Experimental analysis of power harvesting on vehicle vibration using smart piezoelectric materials

In this paper the experimental analysis for power harvesting from mechanical vibration on a vehicle has been studied by using QuickPack smart materials with piezoelectric effect. The finite element ANSYS method (ANSYS FEM) was applied to explore the required mechanical structure, modal and harmonic analysis, and electrical feature, i.e., output voltage, admittance. The experimental platform consists of a shocker and a lever, which simulated a periodical oscillation on vehicle vibration, for evaluating conversion efficiency from mechanical energy to electrical energy. During loading experiments of power generation, the electromechanical coupling characteristics of smart materials were investigated via a proposed testing circuit. Also, various electrical output loadings were specified within resistance of 5~3000 kΩ. Through the experiment analysis, the power harvesting test with a buck converter at the output terminal was processed to obtain the spectrum analysis of output voltage within the vibrating frequencies below 200 Hz, controlled by the electromagnetic shaker. Based on the comparison between ANSYS FEM and spectrum analysis, the optimal results of mechanical oscillating quantities have been verified by the maximum output voltage for the QuickPack NQ45N material. Hence, the optimum power harvesting of the smart material has the maximum output power of 0.18 mW at 26-Hz-vibration on a vehicle

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI

Inhibition of the Mycobacterium tuberculosis reserpine-sensitive efflux pump augments intracellular concentrations of ciprofloxacin and enhances susceptibility of some clinical isolates

Active efflux is known to play a major role in the resistance of many bacteria to antibiotics. To evaluate the possibility of overcoming resistance by suppressing the efflux, we determined the effect of reserpine, an efflux pump inhibitor. Methods: Intracellular accumulations and the minimal inhibitory concentrations (MICs) of ciprofloxacin in M. tuberculosis H37Rv and 16 clinical isolates were determined, compared, and analyzed. Nine of the clinical isolates were resistant to isoniazid and rifampin (multiple-drug resistant MDR). Five of these were resistant to ciprofloxacin. Results: A reserpine-inhibited efflux system was identified in the H37Rv control and 10:1 (90.9%) of ciprofloxacin-susceptible and 4:1 (80%) of ciprofloxacin-resistant clinical isolates. The MIC of ciprofloxacin decreased in the presence of reserpine in 3/10 (30%) of the ciprofloxacin-susceptible and 2/4 (50%) of the MDR ciprofloxacin-resistant strains that expressed efflux pumps. Two of the efflux-positive, ciprofloxacin-resistant strains in which the MIC of ciprofloxacin was not decreased by reserpine were found to carry a D94A gyrA mutation. In contrast, two strains with the D94G gyrA mutation were susceptible to ciprofloxacin in the presence of reserpine. An efflux-negative strain, highly resistant to multiple antibiotics, was found to have a novel G247S mutation that differs from known mutations in the QRDR region of the gyrA gene. Conclusion: These findings indicate t hat reserpine can increase intracellular concentrations of ciprofloxacin, but is unable to overcome other mechanisms of resistance in clinical isolates