Applications of Switch-Mode Rectifiers on Micro-grid Incorporating with EV and BESS

A switch-mode rectifier (SMR) can provide adjustable and well-regulated DC output voltage from the available AC source with good line drawn power quality. Depending on the input/output voltage transfer characteristics, the schematics, the operation quadrant, and control, SMRs possess many classifications and application. Typical potential application examples include grid powered motor drives, battery chargers, various power electronic facilities, micro-grids, and grid-connected battery energy storage system (BESS), etc. In micro-grids, the SMR can be employed as the AC generator-followed converter to yield better generating efficiency. The SMR operation of its grid-connected inverter let the grid-to-microgrid (G2M) operation be conductable in addition to the microgrid-to-grid (M2G) operation. As for the electric vehicle (EV), the bidirectional inverter can be arranged to perform G2V/V2G operations in idle case, wherein the SMR operation is made in G2V battery charging

Room-Temperature Power-Stabilized Narrow-Linewidth Tunable Erbium-Doped Fiber Ring Laser Based on Cascaded Mach-Zehnder Interferometers with Different Free Spectral Range for Strain Sensing

An automatically power-stabilized (with power fluctuation <0.155 dB), narrow-linewidth (0.0171 nm), wavelength-tunable (10.69 nm) erbium-doped fiber laser has been proposed by cascading two fiber Mach-Zehnder interferometers (MZI) without using any temperature controlling device. One of the MZIs (here called the 1st MZI) is composed of two 3 dB couplers to form interference patterns while the other MZI (here termed the 2nd MZI) is constructed with a tapered seven-core fiber (SCF) and based on the principle of supermode interference. For the two MZIs, the free spectral range (FSR), the passband bandwidth and the extinction ratio (ER) at 1560 nm are 0.37 nm, 0.19 nm, 16.6 dB and 13.93 nm, 7.93 nm, 10.1 dB, respectively. Due to the major difference between the two FSR values, the 1st MZI and the 2nd MZI respectively play a role in controlling the laser linewidth and suppressing the homogeneous broadening effect to reach to a satisfactory level of power stability. The 2nd MZI is also used to fine tune the laser wavelength by applying strain to the tapered SCF (TSCF) over the spectral range of 1570.22-1559.33 nm, with an incremental step of 0.37 nm being used. The side-mode suppression ratio (SMSR) of the tunable fiber laser can be up to 45 dB. By appropriately adjusting the polarization controller, dual wavelength lasing can also be achieved. For single wavelength lasing, the 3 dB laser linewidth is 0.0171 nm. The power fluctuation, without a temperature controlling device being used and operating at room temperature, is found to be less than 0.155 dB over 1 hour while the central wavelength drift is less than 0.19 nm

Predicting meiofauna abundance to define preservation and impact zones in a deep-sea mining context using random forest modelling

There is a strong economic interest in commercial deep‐sea mining of polymetallic nodules and therefore a need to define suitable preservation zones in the abyssal plain of the Clarion Clipperton Fracture Zone (CCZ). However, besides ship‐based multibeam data, only sparse continuous environmental information is available over large geographic scales. We test the potential of modelling meiofauna abundance and diversity on high taxonomic level on large geographic scale using a random forest approach. Ship‐based multibeam bathymetry and backscatter signal are the only sources for 11 predictor variables, as well as the modelled abundance of polymetallic nodules on the seafloor. Continuous meiofauna predictions have been combined with all available environmental variables and classified into classes representing abyssal habitats using k‐means clustering. Results show that ship‐based, multibeam‐derived predictors can be used to calculate predictive models for meiofauna distribution on a large geographic scale. Predicted distribution varies between the different meiofauna response variables. To evaluate predictions, random forest regressions were additionally computed with 1,000 replicates, integrating varying numbers of sampling positions and parallel samples per site. Higher numbers of parallel samples are especially useful to smoothen the influence of the remarkable variability of meiofauna distribution on a small scale. However, a high number of sampling positions is even more important, integrating a greater amount of natural variability of environmental conditions into the model. Synthesis and applications. Polymetallic nodule exploration contractors are required to define potential mining and preservation zones within their licence area. The biodiversity and the environment of preservation zones should be representative of the sites that will be impacted by mining. Our predicted distributions of meiofauna and the derived habitat maps are an essential first step to enable the identification of areas with similar ecological conditions. In this way, it is possible to define preservation zones not only based on expert opinion and environmental proxies but also integrating evidence from the distribution of benthic communities

The fcc-bcc crystallographic orientation relationship in AlxCoCrFeNi high-entropy alloys

This paper concentrates on the crystallographic-orientation relationship between the various phases in the Al-Co-Cr-Fe-Ni high-entropy alloys. Two types of orientation relationships of bcc phases (some with ordered B2 structures) and fcc matrix were observed in Al0.5CoCrFeNi and Al0.7CoCrFeNi alloys at room temperature: (1 -1 0)(bcc)//(200)(fcc), [CM](bcc)//[001](fcc), (b) (1 -1 1)B2//(2 - 2 0)(fcc), [011]B2//[11 root 2](fcc). (C) 2016 Elsevier B.V. All rights reserved.</p

Preventing chronic disease in overweight and obese patients with low health literacy using eHealth and teamwork in primary healthcare (HeLP-GP): A cluster randomised controlled trial

OBJECTIVES: To evaluate a multifaceted intervention on diet, physical activity and health literacy of overweight and obese patients attending primary care. DESIGN: A pragmatic two-arm cluster randomised controlled trial. SETTING: Urban general practices in lower socioeconomic areas in Sydney and Adelaide. PARTICIPANTS: We aimed to recruit 800 patients in each arm. Baseline assessment was completed by 215 patients (120 intervention and 95 control). INTERVENTION: A practice nurse-led preventive health check, a mobile application and telephone coaching. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were measured at baseline, 6 and 12 months, and included patient health and eHealth literacy, weight, waist circumference and blood pressure. Secondary outcomes included changes in diet and physical activity, preventive advice and referral, blood lipids, quality of life and costs. Univariate and multivariate analyses of difference-in-differences (DiD) estimates for each outcome were conducted. RESULTS: At 6 months, the intervention group, compared with the control group, demonstrated a greater increase in Health Literacy Questionnaire domain 8 score (ability to find good health information; mean DiD 0.22; 95% CI 0.01 to 0.44). There were similar differences for domain 9 score (understanding health information well enough to know what to do) among patients below the median at baseline. Differences were reduced and non-statistically significant at 12 months. There was a small improvement in diet scores at 6 months (DiD 0.78 (0.10 to 1.47); p=0.026) but not at 12 months. There were no differences in eHealth literacy, physical activity scores, body mass index, weight, waist circumference or blood pressure. CONCLUSIONS: Targeted recruitment and engagement were challenging in this population. While the intervention was associated with some improvements in health literacy and diet, substantial differences in other outcomes were not observed. More intensive interventions and using codesign strategies to engage the practices earlier may produce a different result. Codesign may also be valuable when targeting lower socioeconomic populations. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN 12617001508369) (http://www.ANZCTR.org.au/ACTRN12617001508369.aspx). TRIAL PROTOCOL: The protocol for this trial has been published (open access; https://bmjopen.bmj.com/content/8/6/e023239)