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Scientific model developers are able to verify and validate their software via metamorphic testing, even when the expected output of a given test case is not readily available. The tenet is to check whether certain relations hold among the expected outputs of multiple related inputs. Contemporary approaches require the relations to be defined before tests. Our experience shows that it is often straightforward to first define the multiple iterations of tests for performing continuous simulations, and then keep multiple and even competing metamorphic relations open for investigating the testing-result patterns. We call this new approach , and report our experience of applying it to detect bugs, mismatches, and constraints in automatically calibrating parameters for the United States Environmental Protection Agency's Storm Water Management Model (SWMM).
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http://dx.doi.org/10.1109/MCSE.2018.2880577 | DOI Listing |
J Exp Zool A Ecol Integr Physiol
September 2025
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA.
Peroxisome proliferator-activated receptors (PPAR) are master transcriptional regulators that maintain metabolic homeostasis in vertebrates. Amphibians are often exposed to endocrine disrupting compounds (EDCs) that could dysregulate lipid metabolism. Larvae of the African clawed frog (Xenopus laevis) are routinely used as a model to study aquatic EDC exposures, but PPAR expression has not been characterized across larval development or metamorphosis in this species.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
July 2025
The robust segmentation of different targets in multiple modality images is challenging due to factors such as low contrast, variations in target size and shape, and interference from diseases, which may lead to segmentation ambiguity. In addition, the assessment of the reliability of artificial intelligence is crucial for its clinical application. This paper proposes the Online Bayesian approximation based Uncertainty-aware Network (OBU-Net) for robust ophthalmic image segmentation.
View Article and Find Full Text PDFEvodevo
July 2025
Department of Applied Biosciences, Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwakecho, Sakyo-ku, Kyoto, 606-8502, Japan.
Background: Lineage-specific adult structures form through modifications of pre-existing juvenile body parts during postembryonic development in insects. It remains unclear how these novel traits originate from ancestral structures within the constrained body plan. In the coffin-headed cricket Loxoblemmus equestris, an ancestral rounded head shape directly transforms into a flattened derived form in a sex-specific manner.
View Article and Find Full Text PDFSci Rep
July 2025
Department of Mining Engineering, Earth Mechanics Institute (EMI, ), Colorado School of Mines, Golden, CO, USA.
This study examines how rock properties affect the production of fines and chips during rock cutting, a crucial aspect of mechanized excavation science that dictates cutting efficiency and excavator performance. Small-scale linear cutting tests using a conical tool were conducted on thirteen rock specimens made up of sedimentary and metamorphic rocks. Unrelieved mode cutting depths ranged from 0.
View Article and Find Full Text PDFPLoS One
May 2025
Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learning model (DBN-GRU) that integrates Deep Belief Networks (DBN) for static analysis and Gated Recurrent Units (GRU) for dynamic behavior modeling to enhance malware detection accuracy and efficiency. The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.
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