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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 exercise aimed to validate New Taipei City’s strategic plan for a city lockdown in response to coronavirus disease (COVID-19). The main goal of all solutions was the principle of “reducing citizen activity and strengthening government control.”
We created a suitable exercise, creating 15 hypothetical situations for 3 stages. All participating units designed and proposed policy plans and execution protocols according to each situation.
In the course of the exercise, many existing policies and execution protocols were validated. These addressed (1) situations occurring in Stage 1, when the epidemic was spreading to the point of lockdown preparations; (2) approaches to curb the continued spread of the epidemic in Stage 2; and (3) returning to work after the epidemic was controlled and lockdown lifted in Stage 3. Twenty response units participated in the exercise. Although favorable outcomes were obtained, the evaluators provided comments suggesting further improvements.
Our exercise demonstrated a successful example to help policy-making and revision in a large city of over 4 million people during the COVID-19 pandemic. It also enhanced participants’ subject knowledge and familiarity with the implementation of a city lockdown. For locations intending to go into lockdown, similar tabletop exercises are an effective verification option.
Information systems (IS) have facilitated workflow in the health care system for years. However, the utilization of IS in disaster medical assistance teams (DMATs) has been less studied.
In Taiwan, we started a program in 2008 to build up an information system, MEDical Assistance and Information Dashboard (MED-AID), to improve the capability and increase the efficiency of our national DMAT.
Method: The mission of our national DMAT was to provide acute trauma care and subacute outpatient care in the field after an emergency event (e.g., earthquakes). We built the IS through a user-oriented process to fit the need of the DMAT. We first analyzed the response work in the DMAT missions and reviewed the current paperwork. We evaluated the eligibility and effectiveness of the core functions of DMATs by experts in Taiwan and then developed the IS. The IS was then tested and revised each year in two table-top exercises and one regional full-scale exercise by the DMAT staffs who came from different hospitals in Taiwan.
During the past 10 years, we identified several core concepts of IS of DMAT: patient tracking, medical record, continuity of care, integration of referral resources, disease surveillance, patient information reporting, and medical resources management. The application of the IS facilitate the DMAT in providing safe patient care with continuous recording and integrate patient referral resources based on geographic information. The IS also help the planning in real-time disease surveillance and logistic function in the medical resources monitoring.
Information systems could facilitate patient care and relieve the workload on information analysis and resources management for DMATs.
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.
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