Modern computing: Vision and challenges

Telematics and Informatics Reports, Год журнала: 2024, Номер 13, С. 100116 - 100116

Опубликована: Янв. 8, 2024

Язык: Английский

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Green entrepreneurship and digitalization enabling the circular economy through sustainable waste management - An exploratory study of emerging economy

Journal of Cleaner Production, Год журнала: 2023, Номер 422, С. 138433 - 138433

Опубликована: Авг. 18, 2023

Язык: Английский

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The effects of digital technology application and supply chain management on corporate circular economy: A dynamic capability view

Journal of Environmental Management, Год журнала: 2023, Номер 341, С. 118082 - 118082

Опубликована: Май 2, 2023

Язык: Английский

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Environmental impacts of food waste management technologies: A critical review of life cycle assessment (LCA) studies

Trends in Food Science & Technology, Год журнала: 2023, Номер 143, С. 104287 - 104287

Опубликована: Дек. 6, 2023

Язык: Английский

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Removal of Cd(II) and Pb(II) from synthetic wastewater using Rosa damascena waste as a biosorbent: An insight into adsorption mechanisms, kinetics, and thermodynamic studies

Chemical Engineering Science, Год журнала: 2023, Номер 280, С. 119072 - 119072

Опубликована: Июль 11, 2023

Язык: Английский

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Digital empowerment and win-win co-operation for green and low-carbon industrial development: Analysis of regional differences based on GMM-ANN intelligence models

Journal of Cleaner Production, Год журнала: 2024, Номер 445, С. 141332 - 141332

Опубликована: Фев. 17, 2024

Язык: Английский

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Digitalization and innovation in green ports: A review of current issues, contributions and the way forward in promoting sustainable ports and maritime logistics

The Science of The Total Environment, Год журнала: 2023, Номер 912, С. 169075 - 169075

Опубликована: Дек. 4, 2023

Язык: Английский

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The hidden concept and the beauty of multiple “R” in the framework of waste strategies development reflecting to circular economy principles

The Science of The Total Environment, Год журнала: 2024, Номер 952, С. 175508 - 175508

Опубликована: Авг. 15, 2024

Язык: Английский

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Implications of climate change on water quality and sanitation in climate hotspot locations: A case study in Indonesia

Water Science & Technology Water Supply, Год журнала: 2024, Номер 24(2), С. 517 - 542

Опубликована: Янв. 24, 2024

Abstract Southeast Asia is vulnerable to climate change with over half of its population already being impacted by drought, flooding, and rise in sea levels recently. This work reviews the current water resource challenges Indonesia, prone rising impacts change. A baseline assessment Indonesia's drinking resources related original sources presented. In response a growing concern chronic that undermine supply nationwide, this study analyses safety supervision. To accomplish this, literature survey (100 studies published during 2000–2023 period) was performed identify regional groundwater sustainability security issues. Among main findings study, only 10% rainfall infiltrates groundwater, while 70% rivers are heavily polluted domestic waste. During period, availability decreased 1,200 m3/year 2020, 35% economically feasible for reuse. The deficit Indonesia estimated be 5.5 hm3/year roughly 67% population's demand satisfied 2021. Although might fulfilled private vendors, supply/demand forecasts 2030 suggest gap could not closed increasing supply.

Язык: Английский

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Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild

Journal of Environmental Management, Год журнала: 2024, Номер 351, С. 119908 - 119908

Опубликована: Янв. 1, 2024

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms waste. Deep learning (DL) models have made remarkable strides automating domestic recognition sorting. However, application DL recognize derived from construction, renovation, demolition (CRD) activities remains limited context-specific studies conducted previous research. This paper aims realistically capture complexity streams CRD context. study encompasses collecting annotating images real-world, uncontrolled environments. It then evaluates performance state-of-the-art automatically recognizing in-the-wild. Several pre-trained networks are utilized perform effectual feature extraction transfer during model training. results demonstrated that models, whether integrated with larger or lightweight backbone can composition in-the-wild which useful automated outcome emphasized applicability across various industrial domains, thereby contributing resource recovery encouraging management efforts.

Язык: Английский

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