Optimizing E-waste management: Deep learning classifiers for effective planning

Journal of Cleaner Production, Год журнала: 2024, Номер 443, С. 141021 - 141021

Опубликована: Янв. 29, 2024

Язык: Английский

---

Impact of artificial intelligence on aeronautics: An industry-wide review

Journal of Engineering and Technology Management, Год журнала: 2024, Номер 71, С. 101800 - 101800

Опубликована: Янв. 1, 2024

Curiously, there are few contributions in the scientific literature on subject of artificial intelligence (AI) and its impact aeronautics. However, many communications reports have been published by aeronautic companies about their applications AI technologies. This article makes an industry-wide review aeronautics using a three-step sequential approach: (i) concepts to define develop conceptual map; (ii) selection 100 use cases from that technologies (e.g., Airbus, Boeing, Air France, Safran, EasyJet, Dassault Aviation, Altair); (iii) analysis topics defined map. The main results describe rising interest integration entities sector. Moreover, show most recurrent big data analytics, autonomous intelligent systems, predictive machine learning, robotics. Another finding is related several benefits motivate integrate into industrial operational processes. frequent include customer satisfaction, saving time, safety security, cost reduction, better decision making, solving complex problems, ensuring optimisation efficiency. It also appears performance positively impacted these Such impacts span all departments including marketing, where help satisfy needs; area, which provided with quality products; productivity economic optimised for more

Язык: Английский

---

ESG performance and technological change: Current state-of-the-art, development and future directions

Journal of Cleaner Production, Год журнала: 2023, Номер 429, С. 139493 - 139493

Опубликована: Ноя. 1, 2023

Язык: Английский

---

Policy pathways to sustainable E-waste management: A global review

Journal of Hazardous Materials Advances, Год журнала: 2024, Номер 16, С. 100473 - 100473

Опубликована: Сен. 16, 2024

Язык: Английский

---

Financial Support in E-waste Management within a Circular Economy with Structural Equation Modeling Analysis from Taiwan

Circular Economy and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Язык: Английский

---

Application of Geographic Information Systems (GIS) and Remote Sensing (RS) in solid waste management in Southern Africa: a review

SN Social Sciences, Год журнала: 2024, Номер 4(2)

Опубликована: Фев. 5, 2024

Язык: Английский

---

A computer vision-based system for real-time component identification from waste printed circuit boards

Journal of Environmental Management, Год журнала: 2023, Номер 351, С. 119779 - 119779

Опубликована: Дек. 12, 2023

Язык: Английский

---

Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network

Marine Pollution Bulletin, Год журнала: 2024, Номер 209, С. 117030 - 117030

Опубликована: Окт. 10, 2024

Язык: Английский

---

Recent Advancements in Artificial Intelligence in Battery Recycling

Batteries, Год журнала: 2024, Номер 10(12), С. 440 - 440

Опубликована: Дек. 11, 2024

Battery recycling has become increasingly crucial in mitigating environmental pollution and conserving valuable resources. As demand for battery-powered devices rises across industries like automotive, electronics, renewable energy, efficient is essential. Traditional methods, often reliant on manual labor, suffer from inefficiencies harm. However, recent artificial intelligence (AI) advancements offer promising solutions to these challenges. This paper reviews the latest developments AI applications battery recycling, focusing methodologies, challenges, future directions. technologies, particularly machine learning deep models, are revolutionizing sorting, classification, disassembly processes. AI-powered systems enhance efficiency by automating tasks such as identification, material characterization, robotic disassembly, reducing human error occupational hazards. Additionally, integrating with advanced sensing technologies computer vision, spectroscopy, X-ray imaging allows precise characterization real-time monitoring, optimizing strategies recovery rates. Despite advancements, data quality, scalability, regulatory compliance must be addressed realize AI’s full potential recycling. Collaborative efforts interdisciplinary domains essential develop robust, scalable AI-driven solutions, paving way a sustainable, circular economy materials.

Язык: Английский

---

Assessment of risk propagation in an e-waste collection system using Bayesian networks

Journal of Material Cycles and Waste Management, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Язык: Английский

---