Results 71 to 80 of about 15,135,820 (245)

Flexural Behavior of 3D‐Printed Porous Bouligand‐Structured Polymers: Influence of Pitch Angle and Porosity

Advanced Engineering Materials, EarlyView.
This study examines how helicoidal architectures with different porosities respond to bending. Adjusting layer angle and spacing in 3D‐printed polymers reveals clear tradeoffs between stiffness, strength, and energy absorption. Experiments and simulations highlight designs that distribute stress effectively, offering pathways for optimizing lightweight
Praveenkumar Subhash Patil, Edward D. McCarthy, Parvez Alam   +2 more
wiley   +1 more source

An attribute selection process for software defect prediction

, 2014
In software quality research, software defect prediction is a key topic. The characteristics of software attributes influences the performance and effectiveness of the defect prediction model.
Siddik, Md.S., Rahman, Md.H., Shoyaib, M., Gias, A.U., Khan, J.I., Khaled, S.M.   +5 more
core   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Research on Software Defect Prediction Models Combining Static Analysis Warnings [PDF]

Jisuanji kexue yu tansuo
Static analysis warnings, as an important software quality metric, are widely used to identify potential violations in the source code. Recent studies have shown that static analysis warnings are applied in code smell detection and just-in-time defect ...
WU Haitao, MA Jingyue, GAO Jianhua
doaj   +1 more source

Modular Critical Element Recycling Platform Using a Nanoporous Additively Manufactured Gyroid

Advanced Engineering Materials, EarlyView.
A modular recycling platform integrates 3D‐printed nanoporous gyroid structures to enable efficient critical element recovery. This system utilizes a hierarchical architecture, combining macroscopic channels with polymerization‐induced nanoscale porosity. By systematically tuning structural wall thickness and resin formulation, the platform achieves an
Xiangyu Gao, Keldy Mason, Mert Gülçür, Rachel Paddock‐Lamb, Prince Alvin Fofanah, Zachariah A. Page, Michael Cullinan   +6 more
wiley   +1 more source

Multi-Source Transfer Weighted Learning with Random Forest for Cross-Project Software Defect Prediction

International Journal of Emerging Research in Engineering, Science, and Management
Accurate software defect prediction is essential for identifying fault-prone modules early and reducing software testing and maintenance costs. However, Within-Project Defect Prediction (WPDP) models may perform poorly when the target project has limited
Kummarikunta Sandhya, P. Radhika Raju, A. Anandarao   +2 more
doaj   +1 more source

Seml: A Semantic LSTM Model for Software Defect Prediction

IEEE Access, 2019
Software defect prediction can assist developers in finding potential bugs and reducing maintenance cost. Traditional approaches usually utilize software metrics (Lines of Code, Cyclomatic Complexity, etc.) as features to build classifiers and identify ...
Hongliang Liang, Yue Yu, Lin Jiang, Zhuosi Xie   +3 more
doaj   +1 more source

An Adversarial Discriminative Convolutional Neural Network for Cross-Project Defect Prediction

IEEE Access, 2020
Cross-project defect prediction (CPDP) is a promising approach to help to allocate testing efforts efficiently and guarantee software reliability in the early software lifecycle.
Lei Sheng, Lu Lu, Junhao Lin
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Cross Project Software Defect Prediction Using Machine Learning with Optimized Feature Selection [PDF]

E3S Web of Conferences
In smart city software systems, where interconnected services demand high reliability, Software Defect Prediction (SDP) plays a vital role and reducing maintenance costs by identifying defect-prone modules early in the Software Development Life Cycle ...
Obot Emediong Bassey, Anaga Victor, Thomas Sadiq, Stephen Bliss Utibe-Abasi, Bassey Samuel Okon, Udoabba Solomon Joseph, Asuquo Philip, Nnajiofor Amarachi Vivian, Ighofiomoni Michael Oghale   +8 more
doaj   +1 more source