In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.

This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses Approximate Karbunen–Loeve transform for noise reduction. The proposed QBS-CBMR system uses a music clip as a search key. First, a 51-dimensional matrix containing 39-Mel-frequency cepstral coefficients (MFCCs) features and 12-Chroma features are extracted from an input music clip. Next, adapted symbolic aggregate approximation (adapted SAX) is used to transform each dimension of features into a symbolic sequence. Each symbolic sequence corresponding to each dimension of MFCCs is then converted into a structure called advanced fast pattern index (AFPI) tree. The similarity between the query music clip and the songs in the database is evaluated by calculating a partial score for each AFPI tree. The final score is obtained by calculating the weighted sum of all partial scores, where the weighting of each partial score is determined by its entropy. Experimental results show that the proposed music retrieval system performs robustly and accurately with the entropy weighting mechanism.

Si1−xGexOynanowire (SiGeONW) assemblies with cord-, chain-, and tubelike morphologies were grown on a Si substrate via the carbothermal reduction of GeO2/CuO powders at 1100 °C in Ar. The growth of various SiGeONWs assemblies follows the vapor-liquid-solid process. The CuSiGe droplets formed during the growth of SiGeONWs simultaneously play the roles of catalyst and reactant. The morphology of SiGeONWs assemblies is not temperature controlled but dependent on the Cu concentration and the size of CuSiGe catalysts. This phenomenon is unlike the Ge- and Ga-catalyzed growth of SiOxnanowire assemblies. In addition, the processing parameters and the mechanisms for the growth of SiGeONWs assemblies with various morphologies are discussed.

ZnO films were grown on (0001) sapphire substrates by atomic layer deposition (ALD) using diethylzinc (DeZn) and nitrous oxide (N2O) in an inductively heated reactor operated at atmospheric pressure. Low-temperature (LT) ZnO buffer layers having various thicknesses were deposited at 400¢J followed by subsequent growth of ZnO films at 600¢J. Some of the ZnO films were then post-annealed at 1000¢J in the N2O flow. Under certain growth conditions, ZnO nanowires were formed on the post-annealed ZnO samples. Room temperature (RT) photoluminescence (PL) spectra of the ZnO nanowires show strong ultraviolet (UV) near band edge emissions at 3.27 eV with a typical full width at half-maximum ( FWHM ) of ~130 meV and quenched defect luminescence at 2.8 eV. 10 K PL spectra of the post-annealed ZnO all exhibit sharp excitonic emissions with the dominant emission being located at 3.36 eV having a FWHM of 4.6 meV.