scholarly journals Muslims in France: features of the integrated model

2012 ◽  
pp. 98-102
Author(s):  
A. V. Veretevskaya
Keyword(s):  
Integrated Model ◽  
Integration Model ◽  
Muslim Immigrants ◽  
The Future ◽  
Modern Status

The article deals with an acute problem of integration of Muslim immigrants and their descendants in France. The author follows the problem throughout its history and analyzes its modern status. The article provides thorough analysis of the French Integration Model. The author concludes with a prospect on its use in the future.

scholarly journals A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region

2021 ◽  
Vol 13 (20) ◽  
pp. 4090
Author(s):  
Amit Kumar Batar ◽  
Hideaki Shibata ◽  
Teiji Watanabe
Keyword(s):  
Forest Fragmentation ◽  
Integrated Model ◽  
Himalayan Region ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Forest Landscape ◽  
Indian Himalayan Region ◽  
Proposed Model ◽  
Causative Factors ◽  
The Future

An estimation of where forest fragmentation is likely to occur is critically important for improving the integrity of the forest landscape. We prepare a forest fragmentation susceptibility map for the first time by developing an integrated model and identify its causative factors in the forest landscape. Our proposed model is based upon the synergistic use of the earth observation data, forest fragmentation approach, patch forests, causative factors, and the weight-of-evidence (WOE) method in a Geographical Information System (GIS) platform. We evaluate the applicability of the proposed model in the Indian Himalayan region, a region of rich biodiversity and environmental significance in the Indian subcontinent. To obtain a forest fragmentation susceptibility map, we used patch forests as past evidence of completely degraded forests. Subsequently, we used these patch forests in the WOE method to assign the standardized weight value to each class of causative factors tested by the Variance Inflation Factor (VIF) method. Finally, we prepare a forest fragmentation susceptibility map and classify it into five levels: very low, low, medium, high, and very high and test its validity using 30% randomly selected patch forests. Our study reveals that around 40% of the study area is highly susceptible to forest fragmentation. This study identifies that forest fragmentation is more likely to occur if proximity to built-up areas, roads, agricultural lands, and streams is low, whereas it is less likely to occur in higher altitude zones (more than 2000 m a.s.l.). Additionally, forest fragmentation will likely occur in areas mainly facing south, east, southwest, and southeast directions and on very gentle and gentle slopes (less than 25 degrees). This study identifies Himalayan moist temperate and pine forests as being likely to be most affected by forest fragmentation in the future. The results suggest that the study area would experience more forest fragmentation in the future, meaning loss of forest landscape integrity and rich biodiversity in the Indian Himalayan region. Our integrated model achieved a prediction accuracy of 88.7%, indicating good accuracy of the model. This study will be helpful to minimize forest fragmentation and improve the integrity of the forest landscape by implementing forest restoration and reforestation schemes.

scholarly journals When the Going Gets Tough, How Do We Perceive the Future?

2021 ◽  
Author(s):  
Stephanie J. Tepper ◽  
Neil Anthony Lewis
Keyword(s):  
Time Perception ◽  
Integrated Model ◽  
Future Research ◽  
Social And Behavioral Sciences ◽  
Behavioral Sciences ◽  
Intervention Practice ◽  
The Social ◽  
The Future ◽  
Goal Directed Behavior ◽  
Experienced Difficulty

People struggle to stay motivated to work toward difficult goals. Sometimes the feeling of difficulty signals that the goal is important and worth pursuing; other times, it signals that the goal is impossible and should be abandoned. In this paper, we argue that how difficulty is experienced depends on how we perceive and experience the timing of difficult events. We synthesize research from across the social and behavioral sciences and propose a new integrated model to explain how components of time perception interact with interpretations of experienced difficulty to influence motivation and goal-directed behavior. Although these constructs have been studied separately in previous research, we suggest that these factors are inseparable and that an integrated model will help us to better understand motivation and predict behavior. We conclude with new empirical questions to guide future research and by discussing the implications of this research for both theory and intervention practice.

scholarly journals Trust in Virtual Teams: A Multidisciplinary Review and Integration

2019 ◽  
Vol 23 ◽  
Author(s):  
Janine Viol Hacker ◽  
Michael Johnson ◽  
Carol Saunders ◽  
Amanda L. Thayer
Keyword(s):  
Virtual Teams ◽  
Integrated Model ◽  
Complex Nature ◽  
Trust Development ◽  
Extant Literature ◽  
Organizational Issues ◽  
The Future ◽  
The Relationship ◽  
Team Trust ◽  
Insight Into

Organizations have increasingly turned to the use of virtual teams (VTs) to tackle the complex nature of today’s organizational issues. To address these practical needs, VTs researchers from different disciplines have begun to amass a large literature. However, the changing workplace that is becoming so reliant on VTs comes with its own set of management challenges, which are not sufficiently addressed by current research on VTs. Paradoxically, despite the challenges associated with technology in terms of its disruption to trust development in VTs, trust is one of the most promising solutions for overcoming myriad problems. Though the extant literature includes an abundance of studies on trust in VTs, a comprehensive multidisciplinary review and synthesis is lacking. Addressing this gap, we present a systematic theoretical review of 124 articles from the disparate, multidisciplinary literature on trust in VTs. We use the review to develop an integrated model of trust in VTs. Based on our review, we provide theoretical insights into the relationship between virtuality and team trust, and highlight several critical suggestions for moving this literature forward to meet the needs of workplaces of the future, namely: better insight into how trust evolves alongside the team’s evolution, clarity about how to adequately conceptualize and operationalize virtuality, and greater understanding about how trust might develop differently across diverse types of virtual contexts with various technology usages. We conclude with guidelines for managing VTs in the future workplace, which is increasingly driven and affected by changing technologies, and highlight important trends to consider.

Planning for the Future: Using an Integrated Model To Support Long-Term Strategies in Wastewater Collection and Treatment

2016 ◽  
Vol 2016 (13) ◽  
pp. 1924-1936
Author(s):  
Gill E.J. ◽  
Benedetti L ◽  
H�nonin J ◽  
A Brink-Kj�r ◽  
P.E Nielsen
Keyword(s):  
Integrated Model ◽  
The Future ◽  
Wastewater Collection

Laying the Foundations of a Digital Gas Field Development in a Greenfield Cluster Using Integrated Modelling: A Case Study

2021 ◽  
Author(s):  
Rena Alia Ramdzani ◽  
Oluwole A. Talabi ◽  
Adeline Siaw Hui Chua ◽  
Edwin Lawrence
Keyword(s):  
Steady State ◽  
Network Model ◽  
Hydrate Formation ◽  
Integrated Model ◽  
Integrated Modelling ◽  
Production Data ◽  
Base Case ◽  
Reservoir Model ◽  
The Future ◽  
Surface Network

Abstract Field X located in offshore South East Asia, is a deepwater, turbidite natural gas greenfield currently being developed using a subsea tieback production system. It is part of a group of fields anticipated to be developed together as a cluster. Due to the nature of this development, several key challenges were foreseen: i) subsurface uncertainty ii) production network impact on system deliverability and flow assurance iii) efficient use of high frequency data in managing production. The objective of this study was to demonstrate a flexible and robust methodology to address these challenges by integrating multiple realizations of the reservoir model with surface network models and showing how this could be link to "live" production data in the future. This paper describes the development and deployment of the solutions to overcome those challenges. Furthermore, the paper describes the results and key observations for further recommendation in moving forward to field digitalization. The process started with a quality check of the base case dynamic reservoir model to improve performance and enable multiple realization runs in a reasonable timeframe. This was followed by sensitivity and uncertainty analysis to obtain 10 realizations of the subsurface model which were integrated with the steady-state surface network model. Optimization under uncertainty was then performed on the integrated model to evaluate three illustrative development scenarios. To demonstrate extensibility, two additional candidate reservoirs for future development were also tied in to the system and modelled as a single integrated asset model to meet the anticipated gas delivery targets. Next, the subsurface model was integrated with a multiphase transient network model to show how it can be used to evaluate the risk of hydrate formation along the pipeline during planned production start-up. As a final step, in-built application programming interface (API) in the integration software was used to perform automation, enabling the integrated model to be activated and run automatically while being updated with sample "live" production data. At the conclusion of the study, the reservoir simulation performance was improved, reducing runtime by a factor of four without significant change in base case results. The results of the coupled reservoir to steady-state network simulation and optimization showed that the network could constrain reservoir deliverability by up to 4% in all realizations due to back pressure, and the most optimum development scenario was to delay first gas production and operate with shorter duration at high separator pressure. With the additional reservoirs in the integrated model, the production plateau could be extended up to 15 years beyond the base case without exceeding the specified water handling limit. For hydrates risk analysis, the differences between hydrate formation and fluid temperature indicated there was a potential risk of hydrate formation, which could be reduced by increasing inhibitor concentration. Finally, the automation process was successfully tested with sample data to generate updated production forecast profiles as the "new" production data was fed into the database, enabling immediate analysis. This study demonstrated an approach to improve forecasting and scenario evaluation by using multiple realizations of the reservoir model coupled to a surface network. The study also demonstrated that this integrated model can be carried forward to improve management of the field in the future when combined with "live" data and automation logic to create a foundation for a digital field deployment.

What Is the Future of the Wine-Grape Industry in Veneto? Evidence from a Micro-Macro Prediction Model

2018 ◽  
Vol 13 (4) ◽  
pp. 394-402
Author(s):  
Laura Onofri ◽  
Federica Bianchin ◽  
Vasco Boatto ◽  
Maikol Furlani ◽  
Francesco Pecci ◽  
...  
Keyword(s):  
Prediction Model ◽  
Quantitative Assessment ◽  
Integrated Model ◽  
Wine Grape ◽  
Model Framework ◽  
The Future ◽  
Jel Classifications

AbstractThis article presents a micro-macro integrated model/framework for the disaggregated quantitative assessment of the impacts of various shocks generated in five socio-economic and climate-driven simulations on the wine-grape sector in Veneto, Italy. (JEL Classifications: C01, C67, Q12, Q54)

scholarly journals Better Effectiveness of Multi-Integrated Neural Networks: Take Stock Big Data as an Example

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
HangLin Lu ◽  
XiuYun Peng
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
High Latitude ◽  
Integrated Model ◽  
Experimental Results ◽  
Single Model ◽  
Integration Model ◽  
Model Based ◽  
Better Than

With the development of big data, in the financial market, the stock price prediction has many research directions from the perspective of big data. The classical time series prediction model cannot adapt to the high-latitude information of stock data in the era of big data. The development of deep learning provides a new idea for high-latitude stock data prediction. Four neural network models and three integrated learning models form different strategy sets, and the opening price of the next timestamp is predicted by backtracking information over the past 15 days with the characteristics of 12 indexes of the stock. The experimental results show that the prediction effect of the integration model based on the average weight policy and stacking policy is better than that of the single neural network, and the integration model based on stacking policy is expected to have the highest prediction accuracy and the minimum expected error. The accuracy was 80.2%, and the mean square error was 0.024. Compared with the single model, the accuracy is increased by 2%~7%, and the error is reduced by 0.01~0.03. The innovation of this article lies in the traditional machine learning thinking is applied to deep learning, as an individual with a variety of neural network to study, through the integration of learning strategies, fusion for the integration model, the experimental results show that the effect of the integrated model is better than that of a single model, to improve the robustness and accuracy of the model; the performance of the integrated model is more stable. For the utilization of big data resources, the integrated model of neural network has better prediction effect.

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