Multiphysics coupling in exploitation and utilization of geo-energy: State-of-the-art and future perspectives

Natural gas hydrates and geothermal energy are potential sources of low-carbon geo-energy that are crucial in achieving a sustainable energy future for human society. The exploitation and utilization of these sources inherently involve thermal-hydraulic-mechanical-chemical coupling processes, and these complex coupling processes need to be numerically simulated for exploitation and utilization technology developments. This paper provides a brief overview of the current status and future challenges of numerical simulations for these coupling processes in the context of exploiting and utilizing natural gas hydrates, shallow and deep geothermal energy. It also presents perspectives on how to address these challenges, aiming to advance the development of numerical coupling technology within the geo-energy exploitation and utilization communities.Document Type: PerspectiveCited as: Wan, Y., Yuan, Y., Zhou, C., Liu, L. Multiphysics coupling in exploitation and utilization of geo-energy: State-of-the-art and future perspectives. Advances in Geo-Energy Research, 2023, 10(1): 7-13. https://doi.org/10.46690/ager.2023.10.0

Polarity-dependent electro-wetting or -dewetting on a conductive silicon substrate

We demonstrate droplet manipulation using electric signals to induce the liquid to wet or dewet on a hydrophilic conductive substrate in air without the needed for added layers. In this phenomenon, the contact angle changes more than 15{\deg} or -20{\deg} by using the ionic surfactant mediated droplets, with only 3 volts of the actuation voltage

Farming experience, personal characteristics, and entrepreneurial decisions of urban residents: Empirical evidence from China

Entrepreneurship is an important way to provide solutions for social employment problems. Using data from the 2016 China Labor Force Dynamic Survey (CLDS), we explore the influence of farming experience on urban residents’ entrepreneurial decisions at the theoretical and empirical levels. A Probit model with instrumental variables method was used to analyze the influence of farming experience on urban residents’ entrepreneurial decisions, while a mediating effect model was used to test its channels of action. The results show that: (1) farming experience can contribute to the entrepreneurial decision of urban residents relative to those without experience in farming. To overcome possible endogeneity issues, an Eprobit model based on the estimation of instrumental variables was used for testing. (2) Heterogeneity tests based on age, city type, and physical capital found that this effect was more significant in urban residents with non-capital cities, middle-aged groups, and high-material capital. (3) Farming experience indirectly drives entrepreneurial decisions through the mediating role of promoting positive personality traits, such as “optimism” and “mutual aid consciousness.” Therefore, the farming experience has a positive effect on urban residents’ entrepreneurial decisions and helps to understand the deeper influence of micro-individual characteristics on entrepreneurial decisions in the urbanization process

Styrene-ethylene-butadiene-styrene copolymer/carbon nanotubes composite fiber based strain sensor with wide sensing range and high linearity for human motion detection

Flexible strain sensors have attracted extensive attention due to their potential applications in wearable electronics and health monitoring. However, it is still a challenge to obtain flexible strain sensors with both high stretchability and wide linear strain sensing range. In this study, styrene-ethylene-butadiene-styrene copolymer/carbon nanotubes (SEBS/CNTs) composite fiber which showed both electrical conductivity and high stretchability was fabricated through a scalable wet spinning method. The effect of CNTs content on the strain sensing behavior of the SEBS/CNTs fiber based strain sensor was investigated. The results showed that when the CNTs content reached 7 wt%, the SEBS/CNTs composite fiber was capable of sensing strains as high as 500.20% and showed a wide linear strain sensing range of 0-500.2% with a gauge factor (GF) of 38.57. Combining high stretchability, high linearity and reliable stability, the SEBS/CNTs composite fiber based strain sensor had the ability to monitor the activities of different human body parts including hand, wrist, elbow, shoulder and knee

The fabrication and properties of magnetorheological elastomers employing bio-inspired dopamine modified carbonyl iron particles

To obtain magnetorheological elastomers (MREs) with improved mechanical properties and exhibiting an enhanced magnetorheological (MR) effect, bio-inspired dopamine modification has been used to improve the functionality at the surface of carbonyl iron (CI) particles. Various techniques including x-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to confirm that a polydopamine (PDA) layer of about 27.5 nm had been successfully deposited on the surface of the carbonyl iron particles prior to their inclusion in the MRE composites. The magnetic properties of PDA modified CI particles were shown to be almost the same as those for untreated CI particles. With the introduction of a PDA layer to the surfaces of the particles, both the tensile strength and the elongation at break of the MREs were improved. Furthermore, the MRE composites filled with PDA-coated CI particles exhibited lower zero-field storage moduli but higher magnetic field induced storage moduli when magnetization saturation was reached. The absolute and relative MR effect for the MREs reached 0.68 ± 0.002 MPa and 294% respectively, which were higher than those of MREs with pristine CI particles whose absolute and relative MR effect were 0.57 ± 0.02 MPa and 187% respectively. The findings of this work provide insights into enhanced fabrication of MREs with both improved mechanical properties and magneto-induced performance

Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio

Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes

Gadolinium‐Doped Iron Oxide Nanoprobe as Multifunctional Bioimaging Agent and Drug Delivery System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/1/adfm201502868.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/2/adfm201502868-sup-0001-S1.pd