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An accident event may necessitate triage of multiple cases of traumatic out-of-hospital cardiac arrest (TOHCA). However, factors for prioritizing treatment among multiple TOHCA patients have not been established. This study aims to use easily assessible predictors of TOHCA outcomes to develop a triage scoring system.Patients with TOHCA brought to our hospital by emergency medical services (EMS) were included for analysis to identify independent risk factors for poor outcomes. A scoring system was developed and validated internally and externally.Of the 401 included patients, 86 (21.4%) had return of spontaneous circulation (ROSC) after cardiopulmonary resuscitation (CPR) for 30 min (81 patients, 94.2%) or 45 min (86 patients, 100%). The emergency department (ED) mortality rate was 89.3% and overall in-hospital mortality rate was 99%. Univariate and multivariate analyses identified body temperature33 °C (OR, 4.65; 95% CI, 1.37-15.86), obvious chest injury (OR, 2.11; 95% CI, 1.03-4.34), and presumable etiology of out-of-hospital cardiac arrest (OR, 1.73; 95% CI, 1.01-2.98) as significant independent risk factors for non-ROSC. The TOHCA score, calculated as 1 point per risk factor, correlated significantly with the rate of non-ROSC and ED mortality (TOHCA score 0, 1, 2, 3: non-ROSC rate, 63.0%, 80.4%, 90.8%, 100%, respectively; ED mortality rate, 79.5%, 91.5%, 96.1%, and 100% respectively). The results of internal and external validations show a similar trend in both non-ROSC and mortality in the ED with increasing score.Termination of CPR for TOHCA after 45 min is reasonable; a 30-min resuscitation is acceptable in case of insufficient medical staff or resources. The TOHCA score may be able to be used with caution for triage.

A 'plug-and-play' tiny artificial intelligence (AI)-empowered output filter parameter extraction framework for digital power is presented. It can be integrated into existing digital power readily without introducing extra sensors. The framework aims to provide digital power with the output filter parameter information to enhance or maintain system performance and to conduct system diagnostics. The concept is based on firstly transferring the control from the default controller to the framework with the predetermined control law for a few switching cycles via typical priority-based interrupt programming, then perturbing and observing the response of the control action, and finally determining the filter parameters with a recurrent neural network (RNN) having a single cell. The proposed framework has been applied and evaluated on a 150W, 180V / 120V buck DC/DC converter. Results show that the root-mean-square (RMS) errors of determining the values of the output inductor and capacitor are 2.28% and 2.86%, respectively.

A computational model for simulating the time-domain response of lithium batteries under arbitrary charging and discharging profiles is presented. The methodology is based on first formulating a mathematical model that describes the time-domain voltage–current characteristics of constant phase elements (CPEs), and then uses multiple series-connected CPEs, as well as charge transfer resistances, output resistances, and inductances, to form the computational model. The criteria for ensuring the stability of the computational model will be derived. The validity of the computational model is confirmed by comparing the simulation results of a model consisting of three series-connected CPEs with the measurement results of six SANYO 18650, 3300-mAh, 3.7-V rechargeable Li-ion batteries under controlled charging and discharging profiles. Finally, the accuracy of the computational model will be compared to the accuracy of the models using different orders of parallel resistor–capacitor networks.

An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.

A sophisticated discrete-time battery model with multiple constant phase elements (CPEs) for characterizing electrochemical impedance of batteries over wide frequency range will be presented. Its stability criteria and maximum allowable sampling period will be derived by using a high-order characteristic equation. The validity of the model will be confirmed by comparing the prediction results of a battery model consisting of three CPEs with the measurement results of a SANYO 18650, 3300mAh, 3.7V lithium-ion rechargeable battery under controlled charging and discharging conditions. A test setup with low contact impedance and high repeatability will also be outlined.

A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.

Background Cranial metastasis of thyroid cancer is rare. The aim of this study was to analyse the clinical characteristics, treatments and outcomes of thyroid cancer patients with cranial metastasis and to identify the associated prognostic factors. Materials and methods Between January 1977 and August 2017, a total of 4683 patients were histologically confirmed to have thyroid cancer. Among them, 25 patients (0.53%) were identified as having cranial metastases, and their medical records were reviewed. The Kaplan-Meier method with a log-rank test was performed with cancer-specific survival as the main outcome. Cox regression analysis was used to examine the potential prognostic factors influencing patient survival. Results Of the 25 patients, 21 were female, and 4 were male. The median age at the time of diagnosis of cranial metastasis was 63 years. Sixteen patients had metastases to the brain, and nine patients had metastases involving the skull only. Papillary carcinoma and follicular carcinoma accounted for 84.0% of cases. Twenty-four cases (96.0%) had extracranial metastases at the time of diagnosis of cranial metastases. Twenty patients received surgery, and 4 patients received palliative radiotherapy. One patient received supportive care only. The median cancer-specific survival after the diagnosis of cranial metastases was 27 months. According to the Kaplan-Meier test, 3 factors had a significant impact on survival, the metastatic site, histological types and surgical resection. According to the Cox regression analysis, skull metastases (HR: 0.274, 95% CI: 0.083–0.904, p = 0.033) and surgical resection (HR: 0.134, 95% CI: 0.019–0.929, p = 0.042) were identified as independent prognostic factors for a better outcome. Conclusions Surgical resection is the mainstay therapy for thyroid cancer patients with cranial metastasis. Cranial metastases involving the skull only are associated with a better outcome.

Salmonella enterica serotype Stanley became the third most common non-typhoidal Salmonella serotype among human isolates in 2004. The present study was conducted to gain further understanding of the epidemiology and antimicrobial suseptibility of S. Stanley.A total of 20 culture-confirmed cases were retrieved from the Center for Disease Control collection and analyzed. Clinical features and demographic data of the cases were analyzed. Laboratory investigation of the isolates included antimicrobial susceptibility testing and molecular typing by pulsed-field gel electrophoresis. Ceftriaxone-non-susceptible isolates were further examined by polymerase chain reaction, sequencing, and Southern blot hybridization.The cases studied were distributed widely across Taiwan, suggesting that the infection was an island-wide problem. S. Stanley predominantly caused infections in patients under the age of 5 years (75%). The most common type of illness was uncomplicated enterocolitis. Molecular typing showed 1 predominant genotype with 5 subtypes among these isolates. Antimicrobial resistance to ampicillin (75%), chloramphenicol (95%), and trimethoprim-sulfamethoxazole (95%) was common. Two isolates expressed non-susceptibility to ceftriaxone, and a bla(CMY-2) gene was identified on an 80-kb plasmid in both isolates.The increase in S. Stanley infections may be associated with the spread of an epidemic clone, although this requires further epidemiological surveillance. In view of the high rate of antimicrobial resistance, especially the emergence of resistance to third-generation cephalosporins, continued surveillance of the infections caused by this bacterium should be undertaken.

Background Hypoxia could lead to microglia activation and inflammatory mediators’ overproduction. These inflammatory molecules could amplify the neuroinflammatory process and exacerbate neuronal injury. The aim of this study is to find out whether harpagoside could reduce hypoxia-induced microglia activation. Methods In this study, primary microglia cells harvested from neonatal ICR mice were activated by exposure to hypoxia (1 % O2 for 3 h). Harpagoside had been shown to be no cytotoxicity on microglia cells by MTT assay. The scavenger effect of harpagoside on hypoxia-enhanced microglial cells proliferation, associated inflammatory genes expression (COX-II, IL-1β and IL-6 genes) and NO synthesis were also examined. Results Hypoxia enhances active proliferation of microglial cells, while harpagoside can scavenge this effect. We find that harpagoside could scavenge hypoxia-enhanced inflammatory genes expression (COX-2, IL-1β and IL-6 genes) and NO synthesis of microglial cells. Under 3 h’ hypoxic stimulation, the nuclear contents of p65 and hypoxia inducible factor-1α (HIF-1α) significantly increase, while the cytosol IκB-α content decreases; these effects can be reversed by 1 h’s pre-incubation of 10−8 M harpagoside. Harpagoside could decrease IκB-α protein phosphorylation and inhibit p65 protein translocation from the cytosol to the nucleus, thus suppress NF-κB activation and reduce the HIF-1α generation. Conclusion These results suggested that the anti-inflammatory mechanism of harpagoside might be associated with the NF-κB signaling pathway. Harpagoside protect against hypoxia-induced toxicity on microglial cells through HIF-α pathway.