Results 121 to 130 of about 4,236 (288)
An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
Code smells, which represent poor design choices or suboptimal code implementations, reduce software quality and hinder the code maintenance process. Detecting code smells is, therefore, essential during software development.
Rajwant Singh Rao +2 more
doaj +1 more source
Information Transmission Strategies for Self‐Organized Robotic Aggregation
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
QBP1 Peptide as a Potential Anti‐Amyloidogenic Therapy for Type 2 Diabetes: An In Vitro Study
The anti‐amyloidogenic peptide QBP1 effectively halts human islet amyloid polypeptide (hIAPP) aggregation, preventing the formation of toxic β‐structured intermediates. Through a combination of biophysical assays, molecular dynamics, and cell‐based studies, QBP1 is shown to preserve β‐cell viability and metabolic homeostasis, positioning it as a ...
María M. Tejero‐Ojeda +8 more
wiley +1 more source
A study for method-level code smells detection using machine learning algorithms
Motivation: Code smells reflect poor design decisions that degrade software quality and maintainability. Although several machine learning algorithms have been proposed to detect code smells, the impact of feature selection and cross-validation on ...
Rajwant Singh Rao +3 more
doaj +1 more source
Malectin alleviates high glucose‐induced ER stress and damage in placental trophoblasts, a function dependent on its six critical carbohydrate‐binding residues. In a GDM mouse model, administration of TAT‐Malectin ameliorated hyperglycemia and placental ER stress and prevented fetal macrosomia.
Jiahui Zhu +12 more
wiley +1 more source
We uncover a large variety of putative inhibitory ligand‐gated ion channels (LGICs) in the phylum Cnidaria, the sister group to all bilaterian animals. Phylogenetic analysis suggests a complex evolutionary history of inhibitory LGICs with diverse neurotransmitter ligands.
Abhilasha Ojha +13 more
wiley +1 more source
Machine Learning for Green Solvents: Assessment, Selection and Substitution
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
A Catalog of Data Smells for Coding Tasks
Large Language Models (LLMs) are increasingly becoming fundamental in supporting software developers in coding tasks. The massive datasets used for training LLMs are often collected automatically, leading to the introduction of data smells. Previous work addressed this issue by using quality filters to handle some specific smells. Still, the literature
Antonio Vitale +2 more
openaire +1 more source
Do bad smells indicate "trouble" in code?
In 1999 Fowler et al. identified 22 Bad Smells in code to direct the effective refactoring. These are increasingly being used by software engineers. However, the empirical basis of using Bad Smells to direct refactoring and to address 'trouble' in code ...
Hall, T. +3 more
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