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  • Authors: Kupferman, Orna;
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    Authors: Sharma, Kamal; Trivedi, Manoj Kumar;

    Many researchers have paid significant amount of attention for the development of time-cost, time-cost-quality and time–cost–safety trade-off optimization models. However, there is still a need to integrate the quality and safety together in time–cost trade-off optimization. To fill this gap, considering the multimode activities in project, this paper presents a multimode resource-constrained time–cost–quality–safety trade-off optimization model. Besides, this study considered the limited availability of resources for each execution mode of activities. A population-based meta-heuristics approach the nondominated sorting genetic algorithm III (NSGA III) is employed to develop the model. Also, Latin hypercube sampling for population initialization, analytical hierarchy process for quality determination and fuzzy logic for safety parameters determination are used. A case study of building construction project is used to demonstrate the applicability of proposed model. Whereas, the measure of various performance metrics and comparisons with existing trade-off optimization models demonstrate the effectiveness of proposed model in simultaneous optimization of four objectives. Moreover, a value path plot is prepared for the visualization of more than three objectives and a priori approach is presented to select one solution from obtained Pareto-optimal front.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Other ORP type . 2022
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      Other ORP type . 2022
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  • Authors: Musial, J.; Guzek, M.; Bouvry, P.; Blazewicz, J.;
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  • Authors: Hernik, Bartłomiej; Jagodzińska, Katarzyna; Matuszek, Dominik;
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    Authors: Singh, Deepak Kumar; Tirkey, Jeewan V;

    The COVID-19 Pandemic has a detrimental effect on the environment related to the exponential rise in medical waste (MW). Extraction of energy from the toxic MW with the latest gasification technology instead of conventional incineration is of utmost importance to promote sustainable development. This present study investigates the processing of MW for the generation of enriched hydrogen syngas using steam injected plasma gasifier. Modeling of Plasma gasifier was performed in Aspen Plus and Model validation was done with the experimental result and, a good agreement was attained. Sensitivity analysis was implemented on MW in which the influence of gasification temperature, equivalence ratio (ER), and Steam/Biomass (S/B) on the producer gas (PG) composition, gas yield, H2/CO ratio, cold gas efficiency (CGE), and the higher heating value (HHV) was calculated. Furthermore, Response surface methodology (RSM) has been incorporated for the multi-objective optimization of the variable gasification parameters. R2 values obtained from ANOVA for H2, CGE, and HHV are 98.62%, 99.10%, and 98.9% respectively. Using the response optimizer, the optimum values of H2, CGE, and HHV were found to be 0.43 (mole frac), 89.95%, and 7.49 MJ/Nm3 for temperature at 1560.600C, equivalence ratio 0.1, and S/B 0.99, respectively. The observed coefficient of desirability was about 0.97.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Other ORP type . 2021
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      Other ORP type . 2021
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  • Authors: Pupatwibul Pakawat; Banjar Ameen; Hossain Md. Imam; Braun, Robin; +1 Authors
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  • Authors: Mozyrska, D.; Ostalczyk, P.; Wyrwas, M.;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sungyop Kim; Fengpeng Sun; Irazábal, Clara;

    Climate scientists have developed high-resolution climate models to project local effects of climate change. However, such high-resolution climate information is not widely incorporated into climate change adaptation plans. In this study we analyze high-resolution (1.24 × 1.24-mile grid) temperature data generated by a climate model for Los Angeles County (CA). The data include projected surface air temperature and the number of extreme heat days (daily maximum temperature exceeding 95 °F) between a baseline period (1981–2000) and a mid-21st-century period (2041–2060). The data assume that human-induced greenhouse gas emissions will continuously increase at the current level and no change in existing built environments. We find significant geographic heterogeneity in terms of climate change effects in Los Angeles County. Also, given the assumptions, we expect inland suburban areas where urban expansion has been occurring to have more severe extreme heat effects than central and coastal areas by mid-century. Planners should engage with climate scientists to access and use high-resolution climate data. The use of high-resolution climate data needs to be mandated in plan-making in the era of climate change. Many regions may have different spatial patterns of extreme heat events and require tailored climate adaptation plans. In Los Angeles County, for example, continuing suburbanization in inland valley areas may need to be discouraged, whereas coastal areas with the least effects may consider denser, inclusive redevelopment strategies.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Other ORP type . 2020
    License: CC BY NC ND
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other ORP type . 2020
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      Other ORP type . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other ORP type . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Islas-Samperio, Jorge M.; Birlain-Escalante, Mariano O.; Genice K. Grande-Acosta;

    This article analyzes the possibility of substantially reducing greenhouse gases (GHG) in the Mexican industrial sector by applying a Low-carbon (LC) scenario, through a period of 25 years, composed by a set of mitigation options that involve the use of energy efficiency, materials recycling, cogeneration and renewable energy. Results reveal that a GHG peak of 226 million tons of equivalent CO2 could be reached by 2030 with a GHG reduction of 26.5%, compared to a Base scenario, demonstrating that the LC scenario could exceed Mexico’s Nationally Determined Contributions objective of reducing 19% of its industrial sector GHG by 2030. Additionally, GHG reduction of 47% could be achieved by 2035. Finally, the economic viability of the LC scenario was evaluated using a cost-benefit approach. As a result, economic benefits above $24,000 million dollars could be achieved due the energy savings generated are greater than costs to implement the mitigation options.

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    Other ORP type . 2020
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other ORP type . 2020
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      Other ORP type . 2020
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      Other ORP type . 2020
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  • Authors: IFIP Working Group 12.5, International Federation For Information Processing;
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  • Authors: Kupferman, Orna;
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    Authors: Sharma, Kamal; Trivedi, Manoj Kumar;

    Many researchers have paid significant amount of attention for the development of time-cost, time-cost-quality and time–cost–safety trade-off optimization models. However, there is still a need to integrate the quality and safety together in time–cost trade-off optimization. To fill this gap, considering the multimode activities in project, this paper presents a multimode resource-constrained time–cost–quality–safety trade-off optimization model. Besides, this study considered the limited availability of resources for each execution mode of activities. A population-based meta-heuristics approach the nondominated sorting genetic algorithm III (NSGA III) is employed to develop the model. Also, Latin hypercube sampling for population initialization, analytical hierarchy process for quality determination and fuzzy logic for safety parameters determination are used. A case study of building construction project is used to demonstrate the applicability of proposed model. Whereas, the measure of various performance metrics and comparisons with existing trade-off optimization models demonstrate the effectiveness of proposed model in simultaneous optimization of four objectives. Moreover, a value path plot is prepared for the visualization of more than three objectives and a priori approach is presented to select one solution from obtained Pareto-optimal front.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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  • Authors: Musial, J.; Guzek, M.; Bouvry, P.; Blazewicz, J.;
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  • Authors: Hernik, Bartłomiej; Jagodzińska, Katarzyna; Matuszek, Dominik;
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    Authors: Singh, Deepak Kumar; Tirkey, Jeewan V;

    The COVID-19 Pandemic has a detrimental effect on the environment related to the exponential rise in medical waste (MW). Extraction of energy from the toxic MW with the latest gasification technology instead of conventional incineration is of utmost importance to promote sustainable development. This present study investigates the processing of MW for the generation of enriched hydrogen syngas using steam injected plasma gasifier. Modeling of Plasma gasifier was performed in Aspen Plus and Model validation was done with the experimental result and, a good agreement was attained. Sensitivity analysis was implemented on MW in which the influence of gasification temperature, equivalence ratio (ER), and Steam/Biomass (S/B) on the producer gas (PG) composition, gas yield, H2/CO ratio, cold gas efficiency (CGE), and the higher heating value (HHV) was calculated. Furthermore, Response surface methodology (RSM) has been incorporated for the multi-objective optimization of the variable gasification parameters. R2 values obtained from ANOVA for H2, CGE, and HHV are 98.62%, 99.10%, and 98.9% respectively. Using the response optimizer, the optimum values of H2, CGE, and HHV were found to be 0.43 (mole frac), 89.95%, and 7.49 MJ/Nm3 for temperature at 1560.600C, equivalence ratio 0.1, and S/B 0.99, respectively. The observed coefficient of desirability was about 0.97.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Other ORP type . 2021
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      Other ORP type . 2021
      License: CC BY
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  • Authors: Pupatwibul Pakawat; Banjar Ameen; Hossain Md. Imam; Braun, Robin; +1 Authors
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  • Authors: Mozyrska, D.; Ostalczyk, P.; Wyrwas, M.;
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sungyop Kim; Fengpeng Sun; Irazábal, Clara;

    Climate scientists have developed high-resolution climate models to project local effects of climate change. However, such high-resolution climate information is not widely incorporated into climate change adaptation plans. In this study we analyze high-resolution (1.24 × 1.24-mile grid) temperature data generated by a climate model for Los Angeles County (CA). The data include projected surface air temperature and the number of extreme heat days (daily maximum temperature exceeding 95 °F) between a baseline period (1981–2000) and a mid-21st-century period (2041–2060). The data assume that human-induced greenhouse gas emissions will continuously increase at the current level and no change in existing built environments. We find significant geographic heterogeneity in terms of climate change effects in Los Angeles County. Also, given the assumptions, we expect inland suburban areas where urban expansion has been occurring to have more severe extreme heat effects than central and coastal areas by mid-century. Planners should engage with climate scientists to access and use high-resolution climate data. The use of high-resolution climate data needs to be mandated in plan-making in the era of climate change. Many regions may have different spatial patterns of extreme heat events and require tailored climate adaptation plans. In Los Angeles County, for example, continuing suburbanization in inland valley areas may need to be discouraged, whereas coastal areas with the least effects may consider denser, inclusive redevelopment strategies.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other ORP type . 2020
    License: CC BY NC ND
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other ORP type . 2020
    License: CC BY NC ND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other ORP type . 2020
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other ORP type . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Islas-Samperio, Jorge M.; Birlain-Escalante, Mariano O.; Genice K. Grande-Acosta;

    This article analyzes the possibility of substantially reducing greenhouse gases (GHG) in the Mexican industrial sector by applying a Low-carbon (LC) scenario, through a period of 25 years, composed by a set of mitigation options that involve the use of energy efficiency, materials recycling, cogeneration and renewable energy. Results reveal that a GHG peak of 226 million tons of equivalent CO2 could be reached by 2030 with a GHG reduction of 26.5%, compared to a Base scenario, demonstrating that the LC scenario could exceed Mexico’s Nationally Determined Contributions objective of reducing 19% of its industrial sector GHG by 2030. Additionally, GHG reduction of 47% could be achieved by 2035. Finally, the economic viability of the LC scenario was evaluated using a cost-benefit approach. As a result, economic benefits above $24,000 million dollars could be achieved due the energy savings generated are greater than costs to implement the mitigation options.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    Other ORP type . 2020
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other ORP type . 2020
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other ORP type . 2020
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      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Other ORP type . 2020
      License: CC BY
      Data sources: Datacite
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  • Authors: IFIP Working Group 12.5, International Federation For Information Processing;
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