Results 211 to 220 of about 9,169,616 (322)

Performance change detection and recovery in inferential control loops

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract In most industrial environments, soft sensors are crucial for estimating variables that are hard to measure. It is critical that these variables be estimated quickly and accurately because they are closely linked to plant safety and profit. Therefore, performance drift in the soft sensors cannot be ignored.
Xuanhui Zhai, Yuri A. W. Shardt
wiley   +1 more source

A protection strategy for distribution networks using IoT technology and support vector machine. [PDF]

Sci Rep
Shalby EM, Abdelaziz AY, Ahmed ES, Rashad BA.   +3 more
europepmc   +1 more source

Combating ESG Greenwashing Through AI Models: Evidence From Disaggregated AI Technologies, Mechanisms, and Thresholds

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study examines how artificial intelligence language models influence corporate environmental, social, and governance greenwashing (GWESG$$ {\mathrm{GW}}_{\mathrm{ESG}} $$) behavior, utilizing panel data from Chinese listed firms spanning 2012–2022.
Brahim Bergougui, Hamid Ghazi H. Sulimany, Abdulrahman Atllah Alharbi   +2 more
wiley   +1 more source

Development and validation of an ultrasound-based deep learning radiomics nomogram for risk assessment of lymph node metastasis in papillary thyroid microcarcinoma. [PDF]

Quant Imaging Med Surg
Liu Z, Lu W, Ge M, Zhou J, Jiang H, Wu X, Zhang C.   +6 more
europepmc   +1 more source

Orchestrating Green Transformation: How AI Adoption Enables Corporate Carbon Neutrality

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐
Xiaonan Dong, Sungjin Son
wiley   +1 more source

From Dictionaries to Deep Learning: A Systematic Mapping Review of the Natural Language Processing Tasks Used to Analyze Sustainability Reports

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study aims at shedding light on the vast landscape of natural language processing (NLP) tasks used when analyzing sustainability reports or sustainability within integrated annual reports. A systematic literature review is carried out, identifying 160 studies of relevance.
Hannes Cordes
wiley   +1 more source

Machine learning-based solar radiation prediction across heterogeneous climatic zones using NASA POWER data: a case study in Nigeria. [PDF]

Front Artif Intell
Faloye OT, Awotoye GT, Akinremi J, Olaleye OS, Oluwadare AO, Adeyeri OE, Laokhongthavorn L, Kamchoom V.   +7 more
europepmc   +1 more source

Uniaxial compressive strength prediction and ratio parameter optimization of titanium tailings composite backfill materials using intelligent hybrid models

Deep Underground Science and Engineering, EarlyView.
Based on the 90 datasets, ERT and four optimization algorithms were used to build four hybrid models to predict the UCS of the backfill body. The SMA‐ERT model was the most effective model, and it can reliably guide the design of the backfill ratio parameters. Abstract This study analyzed the feasibility of using titanium (Ti) tailings as a backfilling
Weijun Liu, Zida Liu, Zhixiang Liu
wiley   +1 more source

Study on ice phase change prediction model for transmission lines based on multiphysics coupling. [PDF]

Sci Rep
Pei Y, Wang Q, Wang L, Zuo S, Zhang H, Jing L.   +5 more
europepmc   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source