Ally S. Nyamawe

Machine Learning with Applications • 2022

Sean B. Holden

Foundations and Trends® in Machine Learning • 2020

The decision problem for Boolean satisfiability, generally referred to as SAT, is the archetypal NP-complete problem, and encodings of many problems of practical interest exist allowing them to be treated as SAT problems. Its generalization to quantified SAT (QSAT) is PSPACE-complete, and is useful for the same reason. Despite the computational complexity of SAT and QSAT, methods have been developed allowing large instances to be solved within reasonable resource constraints. These techniques have largely exploited algorithmic developments; however machine learning also exerts a significant influence in the development of state-of- the-art solvers. Here, the application of machine learning is delicate, as in many cases, even if a relevant learning problem can be solved, it may be that incorporating the result into a SAT or QSAT solver is counterproductive, because the run-time of such solvers can be sensitive to small implementation changes. The application of better machine learning methods in this area is thus an ongoing challenge, with characteristics unique to the field. This work provides a comprehensive review of the research to date on incorporating machine learning into SAT and QSAT solvers, as a resource for those interested in further advancing the field.

Ugochukwu Orji, Elochukwu Ukwandu

Machine Learning with Applications • 2024

Manal Binkhonain, Liping Zhao

Machine Learning with Applications • 2023

Siddharth Solaiyappan, Yuxin Wen

Machine Learning with Applications • 2022

Joydeep Ghosh

Machine Learning with Applications • 2024

Devaraj Vivek, Sabu Sunmitha, Elsayed Mohamed Elsayed

Mathematical Foundations of Computing • 2024

Indika Wickramasinghe

Machine Learning with Applications • 2022

Arpit Mallick, Subhra Dhara, Sushant Rath

Machine Learning with Applications • 2021

Miria Feng, Wenying Feng

Mathematical Foundations of Computing • 2020

Xia Liu

Mathematical Foundations of Computing • 2024

Neeta Rana, Hitesh Marwaha

Mathematical Foundations of Computing • 2023

Fanyu Kong, Cuixia Miao, Yujia Huo et al.

Mathematical Foundations of Computing • 2023

Yuejia Sun, Dao-Hong Xiang

Mathematical Foundations of Computing • 2024

Jiaxin Song, Cuixia Miao, Fanyu Kong

Mathematical Foundations of Computing • 2024

Jie Gao, Juan Zou, Xiaoxuan Cheng

Mathematical Foundations of Computing • 2022

Valère Hofstetter

Academia Letters • 2021

Ilkay Gumus, Mehmet Gülcan

Fuel • 2024

Lignesh Durai, Sushmee Badhulika

Fuel • 2023

ZhuChaozheng, Ma Yongzhi, Bu Qingkai et al.

International Journal of Electrochemical Science • 2018

Junsheng Zhu, Xu Zhang, Shuangquan Zhang et al.

International Journal of Electrochemical Science • 2017

Eric D. Ebel, Michael S. Williams

Microbial Risk Analysis • 2020

Ying Zheng, Xianfeng Zheng

International Journal of Electrochemical Science • 2020

Jie Yuan, Hui Zhao, Ruizhuo Ouyang et al.

International Journal of Electrochemical Science • 2020

Kylan S. Jin, Paul H. Fallgren, Nicholas A. Santiago et al.

Environmental Technology & Innovation • 2020

Zhengping Zhao, Sitao Shen, Yuting Li et al.

International Journal of Electrochemical Science • 2020

Hongyan Sun, Xin Kong, Baosen Wang et al.

International Journal of Electrochemical Science • 2017

Dongyun Zhang, Wenping Li, Nan Li et al.

International Journal of Electrochemical Science • 2018

Hongjian Lin, Weiwei Liu, Xin Zhang et al.

Biochemical Engineering Journal • 2017

Xiaojuan Ma, Ligang Gai, Yan Tian

International Journal of Electrochemical Science • 2018

Shuo Yang, Melanie Homberger, Michael Noyong et al.

International Journal of Electrochemical Science • 2016

Andrea Goglio, Stefania Marzorati, Laura Rago et al.

Bioresource Technology • 2019

Kathy Riggs Larsen

Materials Performance • 2017

Bangmin Gao, Yan Li, Yan Tian et al.

International Journal of Electrochemical Science • 2016

László Koók, Nándor Nemestóthy, Péter Bakonyi et al.

Chemosphere • 2017

In this work, the performance of dual-chamber microbial fuel cells (MFCs) constructed either with commonly used Nafion ® proton exchange membrane or supported ionic liquid membranes (SILMs) was assessed. The behavior of MFCs was followed and analyzed by taking the polarization curves and besides, their efficiency was characterized by measuring the electricity generation using various substrates such as acetate and glucose. By using the SILMs containing either [C 6 mim][PF 6 ] or [Bmim][NTf 2 ] ionic liquids, the energy production of these MFCs from glucose was comparable to that obtained with the MFC employing polymeric Nafion ® and the same substrate. Furthermore, the MFC operated with [Bmim][NTf 2 ]-based SILM demonstrated higher energy yield in case of low acetate loading (80.1 J g -1 COD in m -2  h -1 ) than the one with the polymeric Nafion ® N115 (59 J g -1 COD in m -2  h -1 ). Significant difference was observed between the two SILM-MFCs, however, the characteristics of the system was similar based on the cell polarization measurements. The results suggest that membrane-engineering applying ionic liquids can be an interesting subject field for bioelectrochemical system research.

Liquan Lu, Shengming Xu, Junwei An

International Journal of Electrochemical Science • 2016

Zan Luo, Yun Liu, Runliang Zhu et al.

International Journal of Electrochemical Science • 2016

Xiyang Yan, Yansu Wang, Zhiling Ma

International Journal of Electrochemical Science • 2017

Ilhame Bourais, Sara Maliki, Hasna Mohammadi et al.

Enzyme and Microbial Technology • 2016

Sunil A. Patil, Andrea Schievano, Carlo Santoro et al.

Bioresource Technology Reports • 2019