Results 201 to 210 of about 16,855 (255)

Analysis of Chip Electronic Components' Typical Yield in Taping Process Based on Virtual Metrology. [PDF]

Sensors (Basel)
Zhang S, Chen L, Fu J, Yang C, Chen G.
europepmc   +1 more source

Review of Inorganic Separator Engineering for Next‐Generation Lithium–Sulfur Batteries: Compromise or Cornerstone?

Advanced Materials, EarlyView.
Inorganic compound‐modified separators transform lithium–sulfur batteries from passive polysulfide confinement to active reaction‐pathway regulation. A Practical Relevance Index (PRI)‐guided framework bridges interfacial chemistry of inorganic separators with practical constraints, establishing unified design principles for scalable, high‐energy ...
Yuting Qin, Tianyi Wang, Xingyu Wang, Kaifu Xu, Yuzhou Wang, Chengyin Wang, Bing Sun, Guoxiu Wang   +7 more
wiley   +1 more source

Explainable machine learning model for identifying key risk factors in congenital heart disease prediction using questionnaire data: a retrospective case-control study. [PDF]

BMC Med Inform Decis Mak
Wang Y, Li T, Chen J, Ruan J, Shu Q, Xu W, Li D.   +6 more
europepmc   +1 more source

In Situ X-Ray Imaging and Machine Learning in Ultrasonic Field-Assisted Laser-Based Additive Manufacturing: A Review. [PDF]

Materials (Basel)
Fu Z, Weng Y, Deng Z, Pan J, Li A, Qin L, Wu G.   +6 more
europepmc   +1 more source

A nomogram for predicting free flap necrosis in soft tissue reconstruction of lower limbs: a retrospective cohort study. [PDF]

Front Med (Lausanne)
Cheng C, Huang X, Liang H, Jiang Z.
europepmc   +1 more source

Deep Learning for Process Monitoring and Defect Detection of Laser-Based Powder Bed Fusion of Polymers. [PDF]

Polymers (Basel)
Vaezi M, Klamert V, Bublin M.
europepmc   +1 more source

3D Road Defect Mapping via Differentiable Neural Rendering and Multi-Frame Semantic Fusion in Bird's-Eye-View Space. [PDF]

J Imaging
Xing H, Yang F.
europepmc   +1 more source

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An investigation on the feasibility of cross-project defect prediction

Automated Software Engineering, 2011
Software defect prediction helps to optimize testing resources allocation by identifying defect-prone modules prior to testing. Most existing models build their prediction capability based on a set of historical data, presumably from the same or similar project settings as those under prediction. However, such historical data is not always available in
Qing Wang, Ye Yang
exaly   +2 more sources