209 research outputs found

    Monolithic Solid Oxide Fuel Cell development

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    The Monolithic Solid Oxide Fuel Cell (MSOFC) is an oxide-ceramic structure in which appropriate electronic and ionic conductors are fabricated in a honeycomb shape similar to a block of corrugated paperboard. These electronic and ionic conductors are arranged to provide short conduction paths to minimize resistive losses. The power density achievable with the MSOFC is expected to be about 8 kW/kg or 4 kW/L, at fuel efficienceis over 50 percent, because of small cell size and low resistive losses in the materials. The MSOFC operates in the range of 700 to 1000 C, at which temperatures rapid reform of hydrocarbon fuels is expected within the nickel-YSZ fuel channels. Tape casting and hot roll calendering are used to fabricate the MSOFC structure. The performance of the MSOFC has improved significantly during the course of development. The limitation of this system, based on materials resistance alone without interfacial resistances, is 0.093 ohm-sq cm area-specific resistance (ASR). The current typical performance of MSOFC single cells is characterized by ASRs of about 0.4 to 0.5 ohm-sq cm. With further development the ASR is expected to be reduced below 0.2 ohm-sq cm, which will result in power levels greater than 1.4 W/sq cm. The feasibility of the MSOFC concept was proven, and the performance was dramatically improved. The differences in thermal expansion coefficients and firing shrinkages among the fuel cell materials were minimized. As a result of good matching of these properties, the MSOFC structure was successfully fabricated with few defects, and the system shows excellent promise for development into a practical power source

    Zinc (II) and the single-stranded DNA binding protein of bacteriophage T4.

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    Creatine supplementation post-exercise does not enhance training-induced adaptations in middle to older aged males

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    PURPOSE: The present study evaluated the effects of creatine monohydrate (CrM) consumption post-exercise on body composition and muscle strength in middle to older males following a 12-week resistance training program. METHODS: In a double-blind, randomized trial, 20 males aged between 55 and 70 years were randomly assigned to consume either CrM-carbohydrate (CHO) [20 g days(−1) CrM + 5 g days(−1) CHO × 7 days, then 0.1 g kg(−1) CrM + 5 g CHO on training days (average dosage of ~8.8 g)] or placebo CHO (20 g days(−1) CHO × 7 days, then 5 g CHO on training days) while participating in a high intensity resistance training program [3 sets × 10 repetitions at 75 % of 1 repetition maximum (1RM)], 3 days weeks(−1) for 12 weeks. Following the initial 7-day “loading” phase, participants were instructed to ingest their supplement within 60 min post-exercise. Body composition and muscle strength measurements, blood collection and vastus lateralis muscle biopsy were completed at 0, 4, 8 and 12 weeks of the supplement and resistance training program. RESULTS: A significant time effect was observed for 1RM bench press (p = 0.016), leg press (p = 0.012), body mass (p = 0.03), fat-free mass (p = 0.005) and total myofibrillar protein (p = 0.005). A trend for larger muscle fiber cross-sectional area in the type II fibers compared to type I fibers was observed following the 12-week resistance training (p = 0.08). No supplement interaction effects were observed. CONCLUSION: Post-exercise ingestion of creatine monohydrate does not provide greater enhancement of body composition and muscle strength compared to resistance training alone in middle to older males

    Improvement of performance of InAs quantum dot solar cell by inserting thin AlAs layers

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    A new measure to enhance the performance of InAs quantum dot solar cell is proposed and measured. One monolayer AlAs is deposited on top of InAs quantum dots (QDs) in multistack solar cells. The devices were fabricated by molecular beam epitaxy. In situ annealing was intended to tune the QD density. A set of four samples were compared: InAs QDs without in situ annealing with and without AlAs cap layer and InAs QDs in situ annealed with and without AlAs cap layer. Atomic force microscopy measurements show that when in situ annealing of QDs without AlAs capping layers is investigated, holes and dashes are present on the device surface, while capping with one monolayer AlAs improves the device surface. On unannealed samples, capping the QDs with one monolayer of AlAs improves the spectral response, the open-circuit voltage and the fill factor. On annealed samples, capping has little effect on the spectral response but reduces the short-circuit current, while increasing the open-circuit voltage, the fill factor and power conversion efficiency

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    Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

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    There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier
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