Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jenvman.2023.118502 | DOI Listing |
J Diabetes Complications
December 2024
Sinai Health System, Division of General Internal Medicine, Toronto, Ontario, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario. Electronic address:
Aims: To identify factors associated with use of novel diabetes medications among patients hospitalized under general internal medicine.
Methods: We conducted a cohort study of patients with type 2 diabetes mellitus (T2DM) hospitalized in Ontario, Canada between 2015 and 2020. We evaluated the patient- and physician-level factors associated with sodium-glucose cotransporter 2 inhibitor (SGLT2) and glucagon-like peptide 1 receptor agonist (GLP1R) use using a multivariable logistic regression model.
Sci Rep
December 2024
Department of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Akşehir Kadir Yallagöz Health School, Selcuk University, Konya, Türkiye.
Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.
Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction.
Sci Rep
December 2024
Department of Radiology, Stanford University, Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA, 94304, USA.
The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients under 19 years old, collected between March and November 2021, were divided into test and exploring sets. A commercial adult-oriented AI software was utilized to detect lung lesions, including pneumothorax, consolidation, nodule, and pleural effusion, using a standard operating point of 15%.
View Article and Find Full Text PDFCurr Opin Insect Sci
December 2024
European Molecular Biology Laboratory, Heidelberg, Trust Genome Campus, Hinxton CB10 1SD, UK. Electronic address:
Insect populations are declining globally, with multiple potential drivers identified. However, experimental data are needed to understand their relative contributions. We highlight the sublethal effects of pesticides at field-relevant concentrations, often overlooked in standard environmental risk assessments (ERA), as significant contributors to these declines.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!