of subsets of FFMPD scales, with the exception of openness. The current study also demonstrated that the presence of bipolarity is impaired by a number of concerns, including the presence of non-diametric scales, bloated specific factors, general factor of personality disorder, and occupation of interstitial space.
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Carter, Teresa G. "Five-Factor Model as a Predictor for Spoken Dialog Systems." NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/990.

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Human behavior varies widely as does the design of spoken dialog systems (SDS). The search for predictors to match a user’s preference and efficiency for a specific dialog interface type in an SDS was the focus of this research. By using personality as described by the Five-Factor Method (FFM) and the Wizard of Oz technique for delivering three system initiatives of the SDS, participants interacted with each of the SDS initiatives in scheduling an airline flight. The three system initiatives were constructed as strict system, which did not allow the user control of the interaction; mixed system, which allowed the user some control of the interaction but with a system override; and user system, which allowed the user control of the interaction. In order to eliminate gender bias in using the FFM as the instrument, participants were matched in gender and age. Participants were 18 years old to 70 years old, passed a hearing test, had no disability that prohibited the use of the SDS, and were native English speakers. Participants completed an adult consent form, a 50-question personality assessment as described by the FFM, and the interaction with the SDS. Participants also completed a system preference indication form at the end of the interaction. Observations for efficiency were recorded on paper by the researcher. Although the findings did not show a definitive predictor for a SDS due to the small population sample, by using a multinomial regression approach to the statistical analysis, odds ratios of the data helped draw conclusions that support certain personality factors as important roles in a user’s preference and efficiency in choosing and using a SDS. This gives an area for future research. Also, the presumption that preference and efficiency always match was not supported by the results from two of the three systems. An additional area for future research was discovered in the gender data. Although not an initial part of the research, the data shows promise in predicting preference and efficiency for certain SDS. Future research is indicated.
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Edmundson, Maryanne. "A FIVE-FACTOR MEASURE OF SCHIZOTYPAL PERSONALITY DISORDER." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_theses/57.

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The current study provides convergent, discriminant, and incremental validity data for a measure of schizotypia from the perspective of the Five-Factor Model (FFM) of general personality structure. Nine schizotypia facet scales were constructed as maladaptive variants of respective facets of the FFM (e.g., Aberrant Ideas as a maladaptive variant of FFM Openness to Ideas). On the basis of data from 143 undergraduates the convergent validity of these nine facet scales was tested with respect to 11 established measures of schizotypia and the respective facets of the FFM. Discriminant validity was tested with respect to other personality disorders and facets from other FFM domains. Incremental validity was tested with respect to the ability of the FFM schizotypia facet scales to account for variance in two established measures of schizotypia, after variance accounted for by respective FFM facets and other established measures of schizotypia were first removed. The findings support the validity of these new facet scales as measures of schizotypia and as maladaptive variants of the FFM.
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Sullivan, Maureen. "The five-factor model and the processing of self-relevant information." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0017/NQ44603.pdf.

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Books on the topic "Five factor model"

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McCrae, Robert R., and Jüri Allik, eds. The Five-Factor Model of Personality Across Cultures. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0763-5.

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Personality disorders and the five-factor model of personality. 3rd ed. Washington, D.C: American Psychological Association, 2013.

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Costa, Paul T., and Thomas A. Widiger, eds. Personality disorders and the five-factor model of personality. Washington: American Psychological Association, 1994. http://dx.doi.org/10.1037/10140-000.

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Smith, Debra J. A five factor model of grief: A q-methodological study. St. Catharines, Ont: Brock University, Dept. of Psychology, 2001.

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Costa, Paul T., and Thomas A. Widiger, eds. Personality disorders and the five-factor model of personality (2nd ed.). Washington: American Psychological Association, 2002. http://dx.doi.org/10.1037/10423-000.

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Widiger, Thomas A., and Paul T. Costa, eds. Personality disorders and the five-factor model of personality (3rd ed.). Washington: American Psychological Association, 2013. http://dx.doi.org/10.1037/13939-000.

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Trull, Timothy J. Structured interview for the Five-Factor Model of Personality (SIFFM): Professional manual. Odessa, FL: Psychological Assessment Resources, 1997.

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Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model. Fort Collins, CO: United States Department of Agriculture/Forest Service, Rocky Mountain Research Station, 2012.

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A primer of LISREL: Basic applicationsand programming for confirmatory factor analytic models. New York: Springer-Verlag, 1989.

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Byrne, Barbara M. A primer of LISREL: Basic applications and programming for confirmatory factor analytic models. New York: Springer-Verlag, 1989.

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Book chapters on the topic "Five factor model"

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Madison, Guy. "Five-Factor Model." In Encyclopedia of Evolutionary Psychological Science, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-16999-6_681-1.

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Novikova, Irina A., and Alexandra A. Vorobyeva. "The Five-Factor Model." In Cross-Cultural Psychology, 685–706. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119519348.ch33.

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Chmielewski, Michael S., and Theresa A. Morgan. "Five-Factor Model of Personality." In Encyclopedia of Behavioral Medicine, 884–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1226.

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Clemson, Lindy, J. Rick Turner, J. Rick Turner, Farrah Jacquez, Whitney Raglin, Gabriela Reed, Gabriela Reed, et al. "Five-Factor Model of Personality." In Encyclopedia of Behavioral Medicine, 803–4. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1226.

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Piedmont, Ralph L. "Five Factor Model of Personality." In Encyclopedia of Quality of Life and Well-Being Research, 2282. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1055.

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Conte, Jeffrey M. "Five-Factor Model Rating Form." In Encyclopedia of Personality and Individual Differences, 1601–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_32.

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Conte, Jeffrey M. "Five-Factor Model Rating Form." In Encyclopedia of Personality and Individual Differences, 1–3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_32-1.

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McNulty, John L. "Five-factor model of personality." In Encyclopedia of Psychology, Vol. 3., 375–76. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10518-183.

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Costa, Paul T., and Robert R. McCrae. "The Five-Factor Model, Five-Factor Theory, and Interpersonal Psychology." In Handbook of Interpersonal Psychology, 91–104. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118001868.ch6.

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Allik, Jüri, and Robert R. McCrae. "A Five-Factor Theory Perspective." In The Five-Factor Model of Personality Across Cultures, 303–22. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0763-5_15.

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Conference papers on the topic "Five factor model"

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Drobnitsa, Irina. "CORRELATION BETWEEN EYSENCK’S THREE-FACTOR PERSONALITY MODEL, GOLDBERG’S FIVE-FACTOR MODEL (BIG FIVE) AND CATTELL’S FACTOR MODEL FROM THE PERSPECTIVE OF THE STRUCTURAL-ACTIVATION-NEUROCHEMICAL TEMPERAMENT MODEL (SAN-T)." In XVI International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1036.sudak.ns2020-16/192.

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Pramodh, Kasula Chaithanya, and Y. Vijayalata. "Automatic personality recognition of authors using big five factor model." In 2016 IEEE International Conference on Advances in Computer Applications (ICACA). IEEE, 2016. http://dx.doi.org/10.1109/icaca.2016.7887919.

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Li, Hyung-Chul O., Junho Seo, Keetaek Kham, and Seunghyun Lee. "Method of Measuring Subjective 3D Visual Fatigue: A Five-Factor Model." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/dh.2008.dwa5.

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Inggrit Wijaya, Liliana, Randy Kennardi Irawan, and Putu Anom Mahadwartha. "Test of Fama a French five factor-model on Indonesian stock market." In 15th International Symposium on Management (INSYMA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/insyma-18.2018.12.

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Fauzie, Syarief, and Ranika Elizabeth Siagian. "Fama-French Five-Factor Model Analysis on Valuation of Bank Stock Returns." In 2nd INTERNATIONAL RESEARCH CONFERENCE ON ECONOMICS AND BUSINESS 2018. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008786802760284.

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Stidham, Hallie, Michelle Flynn, Joshua D. Summers, and Marissa Shuffler. "Understanding Team Personality Evolution in Student Engineering Design Teams Using the Five Factor Model." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85888.

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This research explores the role of personalities in engineering design teams in a capstone course using the Five Factor Model of Personality. Specifically, the self and peer assessed personality profiles are across a semester project. After four iterations, the expectation was that peers would be better able to identify their teammates personality traits. Results show that the peer evaluations do change over time. For the factors of Openness, Conscientiousness, and Agreeableness, the agreement between self and peer evaluations increased from Iteration 2 to Iteration 4. The Extraversion factor agreement increased, but not to the point where the peer and self-evaluations did not have statistically significant differences. The agreement between the self and peer evaluations for Neuroticism decreased over time. Extended results, limitations, and future work are also discussed.
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Hsien-Lun Wong, Yi-Hsien Tu, and Mei-Chun Lin. "The Validity of Five-Factor Model of Personality to Predicting Impulse Buying Tendency." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.45.

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"EXPERIENTIAL STATE AND EFFECT OF FACTORS ON THE DIMENSIONS OF OFFER IN A SERVICE COMPANY - Using Confirmatory Factor Analysis: A Five Factor Model." In 1st International Conference on Operations Research and Enterprise Systems. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003762504800483.

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Huang, Zheng. "Are Profitability and Investment Good Proxies for Risk Factors The Analysis on Fama and French's Five-Factor Model." In 2016 International Conference on Management Science and Management Innovation. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/msmi-16.2016.48.

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Liu, Yang. "Analysis of Hardware Industry During COVID-19 Based on Fama-French Five Factor Model." In 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2021. http://dx.doi.org/10.1109/ipec51340.2021.9421237.

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Reports on the topic "Five factor model"

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.
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Andrews, Patricia L. Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2012. http://dx.doi.org/10.2737/rmrs-gtr-266.

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Méndez-Vizcaíno, Juan C., and Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1175.

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Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-Switching process for public transfers with an explosive regime. The most notable additions to the existing literature for Latin America are the estimations of entire distributions of public debt limits for various default probabilities and that said limits critically hinge on both current and future states. Results obtained indicate notorious contractions of Fiscal Spaces among all countries during the pandemic, but the sizes of these were very heterogeneous. Countries that in 2019 had positive spaces and got closer to negative spaces in 2020, have since seen deterioration of their sovereign debt ratings or outlooks. Colombia was the only country to lose its positive Fiscal Space and investment grade, thereby joining Brazil, the previously sole member of both groups
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Sutherland, Steve. Fuels planning: science synthesis and integration; environmental consequences fact sheet 02: First Order Fire Effects Model (FOFEM). Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2004. http://dx.doi.org/10.2737/rmrs-rn-23-v2.

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Aalto, Juha, and Ari Venäläinen, eds. Climate change and forest management affect forest fire risk in Fennoscandia. Finnish Meteorological Institute, June 2021. http://dx.doi.org/10.35614/isbn.9789523361355.

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Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.
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McKinnon, Mark B., and Daniel Madrzykowski. Four Firefighters Burned in Residential House Fire - Georgia. UL's Fire Safety Research Institute, June 2022. http://dx.doi.org/10.54206/102376/gekk4148.

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On September 3, 2018, two career Fire Lieutenants and two career Firefighters suffered burn injuries as a result of a residential structure fire. On September 10, 2018, personnel representing several other fire departments in the area, including a member of the Fire Safety Research Institute (FSRI) Advisory Board visited the fire scene to document the incident and collect material samples from the structure. The narrative and analysis presented in this report rely on the photographs and evidence collected on September 10, 2018, dispatch transcript [5] and videos recorded at the time of the incident, and interviews conducted by a local investigator between September 3, 2018 and September 7, 2018 with fire service personnel involved in the incident and the resident of the structure [6]. The LaGrange Fire Department invited FSRI to study this incident as part of FSRI’s Near-Miss Project which is supported by a DHS/FEMA Assistance to Firefighters Grant. The goal of this project is to enhance the safety and situational awareness of the fire service by applying fire dynamics research results to near-miss or line of duty injury fire incidents. By identifying factors that contributed to the incident, perhaps future incidents may be prevented. FSRI’s analysis of this incident will apply research results and utilize fire research tools, such as computer fire models, to examine key fire phenomena and tactical outcomes. This report will explain the incident, what occurred, why it occurred, and what can be done differently in the future to result in a more favorable outcome
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Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.

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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Ohad, Nir, and Robert Fischer. Regulation of Fertilization-Independent Endosperm Development by Polycomb Proteins. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695869.bard.

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Arabidopsis mutants that we have isolated, encode for fertilization-independent endosperm (fie), fertilization-independent seed2 (fis2) and medea (mea) genes, act in the female gametophyte and allow endosperm to develop without fertilization when mutated. We cloned the FIE and MEA genes and showed that they encode WD and SET domain polycomb (Pc G) proteins, respectively. Homologous proteins of FIE and MEA in other organisms are known to regulate gene transcription by modulating chromatin structure. Based on our results, we proposed a model whereby both FIE and MEA interact to suppress transcription of regulatory genes. These genes are transcribed only at proper developmental stages, as in the central cell of the female gametophyte after fertilization, thus activating endosperm development. To test our model, the following questions were addressed: What is the Composition and Function of the Polycomb Complex? Molecular, biochemical, genetic and genomic approaches were offered to identify members of the complex, analyze their interactions, and understand their function. What is the Temporal and Spatial Pattern of Polycomb Proteins Accumulation? The use of transgenic plants expressing tagged FIE and MEA polypeptides as well as specific antibodies were proposed to localize the endogenous polycomb complex. How is Polycomb Protein Activity Controlled? To understand the molecular mechanism controlling the accumulation of FIE protein, transgenic plants as well as molecular approaches were proposed to determine whether FIE is regulated at the translational or posttranslational levels. The objectives of our research program have been accomplished and the results obtained exceeded our expectation. Our results reveal that fie and mea mutations cause parent-of-origin effects on seed development by distinct mechanisms (Publication 1). Moreover our data show that FIE has additional functions besides controlling the development of the female gametophyte. Using transgenic lines in which FIE was not expressed or the protein level was reduced during different developmental stages enabled us for the first time to explore FIE function during sporophyte development (Publication 2 and 3). Our results are consistent with the hypothesis that FIE, a single copy gene in the Arabidopsis genome, represses multiple developmental pathways (i.e., endosperm, embryogenesis, shot formation and flowering). Furthermore, we identified FIE target genes, including key transcription factors known to promote flowering (AG and LFY) as well as shoot and leaf formation (KNAT1) (Publication 2 and 3), thus demonstrating that in plants, as in mammals and insects, PcG proteins control expression of homeobox genes. Using the Yeast two hybrid system and pull-down assays we demonstrated that FIE protein interact with MEA via the N-terminal region (Publication 1). Moreover, CURLY LEAF protein, an additional member of the SET domain family interacts with FIE as well. The overlapping expression patterns of FIE, with ether MEA or CLF and their common mutant phenotypes, demonstrate the versatility of FIE function. FIE association with different SET domain polycomb proteins, results in differential regulation of gene expression throughout the plant life cycle (Publication 3). In vitro interaction assays we have recently performed demonstrated that FIE interacts with the cell cycle regulatory component Retinobalsoma protein (pRb) (Publication 4). These results illuminate the potential mechanism by which FIE may restrain embryo sac central cell division, at least partly, through interaction with, and suppression of pRb-regulated genes. The results of this program generated new information about the initiation of reproductive development and expanded our understanding of how PcG proteins regulate developmental programs along the plant life cycle. The tools and information obtained in this program will lead to novel strategies which will allow to mange crop plants and to increase crop production.
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Bourrier, Mathilde, Michael Deml, and Farnaz Mahdavian. Comparative report of the COVID-19 Pandemic Responses in Norway, Sweden, Germany, Switzerland and the United Kingdom. University of Stavanger, November 2022. http://dx.doi.org/10.31265/usps.254.

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The purpose of this report is to compare the risk communication strategies and public health mitigation measures implemented by Germany, Norway, Sweden, Switzerland, and the United Kingdom (UK) in 2020 in response to the COVID-19 pandemic based on publicly available documents. The report compares the country responses both in relation to one another and to the recommendations and guidance of the World Health Organization where available. The comparative report is an output of Work Package 1 from the research project PAN-FIGHT (Fighting pandemics with enhanced risk communication: Messages, compliance and vulnerability during the COVID-19 outbreak), which is financially supported by the Norwegian Research Council's extraordinary programme for corona research. PAN-FIGHT adopts a comparative approach which follows a “most different systems” variation as a logic of comparison guiding the research (Przeworski & Teune, 1970). The countries in this study include two EU member States (Sweden, Germany), one which was engaged in an exit process from the EU membership (the UK), and two non-European Union states, but both members of the European Free Trade Association (EFTA): Norway and Switzerland. Furthermore, Germany and Switzerland govern by the Continental European Federal administrative model, with a relatively weak central bureaucracy and strong subnational, decentralised institutions. Norway and Sweden adhere to the Scandinavian model—a unitary but fairly decentralised system with power bestowed to the local authorities. The United Kingdom applies the Anglo-Saxon model, characterized by New Public Management (NPM) and decentralised managerial practices (Einhorn & Logue, 2003; Kuhlmann & Wollmann, 2014; Petridou et al., 2019). In total, PAN-FIGHT is comprised of 5 Work Packages (WPs), which are research-, recommendation-, and practice-oriented. The WPs seek to respond to the following research questions and accomplish the following: WP1: What are the characteristics of governmental and public health authorities’ risk communication strategies in five European countries, both in comparison to each other and in relation to the official strategies proposed by WHO? WP2: To what extent and how does the general public’s understanding, induced by national risk communication, vary across five countries, in relation to factors such as social capital, age, gender, socio-economic status and household composition? WP3: Based on data generated in WP1 and WP2, what is the significance of being male or female in terms of individual susceptibility to risk communication and subsequent vulnerability during the COVID-19 outbreak? WP4: Based on insight and knowledge generated in WPs 1 and 2, what recommendations can we offer national and local governments and health institutions on enhancing their risk communication strategies to curb pandemic outbreaks? WP5: Enhance health risk communication strategies across five European countries based upon the knowledge and recommendations generated by WPs 1-4. Pre-pandemic preparedness characteristics All five countries had pandemic plans developed prior to 2020, which generally were specific to influenza pandemics but not to coronaviruses. All plans had been updated following the H1N1 pandemic (2009-2010). During the SARS (2003) and MERS (2012) outbreaks, both of which are coronaviruses, all five countries experienced few cases, with notably smaller impacts than the H1N1 epidemic (2009-2010). The UK had conducted several exercises (Exercise Cygnet in 2016, Exercise Cygnus in 2016, and Exercise Iris in 2018) to check their preparedness plans; the reports from these exercises concluded that there were gaps in preparedness for epidemic outbreaks. Germany also simulated an influenza pandemic exercise in 2007 called LÜKEX 07, to train cross-state and cross-department crisis management (Bundesanstalt Technisches Hilfswerk, 2007). In 2017 within the context of the G20, Germany ran a health emergency simulation exercise with WHO and World Bank representatives to prepare for potential future pandemics (Federal Ministry of Health et al., 2017). Prior to COVID-19, only the UK had expert groups, notably the Scientific Advisory Group for Emergencies (SAGE), that was tasked with providing advice during emergencies. It had been used in previous emergency events (not exclusively limited to health). In contrast, none of the other countries had a similar expert advisory group in place prior to the pandemic. COVID-19 waves in 2020 All five countries experienced two waves of infection in 2020. The first wave occurred during the first half of the year and peaked after March 2020. The second wave arrived during the final quarter. Norway consistently had the lowest number of SARS-CoV-2 infections per million. Germany’s counts were neither the lowest nor the highest. Sweden, Switzerland and the UK alternated in having the highest numbers per million throughout 2020. Implementation of measures to control the spread of infection In Germany, Switzerland and the UK, health policy is the responsibility of regional states, (Länders, cantons and nations, respectively). However, there was a strong initial centralized response in all five countries to mitigate the spread of infection. Later on, country responses varied in the degree to which they were centralized or decentralized. Risk communication In all countries, a large variety of communication channels were used (press briefings, websites, social media, interviews). Digital communication channels were used extensively. Artificial intelligence was used, for example chatbots and decision support systems. Dashboards were used to provide access to and communicate data.
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Eshed, Yuval, and John Bowman. Harnessing Fine Scale Tuning of Endogenous Plant Regulatory Processes for Manipulation of Organ Growth. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696519.bard.

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Background and objectives: Manipulation of plant organ growth is one of the primary reasons for the success of mankind allowing increasing amounts of food for human and livestock consumption. In contrast with the successful selection for desirable growth characteristics using plant breeding, transgenic manipulations with single genes has met limited success. While breeding is based on accumulation of many small alterations of growth, usually arise from slight changes in expression patterns, transgenic manipulations are primarily based on drastic, non-specific up-regulation or knock down of genes that can exert different effects during different stages of development. To successfully harness transgenic manipulation to attain desirable plant growth traits we require the tools to subtly regulate the temporal and spatial activity of plant growth genes. Polar morphology along the adaxial/abaxial axis characterizes lateral organs of all plants. Juxtaposition of two cell types along this axis is a prerequisite of laminar growth induction. In the study summarized here, we addressed the following questions: Can we identify and harness components of the organ polarity establishment pathway for prolonged growth? Can we identify specific regulatory sequences allowing spatial and temporal manipulation in various stages of organ development? Can we identify genes associated with YABBY-induced growth alterations? Major conclusions and implications: We showed that regulated expression, both spatially and temporally of either organ polarity factors such as the YABBY genes, or the organ maturation program such as the CIN-TCPs can stimulate substantial growth of leaves and floral organs. Promoters for such fine manipulation could be identified by comparison of non-coding sequences of KAN1, where a highly conserved domain was found within the second intron, or by examination of multiple 5” regions of genes showing transient expression along leaf ontogeny. These promoters illustrate the context dependent action of any gene we examined thus far, and facilitate fine tuning of the complex growth process. Implications, both scientific and agricultural. The present study was carried out on the model organism Arabidopsis, and the broad application of its findings were tested in the tomato crop. We learned that all central regulators of organ polarity are functionally conserved, probably in all flowering plants. Thus, with minor modifications, the rules and mechanisms outlined in this work are likely to be general.
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