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Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?
J Chem Inf Model
January 2025
Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a single deep learning architecture type to assess whether a focused approach could enhance performance in fault diagnosis.
View Article and Find Full Text PDFPurpose: Primary anterior cruciate ligament (ACL) reconstruction graft failure remains a significant health concern in young patients. Despite the high incidence of poor graft integration in these patients and the resulting high failure rate, little consideration has been given to the quality of the bone into which the graft is anchored at reconstruction. Therefore, we investigated post ACL injury mineralized tissue changes in the ACL femoral entheses of young males and compared them to changes previously reported for young females.
View Article and Find Full Text PDFA hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention.
Sci Rep
January 2025
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, 100055, China.
Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations.
View Article and Find Full Text PDFBioinspired Antiswelling Hydrogel Sensors with High Strength and Rapid Self-Recovery for Underwater Information Transmission.
ACS Appl Mater Interfaces
January 2025
School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Hydrogel-based sensors typically demonstrate conspicuous swelling behavior in aqueous environments, which can severely compromise the mechanical integrity and distort sensing signals, thereby considerably constraining their widespread applicability. Drawing inspiration from the multilevel heterogeneous structures in biological tissues, an antiswelling hydrogel sensor endowed with high strength, rapid self-recovery, and low swelling ratio was fabricated through a water-induced phase separation and coordination cross-linking strategy. A dense heterogeneous architecture was developed by the integration of "rigid" quadridentate carboxyl-Zr coordination bonds and "soft" hydrophobic unit-rich regions featuring π-π stacking and cation-π interactions into the hydrogels.
View Article and Find Full Text PDFMarigold: a machine learning-based web app for zebrafish pose tracking.
BMC Bioinformatics
January 2025
Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.
Background: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise.
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