scholarly journals The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3348
Author(s):  
Yi-Min Huang ◽  
Shao-Wei Lu
Keyword(s):  
Random Forest ◽  
Evaluation Model ◽  
Critical Conditions ◽  
Short Term ◽  
Rainfall Events ◽  
Temporal Characteristics ◽  
The Past ◽  
Random Forest Method ◽  
Cumulative Change ◽  
Potential Evaluation

With the unique rainfall patterns of typhoons, plum rains, and short-term heavy rainfalls, the frequent landslide and debris flow disasters have caused severe loss to people in Taiwan. In the studies of landslide susceptibility, the information of factors used for analysis was usually annual-based content, and it was assumed that the same elements from different years were independent between each year. However, the occurrence of landslides was usually not simply due to the changes within a year. Instead, landslides were triggered because the factors that affected the potential of landslides reached critical conditions after a cumulative change with time. Therefore, this study had well evaluated the influence of temporal characteristics and the ratios of antecedent landslide areas in the past five years in the landslide potential evaluation model. The analysis was conducted through the random forest (RF) algorithm. Additional rainfall events of 2017 were used to test the proposed model’s performance to understand its practicality. The analysis results show that in the study area, the RF model had considerably acceptable performance. The results have also demonstrated that the antecedent landslide ratios in the past five years were essential to describe the significance of cumulative change with time when conducting potential landslide evaluation.

scholarly journals Preliminary Investigation of the Integrated Evaluation Model based on Random Forest Method

2021 ◽  
Vol 275 ◽  
pp. 03056
Author(s):  
Mei Yang ◽  
Jun Zhou
Keyword(s):  
Random Forest ◽  
Vocational Education ◽  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Preliminary Investigation ◽  
Development Trend ◽  
System Construction ◽  
Random Forest Method ◽  
Multiple Trees

With the deepening of reforms, as the closing year of the “Modern Vocational Education System Construction Plan (2014-2020)”, it has witnessed the development of vocational education. Its development trend is “only specialization” and “light basic courses”. Currently, the random forest method is very popular. This method forms a “forest” by building many decision trees, and votes on multiple trees to make decisions. This method can improve the classification accuracy of new samples effectively and it is more suitable for the research proposed. Random forest evaluation model has three functions: formative, diagnostic and summative. This paper establishes a comprehensive evaluation model for basic courses to make the evaluation more standardized, comprehensive and operability. And this evaluation system can supervise all stages of teaching.

Obesity and adiposity: the culprit of dietary protein efficacy

2020 ◽  
Vol 134 (4) ◽  
pp. 389-401
Author(s):  
Carla El-Mallah ◽  
Omar Obeid
Keyword(s):  
Food Intake ◽  
Behavioural Change ◽  
Physiological Mechanism ◽  
Short Term ◽  
The Past ◽  
Body Adiposity ◽  
Reduce Sugar ◽  
Eye Opening ◽  
Potential Strategy ◽  
Limitations And Exceptions

Abstract Obesity and increased body adiposity have been alarmingly increasing over the past decades and have been linked to a rise in food intake. Many dietary restrictive approaches aiming at reducing weight have resulted in contradictory results. Additionally, some policies to reduce sugar or fat intake were not able to decrease the surge of obesity. This suggests that food intake is controlled by a physiological mechanism and that any behavioural change only leads to a short-term success. Several hypotheses have been postulated, and many of them have been rejected due to some limitations and exceptions. The present review aims at presenting a new theory behind the regulation of energy intake, therefore providing an eye-opening field for energy balance and a potential strategy for obesity management.

The Random Forest Method in Research of Impact of Macroeconomic Indicators of Regional Development on Informal Employment Rate

2020 ◽  
Vol 27 (6) ◽  
pp. 37-55
Author(s):  
E. V. Zarova ◽  
E. I. Dubravskaya
Keyword(s):  
Random Forest ◽  
Regional Development ◽  
Russian Federation ◽  
Regional Level ◽  
Informal Employment ◽  
Macroeconomic Factors ◽  
Random Forest Method ◽  
Macroeconomic Indicators ◽  
The Russian Federation ◽  
The Impact

The topic of quantitative research on informal employment has a consistently high relevance both in the Russian Federation and in other countries due to its high dependence on cyclicality and crisis stages in economic dynamics of countries with any level of economic development. Developing effective government policy measures to overcome the negative impact of informal employment requires special attention in theoretical and applied research to assessing the factors and conditions of informal employment in the Russian Federation including at the regional level. Such effects of informal employment as a shortfall in taxes, potential losses in production efficiency, and negative social consequences are a concern for the authorities of the federal and regional levels. Development of quantitative indicators to determine the level of informal employment in the regions, taking into account their specifics in the general spatial and economic system of Russia are necessary to overcome these negative effects. The article proposes and tests methods for solving the problem of assessing the impact of hierarchical relationships on macroeconomic factors at the regional level of informal employment in constituent entities of the Russian Federation. Majority of the works on the study of informal employment are based on basic statistical methods of spatial-dynamic analysis, as well as on the now «traditional» methods of cluster and correlation-regression analysis. Without diminishing the merits of these methods, it should be noted that they are somewhat limited in identifying hidden structural connections and interdependencies in such a complex multidimensional phenomenon as informal employment. In order to substantiate the possibility of overcoming these limitations, the article proposes indicators of regional statistics that directly and indirectly characterize informal employment and also presents the possibilities of using the «random forest» method to identify groups of constituent entities of the Russian Federation that have similar macroeconomic factors of informal employment. The novelty of this method in terms of research objectives is that it allows one to assess the impact of macroeconomic indicators of regional development on the level of informal employment, taking into account the implicit, not predetermined by the initial hypotheses, hierarchical relationships of factor indicators. Based on the generalization of the studies presented in the literature, as well as the authors’ statistical calculations using Rosstat data, the authors came to the conclusion about the high importance of macroeconomic parameters of regional development and systemic relationships of macroeconomic indicators in substantiating the differentiation of the informal level across the constituent entities of the Russian Federation.

Impact of FII’s and FDI’s on Indian Stock Market

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Dr. Kamlesh Kumar Shukla
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Global Economy ◽  
Short Term ◽  
Earnings Growth ◽  
The Past ◽  
Bull Market ◽  
Indian Stock Market

FIIs are companies registered outside India. In the past four years there has been more than $41 trillion worth of FII funds invested in India. This has been one of the major reasons on the bull market witnessing unprecedented growth with the BSE Sensex rising 221% in absolute terms in this span. The present downfall of the market too is influenced as these FIIs are taking out some of their invested money. Though there is a lot of value in this market and fundamentally there is a lot of upside in it. For long-term value investors, there’s little because for worry but short term traders are adversely getting affected by the role of FIIs are playing at the present. Investors should not panic and should remain invested in sectors where underlying earnings growth has little to do with financial markets or global economy.

Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence

2020 ◽  
Vol 27 (3) ◽  
pp. 178-186 ◽  
Author(s):  
Ganesan Pugalenthi ◽  
Varadharaju Nithya ◽  
Kuo-Chen Chou ◽  
Govindaraju Archunan
Keyword(s):  
Random Forest ◽  
Protein Sequence ◽  
Glycosylation Site ◽  
Computational Method ◽  
The Other ◽  
Eukaryotic Protein ◽  
Random Forest Method ◽  
Glycosylation Sites ◽  
Human And Mouse ◽  
Better Than

Background: N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. Objective: In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. Methods: In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. Results: Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. Conclusion: Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.

scholarly journals What Has Clinical Research in Suicide Prevention Done for You Lately?

CNS Spectrums ◽  
2006 ◽  
Vol 11 (6) ◽  
pp. 440-441 ◽  
Author(s):  
Jan Fawcett
Keyword(s):  
Suicidal Ideation ◽  
Suicide Risk ◽  
Clinical Status ◽  
Panic Attacks ◽  
Current Evidence ◽  
Suicide Risk Assessment ◽  
Short Term ◽  
The Past ◽  
Standard Risk ◽  
Suicide Plan

What have you heard or read over the past 10 years that has improved you ability to assess and manage suicide risk in your patients?There has been a paucity of data. What little data there is reviewed in this month's articles.They highlight findings that you should know about. Clinicians seem to cling to the familiar, unless some intense marketing is done.For instance, are you aware that the current evidence shows that a denial of suicide thoughts, plans, or intent—even a contract for safety—means absolutely nothing in the absence of a full suicide risk assessment?Yet clinicians seem to rely on these ’reassurances“ from their patients and are shocked when the patient later commits suicide. Why should a patient who is deciding that life is too painful to live tell you the truth? Robert I. Simon, MD, and Daniel W. Shuman, JD, review these facts.Are you aware that severe psychic anxiety, panic attacks, agitation, and severe insomnia often precede suicide within hours, days, or weeks and can be rapidly modified with treatment?On the other hand, standard risk factors for suicide such as suicidal ideation, hopelessness, and past suicidal attempts are not good predictors of suicide in the short term. A suicide plan, recent high intent attempt, or refusal to contract for safety may well indicate immediate risk, but a denial of suicidal ideation or intent and a contract for no harm mean absolutely nothing without a full suicide assessment that takes current clinical status, past suicidal tendencies, social support, and willingness to accept help into account.

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