The Experts below are selected from a list of 1483950 Experts worldwide ranked by ideXlab platform

Alexandre Gaudeul - One of the best experts on this subject based on the ideXlab platform.

  • competition between open source and proprietary software the la tex case study
    Industrial Organization, 2004
    Co-Authors: Alexandre Gaudeul
    Abstract:

    The paper examines competition between two Development Models, proprietary and open-source (``OS''). It first defines and compares those two Models and then analyzes the influence the Development of one type of software has on the Development of the other. The paper is based on the (La)TeX case study. In that case study, the features, users, and patterns in the Development of the (La)TeX software were compared to its proprietary equivalents. The Models that are presented in this paper describe some aspects of the strategic interactions between proprietary and open-source software. The paper shows that they cannot be analyzed independently; the decisions of one class of agents (OSS developers) are affected by those of the other class of agents (private entrepreneurs).

Madhabananda Das - One of the best experts on this subject based on the ideXlab platform.

  • a review of software cost estimation in agile software Development using soft computing techniques
    Computational Intelligence, 2016
    Co-Authors: Saurabh Bilgaiyan, Samaresh Mishra, Madhabananda Das
    Abstract:

    For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of Development Models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different Development Models having new and innovative phases of software Development, is a crucial task to be done. An accurate prediction always leads to a successful software project within the budget with no delay, but any percentage of misconduct in the overall effort and cost estimate may lead to a project failure in terms of delivery time, budget or features. Software industries have adopted various Development Models based on the project requirements and organization's capabilities. Due to adaptability to changes in a software project, agile software Development model has become a much successful and popular framework for Development over the last decade. The customer is involved as an active participant in the Development using an agile framework. Hence, changes can occur at any phase of Development and they can be dynamic in nature. That is why an accurate prediction of effort and cost of such projects is a crucial task to be done as the complexity of overall Development structure is increased with the time. Soft computing techniques have proven that they are one of the best problem solving techniques in such scenarios. Such techniques are more flexible and presence of bio-intelligence increases their accuracy. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Inference Systems (FIS), etc. are applied successfully for estimation of cost and effort of agile based software projects. This paper deals with such soft computing techniques and provides a detailed and analytical overview of such methods. It also provides the future scope and possibilities to explore such techniques on the basis of survey provided by this paper.

Abdelmutalab G A Azrag - One of the best experts on this subject based on the ideXlab platform.

  • prediction of insect pest distribution as influenced by elevation combining field observations and temperature dependent Development Models for the coffee stink bug antestiopsis thunbergii gmelin
    PLOS ONE, 2018
    Co-Authors: Abdelmutalab G A Azrag, Christian Walter Werner Pirk, Abdullahi Ahmed Yusuf, Fabrice Pinard, Saliou Niassy, Gladys Mosomtai, Regis Babin
    Abstract:

    The antestia bug, Antestiopsis thunbergii (Gmelin 1790) is a major pest of Arabica coffee in Africa. The bug prefers coffee at the highest elevations, contrary to other major pests. The objectives of this study were to describe the relationship between A. thunbergii populations and elevation, to elucidate this relationship using our knowledge of the pest thermal biology and to predict the pest distribution under climate warming. Antestiopsis thunbergii population density was assessed in 24 coffee farms located along a transect delimited across an elevation gradient in the range 1000-1700 m asl, on Mt. Kilimanjaro, Tanzania. Density was assessed for three different climatic seasons, the cool dry season in June 2014 and 2015, the short rainy season in October 2014 and the warm dry season in January 2015. The pest distribution was predicted over the same transect using three risk indices: the establishment risk index (ERI), the generation index (GI) and the activity index (AI). These indices were computed using simulated life table parameters obtained from temperature-dependent Development Models and temperature data from 1) field records using data loggers deployed over the transect and 2) predictions for year 2055 extracted from AFRICLIM database. The observed population density was the highest during the cool dry season and increased significantly with increasing elevation. For current temperature, the ERI increased with an increase in elevation and was therefore distributed similarly to observed populations, contrary to the other indices. This result suggests that immature stage susceptibility to extreme temperatures was a key factor of population distribution as impacted by elevation. In the future, distribution of the risk indices globally indicated a decrease of the risk at low elevation and an increase of the risk at the highest elevations. Based on these results, we concluded with recommendations to mitigate the risk of A. thunbergii infestation.

Regis Babin - One of the best experts on this subject based on the ideXlab platform.

  • prediction of insect pest distribution as influenced by elevation combining field observations and temperature dependent Development Models for the coffee stink bug antestiopsis thunbergii gmelin
    PLOS ONE, 2018
    Co-Authors: Abdelmutalab G A Azrag, Christian Walter Werner Pirk, Abdullahi Ahmed Yusuf, Fabrice Pinard, Saliou Niassy, Gladys Mosomtai, Regis Babin
    Abstract:

    The antestia bug, Antestiopsis thunbergii (Gmelin 1790) is a major pest of Arabica coffee in Africa. The bug prefers coffee at the highest elevations, contrary to other major pests. The objectives of this study were to describe the relationship between A. thunbergii populations and elevation, to elucidate this relationship using our knowledge of the pest thermal biology and to predict the pest distribution under climate warming. Antestiopsis thunbergii population density was assessed in 24 coffee farms located along a transect delimited across an elevation gradient in the range 1000-1700 m asl, on Mt. Kilimanjaro, Tanzania. Density was assessed for three different climatic seasons, the cool dry season in June 2014 and 2015, the short rainy season in October 2014 and the warm dry season in January 2015. The pest distribution was predicted over the same transect using three risk indices: the establishment risk index (ERI), the generation index (GI) and the activity index (AI). These indices were computed using simulated life table parameters obtained from temperature-dependent Development Models and temperature data from 1) field records using data loggers deployed over the transect and 2) predictions for year 2055 extracted from AFRICLIM database. The observed population density was the highest during the cool dry season and increased significantly with increasing elevation. For current temperature, the ERI increased with an increase in elevation and was therefore distributed similarly to observed populations, contrary to the other indices. This result suggests that immature stage susceptibility to extreme temperatures was a key factor of population distribution as impacted by elevation. In the future, distribution of the risk indices globally indicated a decrease of the risk at low elevation and an increase of the risk at the highest elevations. Based on these results, we concluded with recommendations to mitigate the risk of A. thunbergii infestation.

Kevin J Smith - One of the best experts on this subject based on the ideXlab platform.

  • esterification over acid treated mesoporous carbon derived from petroleum coke
    ACS omega, 2019
    Co-Authors: Shida Liu, Haiyan Wang, Patrick Neumann, Changsoo Kim, Kevin J Smith
    Abstract:

    Multistep activation of a Canadian oilsands petroleum coke that yields an acidified mesoporous carbon catalyst is reported. Microporous-activated carbon (APC; ∼2000 m2/g), obtained by thermochemical activation of petroleum coke using KOH, was impregnated with ammonium heptamolybdate and activated by carbothermal hydrogen reduction (CHR). The resulting Mo2C, supported on high-mesopore volume (Vmeso ∼0.4 cm3/g) carbon, yields the desired mesoporous carbon catalyst (Vmeso ∼0.7 cm3/g) following acid washing. The effect of CHR temperature and the benefit of Mo2C loading on mesopore Development is reported, and pore Development Models are discussed. The mesoporous carbons are active for the esterification of acetic acid and 1-butanol at 77 °C, and the butanol conversion correlates with the catalyst acidity, as measured by NH3-TPD.

  • Esterification over Acid-Treated Mesoporous Carbon Derived from Petroleum Coke
    2019
    Co-Authors: Shida Liu, Haiyan Wang, Patrick Neumann, Changsoo Kim, Kevin J Smith
    Abstract:

    Multistep activation of a Canadian oilsands petroleum coke that yields an acidified mesoporous carbon catalyst is reported. Microporous-activated carbon (APC; ∼2000 m2/g), obtained by thermochemical activation of petroleum coke using KOH, was impregnated with ammonium heptamolybdate and activated by carbothermal hydrogen reduction (CHR). The resulting Mo2C, supported on high-mesopore volume (Vmeso ∼0.4 cm3/g) carbon, yields the desired mesoporous carbon catalyst (Vmeso ∼0.7 cm3/g) following acid washing. The effect of CHR temperature and the benefit of Mo2C loading on mesopore Development is reported, and pore Development Models are discussed. The mesoporous carbons are active for the esterification of acetic acid and 1-butanol at 77 °C, and the butanol conversion correlates with the catalyst acidity, as measured by NH3-TPD