Journal articles on the topic 'Crowd risk prediction'
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Harihara Subramanian, Gayathri, and Ashish Verma. "Crowd risk prediction in a spiritually motivated crowd." Safety Science 155 (November 2022): 105877. http://dx.doi.org/10.1016/j.ssci.2022.105877.
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Lee, Ris S. C., and Roger L. Hughes. "Prediction of human crowd pressures." Accident Analysis & Prevention 38, no. 4 (July 2006): 712–22. http://dx.doi.org/10.1016/j.aap.2006.01.001.
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Fu, Runshan, Yan Huang, and Param Vir Singh. "Crowds, Lending, Machine, and Bias." Information Systems Research 32, no. 1 (March 1, 2021): 72–92. http://dx.doi.org/10.1287/isre.2020.0990.
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Can machines outperform crowds in financial lending decisions? Using data from a crowd-lending platform, we show that, compared with portfolios created by crowds, a reasonably sophisticated machine can construct financial portfolios that provide better returns while controlling for risk. Further, we find that the machine-created portfolios benefit not only the lenders, but also the borrowers. Borrowers receive loans at a much lower interest rate as the machine can weed out the riskiest loans better than the crowds. We also find suggestive evidence of algorithmic bias in machine decisions. We find that, compared with women, men are more likely to receive loans by machine. We propose a general and effective “debiasing” method that can be applied to any prediction-focused machine learning (ML) applications. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still improves the crowd’s investment decisions in our context. Our results indicate that ML can help crowd-lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources.
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Zhang, Meihua, Yuan Yao, and Kefan Xie. "Prediction and Diversion Mechanisms for Crowd Management Based on Risk Rating." Engineering 09, no. 05 (2017): 377–87. http://dx.doi.org/10.4236/eng.2017.95021.
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Zhao, Hui, Runran Miao, Fei Lin, and Guoan Zhao. "Risk Score for Prediction of Acute Kidney Injury in Patients with Acute ST-Segment Elevation Myocardial Infarction." Disease Markers 2022 (December 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/7493690.
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Background. Acute kidney injury (AKI) is an important comorbidity of ST-Segment Elevation Myocardial Infarction (STEMI) and worsens the prognosis. The purpose of this study was to investigate the relationship between clinical data, test results, surgical findings, and AKI in STEMI patients and to develop a simple, practical model for predicting the risk of AKI. Method. Prognostic prediction research with clinical risk score development was conducted. The data used for model development was derived from the database of the Henan Province Cardiovascular Disease Clinical Data and Sample Resource Bank Engineering Research Center. The data used for external validation was derived from the China Chest Pain Center database. The study endpoint was defined as the occurrence of AKI. Logistic regression analysis was used to identify independent predictors of AKI. Logistic coefficients of each predictor were used for score weighting and transformation. The predictive performance of the newly derived risk scores was validated, respectively, by receiver operating characteristic (ROC) regression in the development population and the external validation population. Result. A total of 364 patients, 57 (15.6%) with AKI and 307 (84.4%) without AKI, were included for score derivation. The validation crowd includes 88 STEMI patients in different institutions. A total of 11 potential predictors were explored under the multivariable logistic regression model. The new risk score was based on five final predictors which were age > 72 years , ejection fraction of no more than 40%, baseline serum creatinine > 102.7 mmol / L , red blood cell distribution width > 13.15 , and culprit lesion located in the middistal segment. With only five predictor variables, the score predicted the risk of AKI with the effective discriminative ability (area under the receiver operating characteristic curve (AuROC): 0.721, 95% confidence interval (CI): 0.652-0.790). In the external validation, the newly developed score confirmed a similar discrimination as the crowd used for derivation (AuROC: 0.731, 95% CI: 0.624-0.838). Conclusion. The newly developed score was proved to have good predictive performance and could be practically applied for risk stratification of AKI in STEMI patients.
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Xu, Xiaojun, Sen Xiong, Yifeng Huang, and Rong Qin. "Prediction of Epidemic Transmission Path and Risk Management Method in Urban Subway." Mathematical Problems in Engineering 2022 (May 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7555251.
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With the development of COVID-19, the epidemic prevention requirements of city subway system have become stricter. This study studies the transmission path of epidemic disease in city subway system. Using FLUENT software and AnyLogic software, the simulation models of subway platform ventilation structure and crowd behavior mode in subway system are constructed, respectively, and SEIR (vulnerable exposed affected recovered) is used as the general infection model of epidemic disease. According to the actual situation, the parameters such as shoulder width, flow, and moving speed of crowd are determined, and the simulation analysis of epidemic disease transmission in subway system is carried out. The analysis results show that the transmission speed of the disease in the subway will increase with the enhancement of the transmission capacity of the disease and the increase of the contact rate. When the disease transmission capacity is 0.14, the number of latent persons reaches the peak at 14.115 time units, which is 1374, and the number of patients reaches the peak at 28.541 time units, which is 1925. According to the simulation results, the simulation analysis results show that with the enhancement of disease transmission ability and the increase of exposure rate, the maximum number of latent and sick people in the subway environment will increase. The corresponding suggestions on risk management and control of infectious disease transmission in subway are put forward. The research results can provide a useful reference for the epidemic prevention management of urban subway transportation system in China.
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Kondofersky, Ivan, Michael Laimighofer, Christoph Kurz, Norbert Krautenbacher, Julia F. Söllner, Philip Dargatz, Hagen Scherb, Donna P. Ankerst, and Christiane Fuchs. "Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration." F1000Research 5 (November 16, 2016): 2671. http://dx.doi.org/10.12688/f1000research.8680.1.
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In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patients with metastatic castration-resistant prostate cancer treated with first-line docetaxel. A subset of an independent clinical trial was used for interim evaluation of model submissions, providing critical feedback to participating teams for tailoring their models to the desired target. Final submitted models were evaluated and ranked on the independent clinical trial. Our team, called "A Bavarian Dream", utilized many of the common statistical methods for data dimension reduction and summarization during the trial. Three general modeling principles emerged that were deemed helpful for building accurate risk prediction tools and ending up among the winning teams of both sub-challenges. These principles included: first, good data, encompassing the collection of important variables and imputation of missing data; second, wisdom of the crowd, extending beyond the usual model ensemble notion to the inclusion of experts on specific risk ranges; and third, recalibration, entailing transfer learning to the target source. In this study, we illustrate the application and impact of these principles applied to data from the Prostate Cancer DREAM Challenge.
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Li, Zhihong, Shiyao Qiu, Xiaoyu Wang, and Li Zhao. "Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments." International Journal of Environmental Research and Public Health 19, no. 24 (December 12, 2022): 16664. http://dx.doi.org/10.3390/ijerph192416664.
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Human movements in complex traffic environments have been successfully simulated by various models. It is crucial to improve crowd safety and urban resilience. However, few studies focus on reproducing human behavior and predicting escape reaction time in the initial judgement stage in complex traffic environments. In this paper, a pedestrian pre-evacuation decision-making model considering pedestrian heterogeneity is proposed for complex environments. Firstly, the model takes different obvious factors into account, including cognition, information, experience, habits, stress, and decision-making ability. Then, according to the preference of the escapees, the personnel decision-making in each stage is divided into two types: stay and escape. Finally, multiple influencing factors are selected to construct the regression equation for prediction of the escape opportunity. The results show that: (1) Choices of escape opportunity are divided into several stages, which are affected by the pedestrian individual risk tolerance, risk categories strength, distance from danger, and reaction of the neighborhood crowd. (2) There are many important factors indicating the pedestrian individual risk tolerance, in which Gen, Group, Time and Mode are a positive correlation, while Age and Zone are a negative correlation. (3) The analysis of the natural response rate of different evacuation strategies shows that 19.81% of people evacuate immediately. The research in this paper can better protect public safety and promote the normal activities of the population.
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Seyednasrollah, Fatemeh, Devin C. Koestler, Tao Wang, Stephen R. Piccolo, Roberto Vega, Russell Greiner, Christiane Fuchs, et al. "A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer." JCO Clinical Cancer Informatics, no. 1 (November 2017): 1–15. http://dx.doi.org/10.1200/cci.17.00018.
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Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. Results In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. Conclusion This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.
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Shiga, Motoki. "Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer." F1000Research 5 (November 16, 2016): 2678. http://dx.doi.org/10.12688/f1000research.8201.1.
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Metastatic castrate resistant prostate cancer (mCRPC) is the major cause of death in prostate cancer patients. Even though some options for treatment of mCRPC have been developed, the most effective therapies remain unclear. Thus finding key patient clinical variables related with mCRPC is an important issue for understanding the disease progression mechanism of mCRPC and clinical decision making for these patients. The Prostate Cancer DREAM Challenge is a crowd-based competition to tackle this essential challenge using new large clinical datasets. This paper proposes an effective procedure for predicting global risks and survival times of these patients, aimed at sub-challenge 1a and 1b of the Prostate Cancer DREAM challenge. The procedure implements a two-step feature selection procedure, which first implements sparse feature selection for numerical clinical variables and statistical hypothesis testing of differences between survival curves caused by categorical clinical variables, and then implements a forward feature selection to narrow the list of informative features. Using Cox’s proportional hazards model with these selected features, this method predicted global risk and survival time of patients using a linear model whose input is a median time computed from the hazard model. The challenge results demonstrated that the proposed procedure outperforms the state of the art model by correctly selecting more informative features on both the global risk prediction and the survival time prediction.
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Ryoba, Michael J., Shaojian Qu, Ying Ji, and Deqiang Qu. "The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform." Sustainability 12, no. 18 (September 16, 2020): 7642. http://dx.doi.org/10.3390/su12187642.
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Only a small percentage of crowdfunding projects succeed in securing funds, the fact of which puts the sustainability of crowdfunding platforms at risk. Researchers have examined the influences of phased aspects of communication, drawn from updates and comments, on success of crowdfunding campaigns, but in most cases they have focused on the combined effects of the aspects. This paper investigated campaign success contribution of various combinations of phased communication aspects from updates and comments, the best of which can help creators to successfully manage campaigns by focusing on the important communication aspects. Metaheuristic and machine learning algorithms were used to search and evaluate the best combination of phased communication aspects for predicting success using Kickstarter dataset. The study found that the number of updates in phase one, the polarity of comments in phase two, readability of updates and polarity of comments in phase three, and the polarity of comments in phase five are the most important communication aspects in predicting campaign success. Moreover, the success prediction accuracy with the aspects identified after phasing is more than the baseline model without phasing. Our findings can help crowdfunding actors to focus on the important communication aspects leading to improved likelihood of success.
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Ratner, JJ, JJ Sury, MR James, TA Mather, and DM Pyle. "Crowd-sourcing structure-from- motion data for terrain modelling in a real-world disaster scenario: A proof of concept." Progress in Physical Geography: Earth and Environment 43, no. 2 (February 24, 2019): 236–59. http://dx.doi.org/10.1177/0309133318823622.
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Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for disaster risk reduction purposes. Three groups with variable SfM knowledge utilized 16 different camera models, including four camera phones, to collect 1001 total photos in one hour of data collection. Datasets collected by each group were processed using VisualSFM, and the point densities, accuracies and distributions of points in the resultant point clouds (DTM skeletons) were compared. Our results show that the point clouds are resilient to inconsistency in users’ SfM knowledge: crowd-sourced data collected by a moderately informed general public yields topography results comparable in data density and accuracy to those produced with data collected by highly-informed SfM users or experts using LiDAR. This means that in a real-world scenario involving participants with a diverse range of expertise, topography models could be produced from crowd-sourced data quite rapidly and to a very high standard. This could be beneficial to disaster risk reduction as a relatively quick, simple and low-cost method to attain rapidly updated knowledge of terrain attributes, useful for the prediction and mitigation of many natural hazards.
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Elbasi, Ersin, Ahmet E. Topcu, and Shinu Mathew. "Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning." Electronics 10, no. 14 (July 14, 2021): 1677. http://dx.doi.org/10.3390/electronics10141677.
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COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.
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Ullah, Tahira, Sven Lautenbach, Benjamin Herfort, Marcel Reinmuth, and Danijel Schorlemmer. "Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe." ISPRS International Journal of Geo-Information 12, no. 4 (March 27, 2023): 143. http://dx.doi.org/10.3390/ijgi12040143.
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Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.
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Stalgaitis, Carolyn Ann, Mayo Djakaria, and Jeffrey Washington Jordan. "The Vaping Teenager: Understanding the Psychographics and Interests of Adolescent Vape Users to Inform Health Communication Campaigns." Tobacco Use Insights 13 (January 2020): 1179173X2094569. http://dx.doi.org/10.1177/1179173x20945695.
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Background: Adolescent vaping continues to rise, yet little is known about teen vape users beyond demographics. Effective intervention requires a deeper understanding of the psychographics and interests of adolescent vape users to facilitate targeted communication campaigns. Methods: We analyzed the 2017-2018 weighted cross-sectional online survey data from Virginia high school students (N = 1594) to identify and describe subgroups of adolescents who vaped. Participants reported 30-day vape use, identification with 5 peer crowds (Alternative, Country, Hip Hop, Mainstream, Popular), social prioritization, agreement with personal values statements, social media and smartphone use, and television and event preferences. We compared vaping rates and frequency by peer crowd using a chi-square analysis with follow-up testing to identify higher-risk crowds and confirmed associations using binary and multinomial logistic regression models with peer crowd scores predicting vaping, controlling for demographics. We then used chi-square and t tests to describe the psychographics, media use, and interests of higher-risk peer crowds and current vape users within those crowds. Results: Any current vaping was the highest among those with Hip Hop peer crowd identification (25.4%), then Popular (21.3%). Stronger peer crowd identification was associated with increased odds of any current vaping for both crowds, vaping on 1 to 19 days for both crowds, and vaping on 20 to 30 days for Hip Hop only. Compared with other peer crowds and non-users, Hip Hop and Popular youth and current vape users reported greater social prioritization and agreement with values related to being social and fashionable. Hip Hop and Popular youth and current vape users reported heavy Instagram and Snapchat use, as well as unique television show and event preferences. Conclusions: Hip Hop and Popular adolescents are most likely to vape and should be priority audiences for vaping prevention campaigns. Findings should guide the development of targeted health communication campaigns delivered via carefully designed media strategies.
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Bain, Nicolas, and Denis Bartolo. "Dynamic response and hydrodynamics of polarized crowds." Science 363, no. 6422 (January 3, 2019): 46–49. http://dx.doi.org/10.1126/science.aat9891.
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Modeling crowd motion is central to situations as diverse as risk prevention in mass events and visual effects rendering in the motion picture industry. The difficulty of performing quantitative measurements in model experiments has limited our ability to model pedestrian flows. We use tens of thousands of road-race participants in starting corrals to elucidate the flowing behavior of polarized crowds by probing its response to boundary motion. We establish that speed information propagates over system-spanning scales through polarized crowds, whereas orientational fluctuations are locally suppressed. Building on these observations, we lay out a hydrodynamic theory of polarized crowds and demonstrate its predictive power. We expect this description of human groups as active continua to provide quantitative guidelines for crowd management.
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Mason, Michael J., Carolina D. Schinke, Christine Eng, Fadi Towfic, Fred Gruber, Brian S. White, Yi Cui, et al. "Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker." Blood 134, Supplement_1 (November 13, 2019): 4370. http://dx.doi.org/10.1182/blood-2019-127362.
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Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.
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Alnabulsi, Hani, John Drury, and Anne Templeton. "Predicting collective behaviour at the Hajj: place, space and the process of cooperation." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1753 (July 2, 2018): 20170240. http://dx.doi.org/10.1098/rstb.2017.0240.
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Around 2 million pilgrims attend the annual Hajj to Mecca and the holy places, which are subject to dense crowding. Both architecture and psychology can be part of disaster risk reduction in relation to crowding, since both can affect the nature of collective behaviour—particularly cooperation—among pilgrims. To date, collective behaviour at the Hajj has not been systematically investigated from a psychological perspective. We examined determinants of cooperation in the Grand Mosque and plaza during the pilgrimage. A questionnaire survey of 1194 pilgrims found that the Mosque was perceived by pilgrims as one of the most crowded ritual locations. Being in the plaza (compared with the Mosque) predicted the extent of cooperation, though crowd density did not. Shared social identity with the crowd explained more of the variance than both location and density. We examined some of the process underlying cooperation. The link between shared social identity and giving support to others was stronger in the plaza than in the Mosque, and suggests the role of place and space in modulating processes of cooperation in crowds. These findings have implications for disaster risk reduction and for applications such as computer simulations of crowds in pilgrimage locations. This article is part of the theme issue ‘Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour’.
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Adjodah, Dhaval, Yan Leng, Shi Kai Chong, P. M. Krafft, Esteban Moro, and Alex Pentland. "Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions." Entropy 23, no. 7 (June 24, 2021): 801. http://dx.doi.org/10.3390/e23070801.
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A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.
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Turris, Sheila A., Adam Lund, Alison Hutton, Ron Bowles, Elizabeth Ellerson, Malinda Steenkamp, Jamie Ranse, and Paul Arbon. "Mass-gathering Health Research Foundational Theory: Part 2 - Event Modeling for Mass Gatherings." Prehospital and Disaster Medicine 29, no. 6 (November 17, 2014): 655–63. http://dx.doi.org/10.1017/s1049023x14001228.
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AbstractBackgroundCurrent knowledge about mass-gathering health (MGH) fails to adequately inform the understanding of mass gatherings (MGs) because of a relative lack of theory development and adequate conceptual analysis. This report describes the development of a series of event lenses that serve as a beginning “MG event model,” complimenting the “MG population model” reported elsewhere.MethodsExisting descriptions of “MGs” were considered. Analyzing gaps in current knowledge, the authors sought to delineate the population of events being reported. Employing a consensus approach, the authors strove to capture the diversity, range, and scope of MG events, identifying common variables that might assist researchers in determining when events are similar and might be compared. Through face-to-face group meetings, structured breakout sessions, asynchronous collaboration, and virtual international meetings, a conceptual approach to classifying and describing events evolved in an iterative fashion.FindingsEmbedded within existing literature are a variety of approaches to event classification and description. Arising from these approaches, the authors discuss the interplay between event demographics, event dynamics, and event design. Specifically, the report details current understandings about event types, geography, scale, temporality, crowd dynamics, medical support, protective factors, and special hazards. A series of tables are presented to model the different analytic lenses that might be employed in understanding the context of MG events.InterpretationThe development of an event model addresses a gap in the current body of knowledge vis a vis understanding and reporting the full scope of the health effects related to MGs. Consistent use of a consensus-based event model will support more rigorous data collection. This in turn will support meta-analysis, create a foundation for risk assessment, allow for the pooling of data for illness and injury prediction, and support methodology for evaluating health promotion, harm reduction, and clinical response interventions at MGs.TurrisSA, LundA, HuttonA, BowlesR, EllersonE, SteenkampM, RanseJ, ArbonP. Mass-gathering health research foundational theory: part 2 - event modeling for mass gatherings. Prehosp Disaster Med. 2014;29(6):1-9.
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Barker, Jason S., Andrew N. Gray, and Jeremy S. Fried. "The Effects of Crown Scorch on Post-fire Delayed Mortality Are Modified by Drought Exposure in California (USA)." Fire 5, no. 1 (February 2, 2022): 21. http://dx.doi.org/10.3390/fire5010021.
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Accurately predicting the mortality of trees that initially survive a fire event is important for management, such as planning post-fire salvage, planting, and prescribed fires. Although crown scorch has been successfully used to predict post-fire mortality (greater than one-year post-fire), it remains unclear whether other first-order fire effect metrics (e.g., stem char) and information on growing conditions can improve such predictions. Droughts can also elevate mortality and may interact, synergistically, with fire effects to influence post-fire tree survival. We used logistic regression to test whether drought exposure, as indicated by summarized monthly Palmer Drought Severity Index (PDSI) over ten-years could improve predictions of delayed mortality (4–9 years post-fire) at the individual tree level in fire-affected forest inventory and analysis (FIA) plots in California (USA). We included crown scorch, bark thickness, stem char, soil char, slope, and aspect in the model as predictors. We selected the six most prevalent species to include in the model: canyon live oak, Douglas-fir, Jeffrey pine, incense-cedar, ponderosa pine, and white fir. Mean delayed mortality, based on tree count, across all FIA plots across all tree species and plots was 17%, and overall accuracy was good (AUC = 79%). Our model performed well, correctly predicting survivor trees (sensitivity of 0.98) but had difficulty correctly predicting the smaller number of mortality trees (specificity of 0.27) at the standard probability=0.5 mortality threshold. Crown scorch was the most influential predictor of tree mortality. Increasing crown scorch was associated with greater risk of delayed mortality for all six species, with trees exhibiting over 75% crown scorch having a probability of dying that exceeded 0.5. Increasing levels of stem char and soil char (first order indicators) were associated with increasing mortality risk but to less effect than crown scorch. We expected that greater drought exposure would increase delayed post-fire mortality, but we found that increasing drought exposure (median and minimum PDSI) was associated with a modest decrease in post-fire mortality. However, we did find that trees with high levels of crown scorch were less likely to survive with increasing drought exposure (median PDSI). Delayed mortality risk decreased as terrain slope increased. Taken together, our results suggest that trees with substantial crown damage may be more vulnerable to delayed mortality if exposed to drought and that crown scorch is an effective post-fire mortality predictor up to 10 years post-fire.
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Masud, Mehedi, Parminder Singh, Gurjot Singh Gaba, Avinash Kaur, Roobaea Alrobaea Alghamdi, Mubarak Alrashoud, and Salman Ali Alqahtani. "CROWD: Crow Search and Deep Learning based Feature Extractor for Classification of Parkinson’s Disease." ACM Transactions on Internet Technology 21, no. 3 (June 9, 2021): 1–18. http://dx.doi.org/10.1145/3418500.
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Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm (ACSA) and Deep Learning (DL)–based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson’s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson’s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.
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Goldenberg, Mitchell G., Jamal Nabhani, Christopher J. D. Wallis, Sameer Chopra, Andrew J. Hung, Anne Schuckman, Hooman Djaladat, et al. "Feasibility of expert and crowd-sourced review of intraoperative video for quality improvement of intracorporeal urinary diversion during robotic radical cystectomy." Canadian Urological Association Journal 11, no. 10 (October 12, 2017): 331–6. http://dx.doi.org/10.5489/cuaj.4442.
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Introduction: Development of uretero-ileal stricture (UIS) after robotic-assisted radical cystectomy (RARC) may be dependent on surgical technique. Video review of intraoperative technique is an emerging paradigm for surgical quality improvement. We examined whether surgeon-perceived risk of UIS or crowd-sourced assessment of robotic skill are associated with the development of UIS.Methods: We conducted a case-control study comparing the operative technique of uretero-ileal anastomoses resulting in clinically significant UIS with the contralateral anastomosis for the same patient. De-identified videos were analyzed by 1) five high-volume surgeons; and 2) crowd workers (Crowd-Sourced Assessment of Technical Skill, C-SATS) to determine Global Evaluative Assessment of Robotic Skill (GEARS) score. Mantel-Haenszel common odds ratio (OR) estimates were calculated to assess the association between surgeon performance and the development of UIS. Logistic regression models were used to examine the association between GEARS scores and the development of UIS.Results: A total of 10 UIS videos were compared to eight control videos by five surgeons and 2142 crowd workers. Expert surgeons systematically evaluated intraoperative footage, however, no association between the expert mode response and UIS (OR 0.42; 95% confidence interval [CI] 0.05‒3.45; p=0.91) was identified. Crowd-sourced assessment was not predictive of UIS (p=0.62).Conclusions: We used video review to systematically analyze procedure-specific content and technique. The inability of surgeons to predict UIS may reflect the questionnaire, uncontrolled patient factors, or a lack of power. Crowd-sourced GEARS score was unsuccessful in predicting UIS after RARC.
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Zipori, Yaniv, Karen Reidy, T. Gilchrist, Lex W. Doyle, and Mark P. Umstad. "The Outcome of Monochorionic Diamniotic Twins Discordant at 11 to 13+6 Weeks’ Gestation." Twin Research and Human Genetics 19, no. 6 (October 21, 2016): 692–96. http://dx.doi.org/10.1017/thg.2016.81.
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Background: Monochorionic diamniotic (MCDA) twin pregnancies are associated with adverse perinatal outcome. Intertwin discordances at the time of nuchal translucency (NT) screening may have a value in the prediction of fetal loss or twin-to-twin transfusion syndrome. We aimed to determine the ability of intertwin NT and crown rump length (CRL) discordances among MCDA twins to predict adverse outcomes. Material and Methods: All MCDA twins with a documented routine ultrasound at 11 to 13+6 weeks’ gestation, and known pregnancy outcome between August 2003 and August 2012 were included. Receiver operating characteristic curves were used to determine the ideal NT and CRL discordances cut-off points that maximized the ability to predict adverse outcome, which was defined as any of: death of one or both twins, twin-to-twin transfusion syndrome, or estimated fetal weight or birth weight discordances ≥25%. Results: Of the 89 cases, 20 (22.5%) had at least one adverse outcome. NT discordance was more discriminatory of adverse outcome than was CRL discordance. The optimal values for predicting any adverse outcomes for NT were >23.7% and for CRL >3.5%. The positive predictive values for NT (52.4%) and CRL (29.8%) screening were relatively low; however, the lack of either NT or CRL discordances was more reassuring, with negative predictive values of 86.8% and 86.4%, respectively. Conclusions: NT discordance is more predictive for adverse fetal outcome in MCDA twins than CRL discordance. Neither NT nor CRL discordance are likely to modify the intensive monitoring required for these very high-risk pregnancies.
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Theil, Kilian, Dirk Hovy, and Heiner Stuckenschmidt. "Top-Down Influence? Predicting CEO Personality and Risk Impact from Speech Transcripts." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 832–41. http://dx.doi.org/10.1609/icwsm.v17i1.22192.
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How much does a CEO’s personality impact the performanceof their company? Management theory posits a great influence, but it is difficult to show empirically—there is a lack of publicly available self-reported personality data of top managers. Instead, we propose a text-based personality regressor based on crowd-sourced Myers–Briggs Type Indicator (MBTI) assessments. The ratings have a high internal and external validity and can be predicted with moderate to strong correlations for three out of four dimensions. Providing evidence for the upper echelons theory, we demonstrate that the predicted CEO personalities have explanatory power of financial risk.
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Azuma, David, Vicente J. Monleon, and Donald Gedney. "Equations for Predicting Uncompacted Crown Ratio Based on Compacted Crown Ratio and Tree Attributes." Western Journal of Applied Forestry 19, no. 4 (October 1, 2004): 260–67. http://dx.doi.org/10.1093/wjaf/19.4.260.
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Abstract Equations to predict uncompacted crown ratio as a function of compacted crown ratio, tree diameter, and tree height are developed for the main tree species in Oregon, Washington, and California using data from the Forest Health Monitoring Program, USDA Forest Service. The uncompacted crown ratio was modeled with a logistic function and fitted using weighted, nonlinear regression. The models were evaluated using cross-validation. Mean squared error of predicted uncompacted crown ratio was between 0.1 and 0.15, overall bias was negligible, and correlation between the predicted and observed uncompacted crown ratio was high for most species. The sensitivity of crown fire risk to crown ratio estimation method was evaluated using the Fire and Fuels Extension of the Forest Vegetation Simulator. Torching index, an estimate of the wind speed needed for a crown fire to develop, was significantly greater when compacted crown ratio was used instead of uncompacted crown ratio. The close agreement in torching indices simulated using predicted and observed uncompacted crown ratio provides further evidence of the utility of the models developed in this study. West. J. Appl. For. 19(4):260–267.
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Cruz, Miguel G., Bret W. Butler, and Martin E. Alexander. "Predicting the ignition of crown fuels above a spreading surface fire. Part II: model evaluation." International Journal of Wildland Fire 15, no. 1 (2006): 61. http://dx.doi.org/10.1071/wf05045.
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A crown fuel ignition model (CFIM) describing the temperature rise and subsequent ignition of the lower portion of tree crowns above a spreading surface fire was evaluated through a sensitivity analysis, comparison against other models, and testing against experimental fire data. Results indicate that the primary factors influencing crown fuel ignition are those determining the depth of the surface fire burning zone and the vertical distance between the ground/surface fuel strata and the lower boundary of the crown fuel layer. Intrinsic crown fuel properties such as fuel particle surface area-to-volume ratio and foliar moisture content were found to have a minor influence on the process of crown fuel ignition. Comparison of model predictions against data collected in high-intensity experimental fires and predictions from other models gave encouraging results relative to the validity of the model system.
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Satofuka, Yoshifumi, Toshio Mori, Takahisa Mizuyama, Kiichiro Ogawa, and Kousuke Yoshino. "Prediction of Floods Caused by Landslide Dam Collapse." Journal of Disaster Research 5, no. 3 (June 1, 2010): 288–95. http://dx.doi.org/10.20965/jdr.2010.p0288.
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Landslide dam formation and deformation strongly affect water and sediment runoff. When a large-scale landslide dam collapses due to overflow erosion, peak flood discharge may exceed inflow discharge by several times. Such an abrupt flow discharge increase by a dam burst may cause serious damage downstream. We propose a one-dimensional model for river-bed variation and flood runoff consisting of a two-layer model for immature debris flow and a bank erosion model. We applied this model to the Nonoo landslide dam in Japan’s Miyazaki Prefecture, formed by typhoon Nabi in September 2005, and China’s Tangjiashan landslide dam formed in the Wenchuan earthquake in May 2008. The model reproduces the observed flood runoff processes in the two areas. Calculated results suggest that peak flood discharge diminishes when water accumulating behind the landslide dam is small, and excavating the landslide dam crown effectively reduces flood discharge.
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Storey, Michael, Owen Price, and Elizabeth Tasker. "The role of weather, past fire and topography in crown fire occurrence in eastern Australia." International Journal of Wildland Fire 25, no. 10 (2016): 1048. http://dx.doi.org/10.1071/wf15171.
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We analysed the influence of weather, time since fire (TSF) and topography on the occurrence of crown fire, as mapped from satellite imagery, in 23 of the largest wildfires in dry sclerophyll forests in eastern Australia from 2002 to 2013. Fires were analysed both individually and as groups. Fire weather was the most important predictor of crown consumption. TSF (a surrogate for fuel accumulation) had complex nonlinear effects that varied among fires. Crown fire likelihood was low up to 4 years post-fire, peaked at ~10 years post-fire and then declined. There was no clear indication that recent burning became more or less effective as fire weather became more severe. Steeper slope reduced crown fire likelihood, contrary to the assumptions of common fire behaviour equations. More exposed areas (ridges and plains) had higher crown fire likelihood. Our results suggest prescribed burning to maintain an average of 10 years’ TSF may actually increase crown fire likelihood, but burning much more frequently can be effective for risk reduction. Our results also suggest the effects of weather, TSF and slope are not adequately represented in the underlying equations of most fire behaviour models, potentially leading to poor prediction of fire spread and risk.
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Tominaga, Koji, Shaun A. Watmough, and Julian Aherne. "Predicting tree survival in Ontario sugar maple (Acer saccharum) forests based on crown condition." Canadian Journal of Forest Research 38, no. 7 (July 2008): 1730–41. http://dx.doi.org/10.1139/x08-021.
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Decline index (indicator of crown condition) data from 102 forest plots (approximately 10 000 trees) during 1986–2004 were compiled to derive survival models for south-central Ontario, Canada. The dominant species was sugar maple ( Acer saccharum Marsh.) with approximately 75% occurrence (n = 7640). The predictor variables for sugar maple survivorship included the decline index of 1 or 2 years prior to the beginning of the modelled period and ecological region (Algoma, Georgian Bay, Huron–Ontario, and Upper St. Lawrence). The observed crown condition of sugar maple improved significantly over the study period; in contrast, short-term mortality rate did not improve. The risk of sugar maple mortality could be predicted from decline index data for a single year indicating that the risk of tree death increases with higher decline index values (declining crown condition). Moreover, using 2 years of decline index data indicated that the risk of tree death also increased with the length of consecutive time individual trees have higher decline index values. Trees in the Algoma region, which represent the northern limit of sugar maple distribution in Ontario, were significantly more likely to die than trees in Huron–Ontario region.
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Hoffman, C. M., J. Canfield, R. R. Linn, W. Mell, C. H. Sieg, F. Pimont, and J. Ziegler. "Evaluating Crown Fire Rate of Spread Predictions from Physics-Based Models." Fire Technology 52, no. 1 (June 5, 2015): 221–37. http://dx.doi.org/10.1007/s10694-015-0500-3.
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Hollaway, G. J., M. L. Evans, H. Wallwork, C. B. Dyson, and A. C. McKay. "Yield Loss in Cereals, Caused by Fusarium culmorum and F. pseudograminearum, Is Related to Fungal DNA in Soil Prior to Planting, Rainfall, and Cereal Type." Plant Disease 97, no. 7 (July 2013): 977–82. http://dx.doi.org/10.1094/pdis-09-12-0867-re.
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In southeastern Australia, Fusarium crown rot, caused by Fusarium culmorum or F. pseudograminearum, is an increasingly important disease of cereals. Because in-crop control options are limited, it is important for growers to know prior to planting which fields are at risk of yield loss from crown rot. Understanding the relationships between crown rot inoculum and yield loss would assist in assessing the risk of yield loss from crown rot in fields prior to planting. Thirty-five data sets from crown rot management experiments conducted in the states of South Australia and Victoria during the years 2005 to 2010 were examined. Relationships between Fusarium spp. DNA concentrations (inoculum) in soil samples taken prior to planting and disease development and grain yield were evaluated in seasons with contrasting seasonal rainfall. F. culmorum and F. pseudograminearum DNA concentrations in soil prior to planting were found to be positively related to crown rot expression (stem browning and whiteheads) and negatively related to grain yield of durum wheat, bread wheat, and barley. Losses from crown rot were greatest when rainfall during September and October (crop maturation) was below the long-term average. Losses from crown rot were greater in durum wheat than bread wheat and least in barley. Yield losses from F. pseudograminearum were similar to yield losses from F. culmorum. Yield loss patterns were consistent across experiments and between states; therefore, it is reasonable to expect that similar relationships will occur over broad geographic areas. This suggests that quantitative polymerase chain reaction technology and soil sampling could be powerful tools for assessing crown rot inoculum concentrations prior to planting and predicting the risk of yield loss from crown rot wherever this disease is an issue.
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Litwinska, Ewelina, Magdalena Litwinska, Bartosz Czuba, Agnieszka Gach, Sebastian Kwiatkowski, Przemyslaw Kosinski, Piotr Kaczmarek, and Miroslaw Wielgos. "Amniocentesis in Twin Pregnancies: Risk Factors of Fetal Loss." Journal of Clinical Medicine 11, no. 7 (March 31, 2022): 1937. http://dx.doi.org/10.3390/jcm11071937.
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This study aims to determine if second trimester amniocentesis in twin pregnancies provides a significant independent contribution in the prediction of miscarriage or fetal loss at any stage of pregnancy. This was a retrospective cohort study of women with twin gestations booked for routine prenatal care in four fetal medicine units in Poland in the years 2010–2020. The study population included: (1) twin pregnancies that underwent amniocentesis at 16–20 weeks’ gestation; (2) twin pregnancies that did not require any further testing and were followed-up routinely. Univariable and multivariable regression analysis was used to define which maternal and pregnancy characteristics provided a significant independent contribution in the prediction of miscarriage and fetal loss at any stage of pregnancy. In the study period, 2645 twin pregnancies were eligible for analysis. There were 144 cases of miscarriage defined as fetal loss of one or both twins before 24 weeks and 40 cases of intrauterine death of one or both twins after 24 weeks. A total number of 162 twin pregnancies underwent amniocentesis at 16–20 weeks’ gestation. The rate of miscarriage before 24 weeks and the rate of fetal loss at any stage of pregnancy in the group that underwent amniocentesis was 10.49% and 13.58%, respectively, compared to 5.11% and 6.52% that did not undergo amniocentesis. Multivariable regression analysis showed that factors providing a significant independent contribution in the prediction of miscarriage and fetal loss at any stage of pregnancy were monochorionicity (MC), large intertwin discordance in crown-rump length (CRL), low Pregnancy Related Plasma Protein (PAPP-A) MoM and nuchal translucency (NT) above 95th centile. Amniocentesis in twin pregnancies does not provide a significant contribution in the prediction of miscarriage or fetal loss at any stage of pregnancy.
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Cruz, M. G., and M. E. Alexander. "Comments on “Evaluating Crown Fire Rate of Spread Predictions from Physics-Based Models”." Fire Technology 55, no. 6 (April 24, 2019): 1919–25. http://dx.doi.org/10.1007/s10694-019-00856-2.
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Singh, Amandeep, Avepreet Singh, Kamlesh Gupta, and Gauravdeep Singh. "Role of First Trimester Ultrasonographic Parameters for Prediction of Early Pregnancy Failure - A Prospective Observational Study from Punjab, India." Journal of Evidence Based Medicine and Healthcare 8, no. 9 (March 1, 2021): 476–80. http://dx.doi.org/10.18410/jebmh/2021/93.
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BACKGROUND Early pregnancy failure is stated as noncompatible, intrauterine pregnancy with either an empty gestational sac or a gestational sac that contains an embryo or fetus which does not have any fetal cardiac activity in the initial 12 weeks of the pregnancy. In the assessment of early pregnancy, ultrasound plays a significant role. METHODS A prospective observational study was conducted in a tertiary care hospital between May 2019 and April 2020 among 500 pregnant females fulfilling the inclusion and exclusion criteria. Patient follow up was done by weekly telephonic calls until completing 12 weeks gestation or reporting miscarriage. Also, all patients were followed by the recommended routine ultrasound (US) scanning with or without emergency visits. RESULTS In our study period, 500 women fulfilling the inclusion criteria were included in our study. Out of whom, 85 (17.5 %) women had an early pregnancy failure (before 12 weeks). There was significantly lower mean gestational sac diameter (GSD), crown to rump length (CRL), fetal heart rate (FHR), and P-value < 0.001 in women who experienced early pregnancy failure. In pregnancies where the GSD, CRL, and FHR were below the 5th percentile, early pregnancy failure was a more prone outcome. All pregnancies with FHR below 75 beats per minute ended in failure in the present study. When FHR was less than 128 beats per minute, there was enormous rise in the frequency of pregnancy failure. By comparison, yolk sac diameter (YSD) was a less significant predictor of early pregnancy failure. CONCLUSIONS First-trimester ultrasonographic estimations help in predicting early abortion. Risk appraisal tables dependent on combinations of abnormal parameters could significantly help in identifying abnormal pregnancy from normal pregnancy and could improve prediction rates. KEYWORDS Early Pregnancy Failure, Prediction, Transvaginal Ultrasonography, Ultrasonography
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Battaglia, Mike, Frederick W. Smith, and Wayne D. Shepperd. "Predicting mortality of ponderosa pine regeneration after prescribed fire in the Black Hills, South Dakota, USA." International Journal of Wildland Fire 18, no. 2 (2009): 176. http://dx.doi.org/10.1071/wf07163.
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Reduction of crown fire hazard in Pinus ponderosa forests in the Black Hills, SD, often focuses on the removal of overstorey trees to reduce crown bulk density. Dense ponderosa pine regeneration establishes several years after treatment and eventually increases crown fire risk if allowed to grow. Using prescribed fire to control this regeneration is hampered by the limited knowledge of fire-related mortality threshold values for seedlings (<1.4 m tall) and saplings (0.25 to 10 cm diameter at breast height). The present study was initiated to assess fire-related mortality of ponderosa pine seedlings and saplings on prescribed burns across the Black Hills. We established plots in several burn units after the first post-fire growing season to measure crown volume scorch, crown volume consumption, basal scorch, and ground char for ponderosa pine seedlings and saplings. Logistic regression was used to model the probability of mortality based on tree size, flame length, and direct fire effects. Tree size, flame length, crown damage, ground char, and basal char severity were all important factors in the prediction of mortality. Observed mortality was >70% for seedlings but was only 18 to 46% for sapling-sized trees. The differences in mortality thresholds for ponderosa pine seedlings and saplings highlight their susceptibility to different damage pathways and give managers several options when designing burn prescriptions.
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Backhouse, D. "Forecasting the risk of crown rot between successive wheat crops." Australian Journal of Experimental Agriculture 46, no. 11 (2006): 1499. http://dx.doi.org/10.1071/ea04189.
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Published data from long-term trials at Moree, New South Wales (1986–1996), and Billa Billa, Queensland (1986–1993), were analysed to determine the factors that influence the incidence of crown rot, caused by Fusarium pseudograminearum, in successive stubble-retained, no-till wheat crops and to examine the feasibility of developing a forecasting system for the disease. Polyetic progress of the epidemics could be described by a form of the logistic growth model with a carrying capacity (K) about 5% higher than the maximum recorded incidence at each site. Infection rate between seasons was positively correlated with yield and in-crop rainfall in the previous season, both of which were indicators of biomass. Infection rate was negatively correlated with rainfall parameters during the summer fallows, which were indicators of conditions favouring residue decomposition. In-crop rainfall, stored soil moisture and temperature parameters were not significantly correlated with infection rates. Multiple regressions based on incidence in the previous season, summer rainfall and either yield or in-crop rainfall in the previous season accounted for 65–81% of the variation in disease incidence at Moree and 86% of the variation in incidence at Billa Billa. Simplified parameters for use in on-farm forecasting systems were explored. The most useful of these was the square root of the product of incidence and either yield or in-crop rainfall, which gave sufficiently accurate predictions at each site to estimate the qualitative risk of crown rot in the following crop. This could be used to decide whether management options such as resistant varieties, rotations or burning were required.
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Wang, Haoqi, Zhuoran Zhang, and Jun Chen. "Prediction of structural responses induced by single-person jumping through a physical principle based on transfer functions." Advances in Structural Engineering 25, no. 1 (October 2, 2021): 146–57. http://dx.doi.org/10.1177/13694332211046343.
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The vibration caused by human excitation has become a key factor at the structural design stage of large-span structures including footbridges, sport stadia, and high-rise buildings. As the structures tend to become slenderer and lighter, the mass of the crowd is not negligibly small compared with the mass of the structure. In such cases, the crowd and the structure form a coupling system through a mechanism known as human–structure interaction (HSI). Researchers found that the structural responses with and without HSI are different. However, the interaction effect on the structural responses has rarely been quantitatively evaluated from the perspective of human system parameters. In this paper, a novel method using a physical principle to predict jumping-induced structural responses is proposed, in which the structural response is expressed as the multiplication of a series of transfer functions representing human system and structural dynamic properties. Structural responses of a large-span concrete structure under jumping excitation are predicted using the proposed method and identified human system parameters. Comparison with measured responses shows satisfactory agreement. The proposed method provides a solution to consider HSI effect on the calculation of structural responses in the vibration serviceability design for large-span structures.
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Zubaidi, Salah, Hussein Al-Bugharbee, Sandra Ortega-Martorell, Sadik Gharghan, Ivan Olier, Khalid Hashim, Nabeel Al-Bdairi, and Patryk Kot. "A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach." Water 12, no. 6 (June 6, 2020): 1628. http://dx.doi.org/10.3390/w12061628.
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Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that (1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; (2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision.
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Jiménez, Enrique, José Antonio Vega, José María Fernández-Alonso, Daniel Vega-Nieva, Juan Gabriel Álvarez-González, and Ana Daría Ruiz-González. "Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances." Canadian Journal of Forest Research 43, no. 2 (February 2013): 149–58. http://dx.doi.org/10.1139/cjfr-2012-0374.
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Adequate quantification of canopy fuel load and canopy bulk density is required for assessment of the susceptibility of forest stands to crown fire and evaluation of silvicultural treatments aimed at reducing the risk of crowning. The use of tree biomass equations and vertical profile distributions of crown fuels provide the most accurate estimates of the canopy fuel characteristics. In this study, 100 pole-size maritime pine (Pinus pinaster Aiton) trees were destructively sampled in five different sites, covering a wide range of its geographical distribution in the Iberian Peninsula. To estimate crown fuel mass, allometric equations were fitted separately for needles, twigs, and fuel available for crown fire. Models were also fitted to characterize the vertical fuel distributions as a function of tree height. All models were fitted simultaneously to guarantee additivity among tree biomass components, and corrections were also made for heterocedasticity and autocorrelation. Diameter at breast height was the best explanatory variable for all the allometric models. The vertical distribution of crown biomass fractions along tree height depended on the crown size and tree dominance. The system of equations provides a good balance between accurate predictions and low data requirements, allowing quantification of canopy fuel characteristics at stand level.
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Rodríguez-Lizana, Antonio, Alzira Ramos, María João Pereira, Amílcar Soares, and Manuel Castro Ribeiro. "Assessment of the Spatial Variability and Uncertainty of Shreddable Pruning Biomass in an Olive Grove Based on Canopy Volume and Tree Projected Area." Agronomy 13, no. 7 (June 25, 2023): 1697. http://dx.doi.org/10.3390/agronomy13071697.
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Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree−1, with a mean value of 37 kg tree−1. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures.
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Weiner, Carl P., Helen Zhou, Howard Cuckle, Argyro Syngelaki, Kypros H. Nicolaides, Mark L. Weiss, and Yafeng Dong. "Maternal Plasma RNA in First Trimester Nullipara for the Prediction of Spontaneous Preterm Birth ≤ 32 Weeks: Validation Study." Biomedicines 11, no. 4 (April 11, 2023): 1149. http://dx.doi.org/10.3390/biomedicines11041149.
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The first-trimester prediction of spontaneous preterm birth (sPTB) has been elusive, and current screening is heavily dependent on obstetric history. However, nullipara lack a relevant history and are at higher risk for spontaneous (s)PTB ≤ 32 weeks compared to multipara. No available objective first-trimester screening test has proven a fair predictor of sPTB ≤ 32 weeks. We questioned whether a panel of maternal plasma cell-free (PCF) RNAs (PSME2, NAMPT, APOA1, APOA4, and Hsa-Let-7g) previously validated at 16–20 weeks for the prediction of sPTB ≤ 32 weeks might be useful in first-trimester nullipara. Sixty (60) nulliparous women (40 with sPTB ≤ 32 weeks) who were free of comorbidities were randomly selected from the King’s College Fetal Medicine Research Institute biobank. Total PCF RNA was extracted and the expression of panel RNAs was quantitated by qRT-PCR. The analysis employed, primarily, multiple regression with the main outcome being the prediction of subsequent sPTB ≤ 32 weeks. The test performance was judged by the area under the curve (AUC) using a single threshold cut point with observed detection rates (DRs) at three fixed false positive rates (FPR). The mean gestation was 12.9 ± 0.5 weeks (range 12.0–14.1 weeks). Two RNAs were differentially expressed in women destined for sPTB ≤ 32 weeks: APOA1 (p < 0.001) and PSME2 (p = 0.05). APOA1 testing at 11–14 weeks predicted sPTB ≤ 32 weeks with fair to good accuracy. The best predictive model generated an AUC of 0.79 (95% CI 0.66–0.91) with observed DRs of 41%, 61%, and 79% for FPRs of 10%, 20%, and 30%, including crown–rump length, maternal weight, race, tobacco use, and age.
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Buscema, Paolo Massimo, Weldon A. Lodwick, Masoud Asadi-Zeydabadi, Francis Newman, Marco Breda, Riccardo Petritoli, Giulia Massini, David Buscema, Donatella Dominici, and Fabio Radicioni. "Twisting Theory: A New Artificial Adaptive System for Landslide Prediction." Geosciences 13, no. 4 (April 12, 2023): 115. http://dx.doi.org/10.3390/geosciences13040115.
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Landslides pose a significant risk to human life. The Twisting Theory (TWT) and Crown Clustering Algorithm (CCA) are innovative adaptive algorithms that can determine the shape of a landslide and predict its future evolution based on the movement of position sensors located in the affected area. In the first part of this study, the TWT and CCA will be thoroughly explained from a mathematical and theoretical perspective. In the second part, these algorithms will be applied to real-life cases, the Assisi landslide (1995–2008) and the Corvara landslide (2000–2008). A correlation of 0.9997 was attained between the model estimates and the expert’s posterior measurements at both examined sites. The results of these applications reveal that the TWT can accurately identify the overall shape of the landslides and predict their progression, while the CCA identifies complex cause-and-effect relationships among the sensors and represents them in a clear, weighted graph. To apply this model to a wider area and secure regions at risk of landslides, it is important to emphasize its operational feasibility as it only requires the installation of GNSS sensors in a predetermined grid in the target area.
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Nguyen, Dang-Trinh, and Jérôme Brossard. "ADAPTATION OF EXISTING BREAKWATERS TO SEA LEVEL RISE – OVERTOPPING EFFECT." Coastal Engineering Proceedings 1, no. 33 (October 9, 2012): 18. http://dx.doi.org/10.9753/icce.v33.structures.18.
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This paper describes the wave overtopping measurements of small scale maritime breakwater in sea level rise scenarios which are supposed in French program GICC (Gestion et Impacts du Changement Climatique - Management and Impacts of Climate Change). Many reinforced solutions have been carried out with the purpose to conserve the overtopping rate; among them, the influence of raising freeboard crest is analyzed. The test results are compared with results from literature and with the empirical models presented by Owen (1980), Van der Meer (1998) and Besley (1999). Since then, a guideline is proposed for a better prediction of wave overtopping with various types of high crown-wall.
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Ji, Kaiheng. "Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network." BioMed Research International 2022 (September 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/7728596.
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The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division’s economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management.
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Calef, Monika P., Jennifer I. Schmidt, Anna Varvak, and Robert Ziel. "Predicting the Unpredictable: Predicting Landcover in Boreal Alaska and the Yukon Including Succession and Wildfire Potential." Forests 14, no. 8 (August 2, 2023): 1577. http://dx.doi.org/10.3390/f14081577.
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The boreal forest of northwestern North America covers an extensive area, contains vast amounts of carbon in its vegetation and soil, and is characterized by extensive wildfires. Catastrophic crown fires in these forests are fueled predominantly by only two evergreen needle-leaf tree species, black spruce (Picea mariana (Mill.) B.S.P.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). Identifying where these flammable species grow through time in the landscape is critical for understanding wildfire risk, damages, and human exposure. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas.
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Kljakovic Gaspic, Toni, Mirela Pavicic Ivelja, Marko Kumric, Andrija Matetic, Nikola Delic, Ivana Vrkic, and Josko Bozic. "In-Hospital Mortality of COVID-19 Patients Treated with High-Flow Nasal Oxygen: Evaluation of Biomarkers and Development of the Novel Risk Score Model CROW-65." Life 11, no. 8 (July 23, 2021): 735. http://dx.doi.org/10.3390/life11080735.
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To replace mechanical ventilation (MV), which represents the cornerstone therapy in severe COVID-19 cases, high-flow nasal oxygen (HFNO) therapy has recently emerged as a less-invasive therapeutic possibility for those patients. Respecting the risk of MV delay as a result of HFNO use, we aimed to evaluate which parameters could determine the risk of in-hospital mortality in HFNO-treated COVID-19 patients. This single-center cohort study included 102 COVID-19-positive patients treated with HFNO. Standard therapeutic methods and up-to-date protocols were used. Patients who underwent a fatal event (41.2%) were significantly older, mostly male patients, and had higher comorbidity burdens measured by CCI. In a univariate analysis, older age, shorter HFNO duration, ventilator initiation, higher CCI and lower ROX index all emerged as significant predictors of adverse events (p < 0.05). Variables were dichotomized and included in the multivariate analysis to define their relative weights in the computed risk score model. Based on this, a risk score model for the prediction of in-hospital mortality in COVID-19 patients treated with HFNO consisting of four variables was defined: CCI > 4, ROX index ≤ 4.11, LDH-to-WBC ratio, age > 65 years (CROW-65). The main purpose of CROW-65 is to address whether HFNO should be initiated in the subgroup of patients with a high risk of in-hospital mortality.
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Bladon, Andrew J., Paul F. Donald, Nigel J. Collar, Jarso Denge, Galgalo Dadacha, Mengistu Wondafrash, and Rhys E. Green. "Climatic change and extinction risk of two globally threatened Ethiopian endemic bird species." PLOS ONE 16, no. 5 (May 19, 2021): e0249633. http://dx.doi.org/10.1371/journal.pone.0249633.
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Climate change is having profound effects on the distributions of species globally. Trait-based assessments predict that specialist and range-restricted species are among those most likely to be at risk of extinction from such changes. Understanding individual species’ responses to climate change is therefore critical for informing conservation planning. We use an established Species Distribution Modelling (SDM) protocol to describe the curious range-restriction of the globally threatened White-tailed Swallow (Hirundo megaensis) to a small area in southern Ethiopia. We find that, across a range of modelling approaches, the distribution of this species is well described by two climatic variables, maximum temperature and dry season precipitation. These same two variables have been previously found to limit the distribution of the unrelated but closely sympatric Ethiopian Bush-crow (Zavattariornis stresemanni). We project the future climatic suitability for both species under a range of climate scenarios and modelling approaches. Both species are at severe risk of extinction within the next half century, as the climate in 68–84% (for the swallow) and 90–100% (for the bush-crow) of their current ranges is predicted to become unsuitable. Intensive conservation measures, such as assisted migration and captive-breeding, may be the only options available to safeguard these two species. Their projected disappearance in the wild offers an opportunity to test the reliability of SDMs for predicting the fate of wild species. Monitoring future changes in the distribution and abundance of the bush-crow is particularly tractable because its nests are conspicuous and visible over large distances.
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Silvestro, F., S. Gabellani, F. Giannoni, A. Parodi, N. Rebora, R. Rudari, and F. Siccardi. "A hydrological analysis of the 4 November 2011 event in Genoa." Natural Hazards and Earth System Sciences 12, no. 9 (September 3, 2012): 2743–52. http://dx.doi.org/10.5194/nhess-12-2743-2012.
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Abstract. On the 4 November 2011 a flash flood event hit the area of Genoa with dramatic consequences. Such an event represents, from the meteorological and hydrological perspective, a paradigm of flash floods in the Mediterranean environment. The hydro-meteorological probabilistic forecasting system for small and medium size catchments in use at the Civil Protection Centre of Liguria region exhibited excellent performances for the event, by predicting, 24–48 h in advance, the potential level of risk associated with the forecast. It greatly helped the decision makers in issuing a timely and correct alert. In this work we present the operational outputs of the system provided during the Liguria events and the post event hydrological modelling analysis that has been carried out accounting also for the crowd sourcing information and data. We discuss the benefit of the implemented probabilistic systems for decision-making under uncertainty, highlighting how, in this case, the multi-catchment approach used for predicting floods in small basins has been crucial.
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Baranovskiy, Nikolay Viktorovich, and Viktoriya Andreevna Kirienko. "Mathematical Simulation of Forest Fuel Pyrolysis and Crown Forest Fire Impact for Forest Fire Danger and Risk Assessment." Processes 10, no. 3 (February 27, 2022): 483. http://dx.doi.org/10.3390/pr10030483.
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In order to predict and assess the danger from crown forest fires, it is necessary to study the thermal degradation of different forest fuels in a high-temperature environment. In this paper, the main characteristics of pyrolysis accompanied by moisture evaporation in a foliage sample of angiosperms (birch) were investigated within conditions typical for a crown forest fire. The heat and mass transfer in the forest fuel element is described by the system of non-stationary non-linear heat conduction equations with corresponding initial and boundary conditions. The considered problem is solved within the framework of the three-dimensional statement by the finite difference method. The locally one-dimensional method was used to solve three-dimensional equations for heat conduction. The simple iteration method was applied to solve nonlinear effects caused by the forest fuel pyrolysis and moisture evaporation. The fourth kind of boundary conditions are applicable at the interface between the sub-areas. Software implementation of the mathematical model is performed in the high-level programming language Delphi in the RAD Studio software. The characteristic changes in the sample temperature field and the phase composition under high-temperature exposure from a forest fire are presented. The induction period of the thermal decomposition of dry organic matter in the sample was determined. Recommendations are made about key features of accounting for the pyrolysis and evaporation processes when predicting forest fire danger. The research results can be used in the development and improvement of systems for predicting forest fire danger and environmental consequences of the forest fires.