Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection.

Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection.

Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images.

A [(M)L] metal-organic polyhedron with high CO uptake and efficient chemical conversion of CO under ambient conditions.

A new metal-organic polyhedron with a high surface area of 407 m g, possessing high CO uptake, is reported, which is synthesized using 4-connected Cu(CO) paddle-wheel moieties and 3-connected semi-rigid tripodal carboxylates. This material possesses a high density of Cu(II) Lewis acidic sites and demonstrates excellent performance as a heterogeneous catalyst for the chemical fixation of CO into cyclic carbonates under ambient conditions.

The applications and prospects of hydrophobic metal-organic frameworks in catalysis.

In recent years, large numbers of hydrophobic/superhydrophobic metal-organic frameworks (MOFs) have been developed. These hydrophobic MOFs not only retain rich structural variety, highly crystalline frameworks, and uniform micropores, but they also have lower affinity towards water and boosted hydrolytic stability. Until now, there were two main strategies to prepare hydrophobic MOFs, including a one-step method and post-synthesis modification (PSM).

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars.

A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm.

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency.

High accuracy localization of long term evolution based on a new multiple carrier noise model.

A high accuracy localization technique using Long Term Evolution (LTE) based on a new and accurate multiple carrier noise model has been developed. In the noise consideration, the LTE multiple carriers phase noise has been incorporated so that a new and accurate noise model is achieved. An experiment was performed to characterize the phase noise of carriers at 2 GHz.