To simplify the micro-level CO (carbon dioxide) emission calculation model, reduce the dataset quality requirement of the model, and cut down the volume of calculation, a meso-level voyage-based emission model (MeVEM) for inland ships is proposed with their navigation characteristics considered. The navigation characteristics and the main influencing factors of inland ship emissions are analyzed. The main engine power and average speed of the ships are selected as the model inputs. Accurate CO emissions are calculated by the use of the micro-level emission model. With that, first-order and second-order polynomial regression models are employed to establish the fitting formula to estimate the emissions per kilometer. To validate the proposed model, the Junshan segment in the middle reaches of the Yangtze River is selected as the study area, and the model parameters are determined to estimate the CO emissions. It is found that the model of emission per kilometer (e) established by second-order polynomial regression is more accurate. The results show that the percentage error in the total amount (PETA) of the results estimated by the four proposed models (CO emission estimation model for the upstream cargo ships, the downstream cargo ships, the upstream oil tankers, and the downstream oil tankers) are all within ±5%, which verifies the feasibility and applicability of the model. The proposed meso-level model allows us to use a smaller input dataset which is easier to obtain, and estimate CO emissions from ships simply and accurately.
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http://dx.doi.org/10.1016/j.scitotenv.2022.156271 | DOI Listing |
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Republic of Korea; Department of Pharmaceutical Medicine and Regulatory Science, Yonsei University, Incheon, Republic of Korea; Graduate Program of Industrial Pharmaceutical Science, Yonsei University, Incheon, Republic of Korea; Department of Integrative Biotechnology, Yonsei University, Incheon, Republic of Korea. Electronic address:
Background: Erlotinib is a potent first-generation epidermal growth factor receptor tyrosine kinase inhibitor. Due to its proximity to the upper limit of tolerability, dose adjustments are often necessary to manage potential adverse reactions resulting from its pharmacokinetic (PK) variability.
Methods: Population PK studies of erlotinib were identified using PubMed databases.
Comput Biol Med
January 2025
Neurological Sciences and Cerebrovascular Research Laboratory, Department of Neurology and Stroke Centre, Neurology and Cerebrovascular Disease Group, Neuroscience Area La Paz Institute for Health Research (idiPAZ), (La Paz University Hospital- Universidad Autónoma de Madrid), Spain. Electronic address:
The quantitative evaluation of motor function in experimental stroke models is essential for the preclinical assessment of new therapeutic strategies that can be transferred to clinical research; however, conventional assessment tests are hampered by the evaluator's subjectivity. We present an artificial intelligence-based system for the automatic, accurate, and objective analysis of target parameters evaluated by the ledged beam walking test, which offers higher sensitivity than the current methodology based on manual and visual counting. This system employs a residual deep network model, trained with DeepLabCut (DLC) to extract target paretic hindlimb coordinates, which are categorized to provide a ratio measurement of the animal's neurological deficit.
View Article and Find Full Text PDFJ Mol Graph Model
January 2025
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Gomtinagar Extension, Lucknow, 226028, India; Research Cell, Amity University Uttar Pradesh, Lucknow Campus, India. Electronic address:
The Acinetobacter baumannii is a member of the "ESKAPE" bacteria responsible for many serious multidrug-resistant (MDR) illnesses. This bacteria swiftly adapts to environmental cues leading to the emergence of multidrug-resistant variants, particularly in hospital/medical settings. In this work, we have demonstrated the outer membrane protein 33-36 (Omp33-36) porin as a potential therapeutic target in A.
View Article and Find Full Text PDFJ Mol Graph Model
January 2025
"VINČA" Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, 11001, Belgrade, Serbia.
Technetium-99m plays a pivotal role in nuclear medicine, offering unique IMAGING capabilities due to its favorable physical and chemical properties. This study investigates the redox behavior and electronic structures of three representative Tc(V) oxo complexes, [TcO(HMPAO)], [TcO(Bicisate)], and [TcO(DMSA)], using computational techniques. Employing relativistic density functional theory with the Zero-Order Regular Approximation (ZORA), we analyze singlet-triplet energy gaps, Gibbs free energy changes, and redox potentials in neutral and acidic environments.
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