Results 221 to 230 of about 36,988 (280)

Assessing Climate Change Disclosure and Its Governance Drivers: Insights From the European Utilities Sector

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study aims to enhance academic understanding of the factors influencing the disclosure practices of climate change among European utility companies, specifically in the context of their sustainability reporting. The primary objective is to explore, through a multi‐theoretical framework, the governance drivers that significantly affect the
Cristina Boţa‐Avram, Adriana Tiron Tudor, Liana Stanca   +2 more
wiley   +1 more source

Optimal Transport Theory to Extract Spiking Motifs

Grimaldi A, Sotomayor-Gómez B, Gilson M, Perrinet L, Alamia A, Vinck M.   +5 more
europepmc   +1 more source

Transmitter-assisted joint data-aided channel estimation and PAPR reduction scheme in wireless fading channels. [PDF]

Sci Rep
Khan I, Hasan MM, Cheffena M.
europepmc   +1 more source

Designing Governance for ESG: Incentive and Oversight Complementarities in Corporate Sustainability Performance

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study investigates how internal governance design supports credible ESG performance by distinguishing between Incentive and Oversight Architectures. Using 13,993 firm‐year observations of US nonfinancial firms from 2018 to 2024, we estimate fixed effects and two‐step system GMM models.
Beyza Gürel, Nehir Balci, Mustafa Reha Okur   +2 more
wiley   +1 more source

DG-LSTM-SA model: A deep gated LSTM network with self-attention mechanism for power generation and load forecasting. [PDF]

PLoS One
Sun G, Zhao Y, Li J, Cui J, Qi X.
europepmc   +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Effect of disc harrow on the soil disturbance area and its prediction and optimization using the artificial neural network (ANN) and response surface methodology (RSM). [PDF]

Sci Rep
Sheshpoli HS, Askari M, Kalantari D.
europepmc   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Information-Driven Rule Reduction in Belief Rule Bases for Complex System Modeling. [PDF]

Entropy (Basel)
Liu X, Huang H, Li Y, Xia Z, Zhao S.
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