scholarly journals Predictive modelling of seabed habitats: case study of subtidal kelp forests on the coast of Brittany, France

2010 ◽  
Vol 157 (7) ◽  
pp. 1525-1541 ◽  
Author(s):  
Vona Méléder ◽  
Jacques Populus ◽  
Brigitte Guillaumont ◽  
Thierry Perrot ◽  
Pascal Mouquet
2020 ◽  
pp. 607-612
Author(s):  
Bernard Coûteaux

This paper elaborates on the key solutions offered by De Smet Engineers & Contractors (DSEC) to optimize the efficiency of cane sugar producing and processing facilities. In order to meet customer needs, DSEC offers proprietary predictive models built using the latest versions of specialized software. These models allow factory managers to envision the whole picture of increased operational and capital efficiency before it becomes reality. An integrated energy model and the CAPEX/OPEX evaluation method are discussed as ways to estimate and optimize costs, both for new greenfield projects and revamping of existing factories. The models demonstrate that factory capacities can be successfully increased using equipment that is already available. Special attention is paid to crystallization and centrifugation process simulations and the potential improvement of the global energy balance. One case study shows the transformation of a beet sugar factory into a refinery to process raw cane sugar after beet crop season and the second case shows the integration of a refinery into a cane sugar factory. The primary focus of the article is optimization of the technological process through predictive modelling. DSEC’s suggested solutions, which lead to great improvements in a plant’s efficiency and its ability to obtain very low energy consumption, are discussed.


2006 ◽  
Vol 13 (4) ◽  
pp. 241-250 ◽  
Author(s):  
Chris J. Carey ◽  
Tony G. Brown ◽  
Keith C. Challis ◽  
Andy J. Howard ◽  
Lynden Cooper

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Daniel Bayley ◽  
Paul Brickle ◽  
Paul Brewin ◽  
Neil Golding ◽  
Tara Pelembe

Kelp forests provide many important ecosystem services to people, including mitigating storm damage, cycling nutrients, and providing commercially-harvestable resources. However, kelp forests’ ability to sequester carbon dioxide, and therefore help regulate the climate, has until recently, been overlooked in assessments of the beneficial services they provide. In this study we incorporate updated knowledge on the potential of kelp to sequester ‘blue carbon’, and use the extensive kelp forests of the Falkland Islands as a case study to assess the value of kelp forest to society through multiple associated ecosystem services. Our analysis shows kelp forests provide a highly valuable range of direct and indirect services, which if managed correctly, will continue to benefit people, both now and in the future. The total estimated value of the Falkland Islands’ kelp system is currently equivalent to ~ £2.69 billion per year (or £3.24 million km-2 year-1). However, the true value of the kelp forest surrounding the Falkland Islands is likely to be higher still, given that our estimate does not account for elements such as associated scientific research, tourism, and cultural services, due to the necessary data currently being unavailable. Similarly, the full value of these highly biodiverse ecosystems in supplying habitat and food to a large range of associated species is crucial, yet extremely difficult to fully quantify. This study illustrates the importance of maintaining kelp ecosystems in a healthy state to ensure they continue to supply valuable ecological processes, functional roles, and ecosystem services, including their overlooked role as significant long-term carbon sinks.


2020 ◽  
Vol 28 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.


2021 ◽  
Author(s):  
Troy Smith

The study examines the applicability of Naïve Bayes in predictive classification modelling using a case study of cybercrime victimization data. The goal of which was a targeted presentation of the benefits of Bayesian analysis in crime research geared to policymakers. The method is assessed using a Model-Comparison Approach and model performance metrics. The study shows that Naïve Bayes can be useful in predictive classification where the target population is small or difficult to acquire such as offender profiling and analysis of high crime areas. This is important as it provides a plausible option to traditional Frequentist methods, that overcome statistical limitations and provides results in a form easily conveyable to policymakers. Further, the conditional probability data produced makes future prediction transparent and can foster confidence in predicted outcomes. In particular, Directed Acyclic Graph can be easily used to represent the Naïve Bayes output allowing visualization of the relationships between variables.


2017 ◽  
Vol 1 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ronlyn Duncan

Worldwide, the cumulative effects of diffuse pollution arising from a range of human activities are diminishing the quality and ecosystem capacity of lakes, rivers, estuaries, and oceans. Devising effective ways to regulate the causes and effects of diffuse pollution is a fraught legal, political, policy, and management challenge given the difficulties in identifying and measuring who is responsible for what, where, and when. In 2011, under its Resource Management Act, 1991, the South Pacific nation of New Zealand introduced national policy to arrest diffuse pollution with a requirement for local government to institute enforceable water quality and quantity limits on all freshwater bodies. The blueprint for these national freshwater policy reforms comes from its South Island region of Canterbury. Canterbury's regional council has adopted a catchment load approach whereby an overarching limit on nutrient losses from agricultural land is calculated and linked to land use rules to control property-scale agricultural activities. With a focus on the Canterbury region, this case study examines two approaches to establishing a catchment load for diffuse nutrient pollution to link to legal provisions in its regional plan. One is based on a river's nutrient concentrations and the other relies on predictive modelling. The case study opens important questions about measuring and regulating diffuse pollution and the difficulties faced by policy-makers and regulators in linking numbers to legally binding compliance and enforcement mechanisms, e.g. how to account for lag effects when establishing ‘in-stream’ limits and how to address changes in software when relying on ‘modelled’ limits?


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