scholarly journals Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

2012 ◽  
Vol 5 (3) ◽  
pp. 2289-2310
Author(s):  
J.-F. Exbrayat ◽  
N. R. Viney ◽  
H.-G. Frede ◽  
L. Breuer
Keyword(s):  
Structural Model ◽  
Model Averaging ◽  
Mitigation Measures ◽  
Ensemble Averaging ◽  
Catchment Scale ◽  
Biogeochemical Models ◽  
Model Predictions ◽  
Different Response ◽  
Management Changes ◽  
The Impact

Abstract. Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the Reliability Ensemble Averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the well-monitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilization scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members, which is always sensible.

scholarly journals Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

2013 ◽  
Vol 6 (1) ◽  
pp. 117-125 ◽  
Author(s):  
J.-F. Exbrayat ◽  
N. R. Viney ◽  
H.-G. Frede ◽  
L. Breuer
Keyword(s):  
Structural Model ◽  
Model Averaging ◽  
Mitigation Measures ◽  
Ensemble Averaging ◽  
Catchment Scale ◽  
Biogeochemical Models ◽  
Model Predictions ◽  
Different Response ◽  
Management Changes ◽  
The Impact

Abstract. Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the reliability ensemble averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the well-monitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilisation scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well-calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members.

Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy

2010 ◽  
Vol 139 (1) ◽  
pp. 68-79 ◽  
Author(s):  
M. AJELLI ◽  
S. MERLER ◽  
A. PUGLIESE ◽  
C. RIZZO
Keyword(s):  
Control Strategies ◽  
Influenza Pandemic ◽  
Mitigation Measures ◽  
Epidemiological Surveillance ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Policy Makers ◽  
Modelling Analysis ◽  
Model Predictions ◽  
The Impact

SUMMARYWe describe the real-time modelling analysis conducted in Italy during the early phases of the 2009 A/H1N1v influenza pandemic in order to estimate the impact of the pandemic and of the related mitigation measures implemented. Results are presented along with a comparison with epidemiological surveillance data which subsequently became available. Simulated epidemics were fitted to the estimated number of influenza-like syndromes collected within the Italian sentinel surveillance systems and showed good agreement with the timing of the observed epidemic. On the basis of the model predictions, we estimated the underreporting factor of the influenza surveillance system to be in the range 3·3–3·7 depending on the scenario considered. Model prediction suggested that the epidemic would peak in early November. These predictions have proved to be a valuable support for public health policy-makers in planning interventions for mitigating the spread of the pandemic.

Structural Modeling of Institute of Entrepreneurship in Russia

2019 ◽  
pp. 34-48
Author(s):  
Svetlana L. Sazanova
Keyword(s):  
Global Economy ◽  
Structural Model ◽  
Institutional Environment ◽  
Small And Medium Enterprises ◽  
Structural Modeling ◽  
Entrepreneurial Activity ◽  
Pattern Modeling ◽  
Medium Enterprises ◽  
The Impact ◽  
The Relationship

Entrepreneurship plays an important role in the modern global economy; the share of products of small and medium enterprises in the gross product and exports not only of the developed but also of developing countries is growing. Innovation processes cover all sectors of the economy, and more and more people are involved in entrepreneurial activity, which contributes to the penetration of entrepreneurial thinking and business values in all areas of the socioeconomic life of society. The Institute of Entrepreneurship plays an increasingly prominent role in the institutional environment of socio-economic systems. This actualizes the problem of studying the relationship of the institution of entrepreneurship with the institutions of law, culture, management. This requires a methodology that allows you to explore the impact on the institute of entrepreneurship not only economic, but also non-economic factors. The methodology of the “old” institutionalism possesses such a tool, it is structural modeling (pattern modeling), which allows to explore the diversity of interrelationships of the institution of entrepreneurship with other components of the institutional and economic environment. The article explored the features of the development of the institution of entrepreneurship in Russia, established the relationship between the institution of entrepreneurship, values, motives and incentives for entrepreneurial activity, built a structural model of the institution of entrepreneurship based on the methodology of the old institutionalism (pattern modeling). The structural model of the institution of entrepreneurship reveals the relationship between the institution of entrepreneurship, the values of entrepreneurial activity, its motives and incentives; as well as the relationship between the institution of entrepreneurship with the institutions of governance, cultural and religious institutions, legal institutions and society.

Structural Model of Service Quality Factors Affecting Loyalty Sustainability

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Eman Al-erqi ◽  
◽  
Mohd Lizam Mohd Diah ◽  
Najmaddin Abo Mosali ◽  
◽  
...  
Keyword(s):  
International Students ◽  
Service Quality ◽  
Goodness Of Fit ◽  
Structural Model ◽  
Quality Factors ◽  
Factors Affecting ◽  
Measurement Models ◽  
Confirmatory Factor ◽  
The University ◽  
The Impact

This study seeks to address the impact of service quality affecting international student's satisfaction towards loyalty tothe Universiti Tun Hussein Onn Malaysia(UTHM). The aim of thestudy is to develop relationship between service quality factor and loyalty to the university from the international students’ perspectives. The study adopted quantitative approach where data was collected through questionnaire survey and analysed statistically. A total of 246 responses were received and found to be valid. The model was developed and analysed using AMOS-SEM software. Confirmatory factor analysis (CFA) function of the software was to assessed the measurement models and found that all the models achieved goodness of fit. Then path analysis function was used to assessed structural model and found that service qualityfactors have a significant effect on the students’ satisfaction and thus affecting the loyaltyto the university. Hopefully the outcome form this study will benefit the university in providing services especially to the international students.

The Impact of Service Satisfaction, Relational Satisfaction and Commitment on Customer Loyalty in Logistics Outsourcing Relationship

2015 ◽  
Vol 4 (1and2) ◽  
Author(s):  
Sanjit Singh H.
Keyword(s):  
Customer Loyalty ◽  
Structural Equation ◽  
Structural Model ◽  
Reliability And Validity ◽  
Service Satisfaction ◽  
Equation Modeling ◽  
Relational Satisfaction ◽  
Logistics Outsourcing ◽  
Major Predictor ◽  
The Impact

This research explores the impact of service satisfaction, relational satisfaction, price satisfaction, and commitment on customer loyalty in logistics outsourcing relationships in Indian scenario. 254 users of logistics services from India were selected for investigating the potential linkages among the aforementioned satisfaction aspects and loyalty. Structural equation modeling (SEM) was employed to test the reliability and validity of the measurement and structural model developed to study the relationship among the linkages. Findings from the study supports that logistics service satisfaction, price satisfaction, relational satisfaction and commitment do influence loyalty positively. The analysis suggests that service satisfaction is the most important antecedent having primary influence in the formation of customer loyalty. Service satisfaction also has secondary influence on loyalty by acting as a strong driver in both relational satisfaction and commitment aspects of the service dimensions. Price satisfaction though positively been driven by service satisfaction, was found to have less significant effect as a predictor of loyalty in this context. The present study suggests that relational satisfaction is the second major predictor of loyalty which also drives commitment. This research is not an end-point but an attempt to establish the linkages and the effect among the antecedents driving the building and retention of good buyer-seller relationship in logistics outsourcing.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services

2020 ◽  
Vol 31 (3) ◽  
pp. 465-487 ◽  
Author(s):  
Carla Ruiz-Mafe ◽  
Enrique Bigné-Alcañiz ◽  
Rafael Currás-Pérez
Keyword(s):  
Structural Model ◽  
Online Reviews ◽  
Moderating Effect ◽  
Online Information ◽  
Bias Effect ◽  
Content Type ◽  
Digital Services ◽  
Information Cues ◽  
Cognitive Information ◽  
The Impact

PurposeThis paper analyses the interrelationships between emotions, the cognitive information cues of online reviews and intention to follow the advice obtained from digital platforms, paying special attention to the moderating effect of the sequencing of review valence.Design/methodology/approachThe data were collected from 830 Spanish Tripadvisor users. In a two-step approach, a measurement model was estimated and a structural model analysed to test the proposed hypotheses. SmartPLS 3.0 software was used. The moderating effect of sequencing of reviews is tested.FindingsThe data analysis showed a bias effect of review sequence on the impact of online information cues and emotions on intention to follow advice obtained from Tripadvisor. When the online reviews of a restaurant begin with positive commentaries, their perceived persuasiveness is a stronger driver of the pleasure and arousal elicited by online reviews than when they begin with negative reviews. On the other hand, the perceived helpfulness of online reviews only triggers arousal when the user reads negative, followed by positive, comments. The impact of pleasure on intention to follow the advice provided in an online travel community is higher with positive-negative than with negative-positive sequences.Originality/valueWhile researchers have demonstrated the benefits of customer reviews on company sales, a largely uninvestigated issue is the interplay between emotions and cognitive information cues in the processing of online reviews. This is one of the first studies to examine the moderating effect of conflicting reviews on the impact of emotions and cognitive information cues on consumer intention to follow the advice obtained from digital services.

Eating Behavior of Brazilian College Students: Influences of Lifestyle, Negative Affectivity, and Personal Characteristics

2020 ◽  
pp. 003151252098308
Author(s):  
Bianca G. Martins ◽  
Wanderson R. da Silva ◽  
João Marôco ◽  
Juliana A. D. B. Campos
Keyword(s):  
College Students ◽  
Eating Behavior ◽  
Emotional Eating ◽  
Eating Behaviors ◽  
Structural Model ◽  
Negative Affectivity ◽  
Personal Characteristics ◽  
Second Order ◽  
The Impact ◽  
The Relationship

In this study we proposed to estimate the impact of lifestyle, negative affectivity, and college students’ personal characteristics on eating behavior. We aimed to verify that negative affectivity moderates the relationship between lifestyle and eating behavior. We assessed eating behaviors of cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE)) with the Three-Factor Eating Questionnaire-18. We assessed lifestyle with the Individual Lifestyle Profile, and we assessed negative affectivity with the Depression, Anxiety and Stress Scale-21. We constructed and tested (at p < .05) a hypothetical causal structural model that considered global (second-order) and specific (first-order) lifestyle components, negative affectivity and sample characteristics for each eating behavior dimension. Participants were 1,109 college students ( M age = 20.9, SD = 2.7 years; 65.7% females). We found significant impacts of lifestyle second-order components on negative affectivity (β = −0.57–0.19; p < 0.001–0.01) in all models. Physical and psychological lifestyle components impacted directly only on CR (β=−0.32–0.81; p < 0.001). Negative affectivity impacted UE and EE (β = 0.23–0.30; p < 0.001). For global models, we found no mediation pathways between lifestyle and CR or UE. For specific models, negative affectivity was a mediator between stress management and UE (β=−0.07; p < 0.001). Negative affectivity also mediated the relationship between thoughts of dropping an undergraduate course and UE and EE (β = 0.06–0.08; p < 0.001). Participant sex and weight impacted all eating behavior dimensions (β = 0.08–0.34; p < 0.001–0.01). Age was significant for UE and EE (β=−0,14– −0.09; p < 0.001–0.01). Economic stratum influenced only CR (β = 0.08; p = 0.01). In sum, participants’ lifestyle, negative emotions and personal characteristics were all relevant for eating behavior assessment.

scholarly journals The Impact of Force Factors on the Benefits of Digital Transformation in Romania

2021 ◽  
Vol 11 (5) ◽  
pp. 2365
Author(s):  
Sorinel Căpușneanu ◽  
Dorel Mateș ◽  
Mirela Cătălina Tűrkeș ◽  
Cristian-Marian Barbu ◽  
Adela-Ioana Staraș ◽  
...  
Keyword(s):  
Perceived Risk ◽  
Industry 4.0 ◽  
Business Models ◽  
Structural Model ◽  
Digital Transformation ◽  
Sample Survey ◽  
Intention To Use ◽  
Online Questionnaire ◽  
Use Of Technology ◽  
The Impact

The digital transformation has produced changes in all existing areas of activity worldwide. There are many factors that can influence the intention to use Industry 4.0 processes and solutions and change the behavior of organizations and their business models. The aim of this study is to validate the econometric model on assessing the significant impact of distinct factors on the intention to use Industry 4.0 processes and solutions, the benefits of digital transformation perceived by organizational management and the differences between distinct groups analyzed. The research method used within the quantitative study was the sample survey, using the online questionnaire as a data collection tool. Three hundred forty-seven valid questionnaires were collected and the response rate of the respondents was 64.25%. A new structural model was generated based on the elements of the Unified Theory of Acceptance and Use of Technology (UTAUT). The results of the study indicated that Perceived competitiveness and Perceived risk have a significant impact on Intention to Use Industry 4.0 processes while Perceived vertical networking solutions and Perceived integrated engineering solutions have a significant influence on the Intention to Use Industry 4.0 solutions. In conclusion, there is a positive and significant association between Intention to Use Industry 4.0 solutions and Benefits of Digital Transformation.

scholarly journals Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5213
Author(s):  
Chin-Shiuh Shieh ◽  
Wan-Wei Lin ◽  
Thanh-Tuan Nguyen ◽  
Chi-Hong Chen ◽  
Mong-Fong Horng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Denial Of Service ◽  
Gaussian Mixture ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Ddos Attacks ◽  
Open Set ◽  
The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

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