scholarly journals Modeling the Influence of Eucalypt Plantation on Wildfire Occurrence in the Brazilian Savanna Biome

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 844
Author(s):  
Luiz Felipe de Castro Galizia ◽  
Marcos Rodrigues
Keyword(s):  
Predictive Accuracy ◽  
Hot Spot ◽  
Characteristic Curve ◽  
Management Strategies ◽  
Brazilian Savanna ◽  
Ignition Probability ◽  
Wildfire Occurrence ◽  
Eucalypt Plantation ◽  
Eucalypt Plantations ◽  
And Cluster Analysis

In the last decades, eucalypt plantations are expanding across the Brazilian savanna, one of the most frequently burned ecosystems in the world. Wildfires are one of the main threats to forest plantations, causing economic and environmental loss. Modeling wildfire occurrence provides a better understanding of the processes that drive fire activity. Furthermore, the use of spatially explicit models may promote more effective management strategies and support fire prevention policies. In this work, we assessed wildfire occurrence combining Random Forest (RF) algorithms and cluster analysis to predict and detect changes in the spatial pattern of ignition probability over time. The model was trained using several explanatory drivers related to fire ignition: accessibility, proximity to agricultural lands or human activities, among others. Specifically, we introduced the progression of eucalypt plantations on a two-year basis to capture the influence of land cover changes over fire likelihood consistently. Fire occurrences in the period 2010–2016 were retrieved from the Brazilian Institute of Space Research (INPE) database. In terms of the AUC (area under the Receiver Operating Characteristic curve), the model denoted fairly good predictive accuracy (AUC ≈ 0.72). Results suggested that fire occurrence was mainly linked to proximity agricultural and to urban interfaces. Eucalypt plantation contributed to increased wildfire likelihood and denoted fairly high importance as an explanatory variable (17% increase of Mean Square Error [MSE]). Nevertheless, agriculture and urban interfaces proved to be the main drivers, contributing to decreasing the RF’s MSE in 42% and 38%, respectively. Furthermore, eucalypt plantations expansion is progressing over clusters of high wildfire likelihood, thus increasing the exposure to wildfire events for young eucalypt plantations and nearby areas. Protective measures should be focus on in the mapped Hot Spot zones in order to mitigate the exposure to fire events and to contribute for an efficient initial suppression rather than costly firefighting.

scholarly journals Prospective Validation of Pentraxin-3 as a Novel Serum Biomarker to Predict the Risk of Prostate Cancer in Patients Scheduled for Prostate Biopsy

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1611
Author(s):  
Ugo Giovanni Falagario ◽  
Gian Maria Busetto ◽  
Giuseppe Stefano Netti ◽  
Francesca Sanguedolce ◽  
Oscar Selvaggio ◽  
...  
Keyword(s):  
Prostate Biopsy ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Digital Rectal Examination ◽  
Multivariable Logistic Regression Analysis ◽  
Gleason Grade ◽  
Pentraxin 3 ◽  
Net Benefit ◽  
Multivariable Models

Purpose: To test and internally validate serum Pentraxin-3 (PTX3) levels as a potential PCa biomarker to predict prostate biopsy (PBx) results. Materials and Methods: Serum PSA and serum PTX3 were prospectively assessed in patients scheduled for PBx at our Institution due to increased serum PSA levels or abnormal digital rectal examination. Uni- and multivariable logistic regression analysis, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), were used to test the accuracy of serum PTX3 in predicting anyPCa and clinically significant PCa (csPCa) defined as Gleason Grade (GG) ≥ 2. Results: Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. During univariate analysis, PTX3 outperformed other variables in predicting both anyPCa and csPCa. The addition of PTX3 to multivariable models based on standard clinical variables, significantly increased each model’s predictive accuracy for anyPCa (AUC from 0.73 to 0.82; p < 0.001) and csPCa (AUC from 0.79 to 0.83; p < 0.001). At DCA, PTX3, and PTX3, density showed higher net benefit than PSA and PSA density and increased the net benefit of multivariable models in deciding when to perform PBx. Conclusions: Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results. Should our findings be confirmed, this novel reflex test could be used to reduce the number and burden of unnecessary prostate biopsies.

scholarly journals Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Bivard ◽  
Christopher Levi ◽  
Longting Lin ◽  
Xin Cheng ◽  
Richard Aviv ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Ct Angiography ◽  
Predictive Accuracy ◽  
Ct Perfusion ◽  
Imaging Modality ◽  
Characteristic Curve ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Stroke Patients

In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were included in this study. A model including clinical variables, alteplase treatment, and NCCT ASPECTS was weak (R2 0.067, P &lt; 0.001, AUC 0.605) at predicting 90 day mRS. A second model, including dynamic CTA variables (collateral grade, occlusion severity) showed better predictive accuracy for patient outcome (R2 0.381, P &lt; 0.001, AUC 0.781). A third model incorporating CTP variables showed very high predictive accuracy (R2 0.488, P &lt; 0.001, AUC 0.899). Combining all three imaging modalities variables also showed good predictive accuracy for outcome but did not improve on the CTP model (R2 0.439, P &lt; 0.001, AUC 0.825). CT perfusion predicts patient outcomes from alteplase therapy more accurately than models incorporating NCCT and/or CT angiography. This data has implications for artificial intelligence or machine learning models.

scholarly journals Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining

2021 ◽  
Vol 3 ◽  
Author(s):  
Oliver Haas ◽  
Luis Ignacio Lopera Gonzalez ◽  
Sonja Hofmann ◽  
Christoph Ostgathe ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Palliative Care ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Rule Mining ◽  
Data Set ◽  
Routinely Collected Data ◽  
Previous State ◽  
Insight Into

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

Assessing Dynamic Violence Risk in a High-Risk Treated Sample of Violent Offenders

Assessment ◽  
2018 ◽  
Vol 27 (8) ◽  
pp. 1886-1900 ◽  
Author(s):  
Richard B. A. Coupland ◽  
Mark E. Olver
Keyword(s):  
Risk Assessment ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Violent Offenders ◽  
Violence Risk ◽  
Violence Risk Assessment ◽  
Change Scores ◽  
Assessment And Treatment ◽  
General Community

The present study featured an investigation of the predictive properties of risk and change scores of two violence risk assessment and treatment planning tools—the Violence Risk Scale (VRS) and the Historical, Clinical, Risk–20, Version 2 (HCR-20)—in sample of 178 treated adult male violent offenders who attended a high-intensity violence reduction program. The cases were rated on the VRS and HCR-20 using archival information sources and followed up nearly 10 years postrelease. Associations of HCR-20 and VRS risk and change scores with postprogram institutional and community recidivism were examined. VRS and HCR-20 scores converged in conceptually meaningful ways, supporting the construct validity of the tools for violence risk. Receiver operating characteristic curve analyses demonstrated moderate- to high-predictive accuracy of VRS and HCR-20 scores for violent and general community recidivism, but weaker accuracy for postprogram institutional recidivism. Cox regression survival analyses demonstrated that positive pretreatment and posttreatment changes, as assessed via the HCR-20 and VRS, were each significantly associated with reductions in violent and general community recidivism, as well as serious institutional misconducts, after controlling for baseline pretreatment score. Implications for use of the HCR-20 and VRS for dynamic violence risk assessment and management are discussed.

scholarly journals Environmental Protection and Corporate Responsibility: The Perspectives of Senior Managers and CxOs in China

2019 ◽  
Vol 11 (13) ◽  
pp. 3610 ◽  
Author(s):  
Cubie L.L. Lau ◽  
Zinette Bergman ◽  
Manfred Max Bergman
Keyword(s):  
Environmental Management ◽  
Corporate Responsibility ◽  
Hot Spot ◽  
Management Strategies ◽  
Environmental Crisis ◽  
Business Strategies ◽  
Chinese Government ◽  
Senior Managers ◽  
Chinese Business ◽  
The Government

In the mid-2000s, China’s environmental crisis had become a major social and political ‘hot spot’. In the interest of civic conciliation, national stability, and performance legitimacy, the Chinese government responded by introducing the ‘Scientific Approach to Development’ as part of the 11th Five-Year Plan in 2005. It signaled a significant policy shift, in which the government reoriented China’s national goals away from ‘Growth First’ policies and toward a model of sustainable development. In this study, we explore how Chinese business leaders reacted to this significant policy change. Specifically, our aim is three-fold: (1) to identify how senior managers and CxOs (executives or owners of enterprises, including CEOs, CFOs, CSOs) of Chinese firms responded to the explicit and systemic introduction of environmental management in the 11th Five-Year Plan; (2) examine motivations and justifications associated with their responses; (3) and explore contexts in which different motivations connected to organizational change and its management. In our study, we examine the perspectives of 72 senior managers and CxOs in China. We find that the integration of environmental management and corporate responsibility policies was predominately driven by national, international, and market contexts, and motivated by instrumental, relational, and moral considerations. We identify complex strategies and implementation plans that transformed government directives into multiple and overlapping business strategies. The main contribution of our study is the identification of specific sets of strategies employed by firms to concurrently comply with government directives and seek profits. Broadly speaking, these environmental management strategies are divided into compliance, a pursuit of competitive advantage, and a structural integration of environmental management.

scholarly journals The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

2020 ◽  
pp. 009385482096975
Author(s):  
Mehdi Ghasemi ◽  
Daniel Anvari ◽  
Mahshid Atapour ◽  
J. Stephen wormith ◽  
Keira C. Stockdale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Assessment Instrument ◽  
Support Vector ◽  
Data Set ◽  
Applied Machine Learning ◽  
Vector Machines ◽  
Individual Scores

The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.

scholarly journals Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

2020 ◽  
Vol 13 (5) ◽  
pp. 92
Author(s):  
Katarina Valaskova ◽  
Pavol Durana ◽  
Peter Adamko ◽  
Jaroslav Jaros
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Early Warning Systems ◽  
Emerging Countries ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

scholarly journals Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Lei Mao ◽  
Xianghui Zhang ◽  
Yunhua Hu ◽  
Xinping Wang ◽  
Yanpeng Song ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Baseline Survey ◽  
Cvd Risk ◽  
Cytokine Levels ◽  
Study Population ◽  
The Town

Background. This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. Methods. The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n=1005) and validation cohorts (n=503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. Results. At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. Conclusions. The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment.

Lightning Fires in a Brazilian Savanna National Park: Rethinking Management Strategies

2000 ◽  
Vol 26 (6) ◽  
pp. 675-684 ◽  
Author(s):  
Mário Barroso Ramos-Neto ◽  
Vânia Regina Pivello
Keyword(s):  
National Park ◽  
Management Strategies ◽  
Brazilian Savanna
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