Carbon Emission Prediction and Clean Industry Transformation Based on Machine Learning: A Case Study of Sichuan Province

This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Penalized regression models were then applied for their advantages in overfitting control, high-dimensional data processing, and feature selection - well-suited for the complex energy data. Results showed the second cluster around coal had highest emissions due to production needs. Emissions from gasoline-focused and coke-focused clusters were also significant. Based on this, emission reduction suggestions included clean coal technologies, transportation management, coal-electricity replacement in steel, and industry standardization. The research introduced unsupervised learning to objectively select factors and aimed to explore new emission reduction avenues. In summary, the study identified industry groupings, assessed emissions drivers, and proposed scientific reduction strategies to better inform decision-making using algorithms like DBSCAN and penalized regression models.Comment: 21 pages,19 figure

The effect of emotion regulation on happiness and resilience of university students: The chain mediating role of learning motivation and target positioning

ObjectiveTo investigate the effect andmechanism among emotion regulation, relationship,happiness, learning motivation, target positioning, and resilience of university students.MethodA total of 904 university students in China were included in this cross-sectional survey from April to May this year. The self-administered questionnaires, including the adapted Mental Health Scale with a Healthy Personality Orientation for College Students, were used to construct structural equations to test the chain mediating effects of learning motivation and target positioning based on a multi-stage whole group sample of university students.ResultEmotion regulation indirectly affected happiness through the mediating effect of interpersonal relationship (Med = −0.387, p = 0.001). Learning motivation and target positioning play the chain mediating role in the effect of emotion regulation on happiness (Med = −0.307, p = 0.001) and resilience (Med = −0.275, p = 0.001).ConclusionEmotion regulation indirectly affected happiness and resilience through the chain mediating effect of learning motivation and target positioning

Conformal single-layer encapsulation of PEDOT at low substrate temperature

In this work, we demonstrate a single-layer encapsulation method for poly(3,4-ethylenedioxythiophene) (PEDOT). This method is achieved by initiated chemical vapor deposition (iCVD) process, which is scalable and employs solvent-free and low-substrate temperature conditions. The encapsulant used, poly(divinylbenzene-co-maleic anhydride) (PDVB-MA), was first time synthesized via vapor phase process. This cross-linked iCVD polymer can be rapidly deposited (40 nm min−1) with uniform and conformal morphology. In the test of PEDOT degradation, the encapsulation extended the halflife of PEDOT to 900 h at 30 °C in air, which is more than 10 times of the counterpart without encapsulation.Eni S.p.A. (Firm) (Eni-MIT Solar Frontiers Alliance

On the Convergence of Newton-type Proximal Gradient Method for Multiobjective Optimization Problems

In a recent study, Ansary (Optim Methods Softw 38(3):570-590,2023) proposed a Newton-type proximal gradient method for nonlinear multiobjective optimization problems (NPGMO). However, the favorable convergence properties typically associated with Newton-type methods were not established for NPGMO in Ansary's work. In response to this gap, we develop a straightforward framework for analyzing the convergence behavior of the NPGMO. Specifically, under the assumption of strong convexity, we demonstrate that the NPGMO enjoys quadratic termination, superlinear convergence, and quadratic convergence for problems that are quadratic, twice continuously differentiable and twice Lipschitz continuously differentiable, respectively.Comment: arXiv admin note: text overlap with arXiv:2306.0979

A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction

Subsurface fluid flow, essential in various natural and engineered processes, is largely governed by a rock's permeability, which describes its ability to allow fluid passage. While convolutional neural networks (CNNs) have been employed to estimate permeability from high-resolution 3D rock images, our novel visualization technology reveals that they occasionally miss higher-level characteristics, such as nuanced connectivity and flow paths, within porous media. To address this, we propose a novel fusion model to integrate CNN with the graph neural network (GNN), which capitalizes on graph representations derived from pore network model to capture intricate relational data between pores. The permeability prediction accuracy of the fusion model is superior to the standalone CNN, whereas its total parameter number is nearly two orders of magnitude lower than the latter. This innovative approach not only heralds a new frontier in the research of digital rock property predictions, but also demonstrates remarkable improvements in prediction accuracy and efficiency, emphasizing the transformative potential of hybrid neural network architectures in subsurface fluid flow research