Search

Context. Cardiovascular risk is increased in women with polycystic ovary syndrome (PCOS). Do insulin sensitizing agents such as metformin (MET) and myoinositol (MI) ameliorate biomarkers of cardiovascular risk? Objective. To compare the effects of MET and MI on blood pressure, lipid profile and high sensitive C-reactive protein (hs-CRP) in women with PCOS in respect to their body mass index (BMI). Design. Open label, parallel randomized, single center study. Subjects and Methods. Sixty six women with PCOS (33 normal-weight and 33 overweight/obese) were randomized to either MI (4 g/day) or MET (1500 mg/day) for a period of 6 months. Serum concentration of hormones, lipid profile, oxidized LDL (ox-LDL), hs-CRP, blood pressure measurement and clinical assessment of BMI, waist circumference (WC) and Ferriman Gallwey score (FG score) were performed before and after treatment. Results. Thirty patients in each group completed the trial. Compared with MET, MI significantly decreased diastolic blood pressure (DBP) (p=0.036) and significantly increased serum hs-CRP (p=0.043). No differences between groups in total cholesterol (TC), HDL-cholesterol, LDLcholesterol, ox-LDL and triglycerides were reported after 6 months. Treatment with MI reduced BMI (p=0.037), WC (p=0.005), DBP (p=0.021) and TC (p=0.008). During MET treatment a significant decrease in BMI (p=0.005), WC (p=0.004), FG score (p=0.001), testosterone (p=0.013) and free androgen index (FAI) (p=0.006) was observed. Conclusions. Our study showed an advantage of MI in reduction of DBP and TC thus predicting favorable metabolic and cardiovascular outcomes in PCOS women. MET more effectively decrease indices of hyperandrogenism. [ABSTRACT FROM AUTHOR]

Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, sub-metering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and NILM data. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households’ own lived experience. Thus, besides strictly technical algorithmic approaches for processing quantitative smart meter data, we also draw on social science time use approaches and interview and ethnography data. Our method disaggregates a households total electricity load down to appliance level and provides the start time, duration, and total electricity consumption for each occurrence of appliance usage. We then make inferences about activities occurring in the home by combining these disaggregated data with an ontology that formally specifies the relationships between electricity-using appliances and activities. We also propose two novel standardised metrics to enable easy quantifiable comparison within and across households of the energy intensity and routine of activities of interest. Finally, we demonstrate our results over a sample of ten households with an in-depth analysis of which activities can be inferred with the qualitative and quantitative data available for each household at any time, and the level of accuracy with which each activity can be inferred, unique to each household. This work has important applications from providing meaningful energy feedback to households to comparing the energy efficiency of households’ daily activities, and exploring the potential to shift the timing of activities for demand management. [ABSTRACT FROM AUTHOR]

Aim Our aim was to estimate the incidence of acute and late genitourinary toxicity in patients treated with three-dimensional conformal radiotherapy (3DCRT) for localised prostate cancer and to estimate the possible influence of individual and clinical characteristics. Materials and methods Between September 2009 and September 2013, 225 patients with localised prostate cancer were treated with 3DCRT. Ninety-four patients with an estimated risk of lymph node involvement ≤15%, according to the Roach formula, were evaluated in this study. All patients received a total dose of 72 Gy in 36 fractions. Acute and late genitourinary toxicity were graded according to the European Organization for Research and Treatment of Cancer radiation morbidity scoring scale. Characteristics such as age, smoking status, previous abdominal or pelvic surgery (PAPS), diabetes mellitus and the use of diuretics were analysed as possible predictive factors of toxicity. The median follow-up was 27 months. Results Grade ≥2 acute toxicity during 3DCRT developed in 25 of 94 patients (26.5%). Predictive factors of acute genitourinary toxicity grade ≥2 in the multivariate logistic regression analysis (MVA) were current smoking status ( P = 0.003), PAPS ( P = 0.012) and the use of diuretics ( P = 0.017). The 2 and 3 year cumulative risk of late genitourinary toxicity grade ≥1 was 25.3% and 30.2%, respectively. In the MVA, acute genitourinary toxicity was significantly associated with late genitourinary toxicity ( P = 0.024). Conclusion Current smoking status, PAPS and the use of diuretics have a significant effect on the occurrence of acute genitourinary toxicity grade ≥2. The occurrence of any grade of acute genitourinary toxicity has a significant influence on the development of any grade of late genitourinary toxicity. [ABSTRACT FROM AUTHOR]

Abstract: Solvent extraction experiments were carried out to recover silver from nitric acid solutions using as the extractants calix[4]arene tetramide (LBC) and its thio-analogue (THIO) dissolved in dichloromethane. The stoichiometry of the formation of the silver-calixarene complex differs depending on the used extractant. It was shown that one silver ion reacts with a single molecule of LBC, while two silver ions are extracted with a single THIO molecule. Very high percent extractions (>90% with LBC and >99% with THIO) were achieved with both extractants by just adding surplus extractant (THIO: 0.6mol/mol Ag+ and LBC: 1.4–1.5mol/mol Ag+). Based on equilibrium data, the extraction isotherm relationship and the extraction constant K ex for each extractant were derived. The extraction results show that THIO is an efficient extractant extracting silver either from acid or from neutral solutions with the same efficiency. Nitric acid, in the concentration range from 0 to 0.1moldm−3, strongly affects the extraction capability of LBC in less acidic solutions, reducing the percent extraction down to 40%. LBC extracts both silver and sodium collectively and with the same efficiency. The selectivity coefficient has a value close to unity (β ≈1). THIO has demonstrated extremely high selectivity for silver over sodium in a mixture. Stripping of silver was performed by two-phase electrowinning from the loaded organic phase, thereby achieving a high stripping degree (50–90%) and reasonably high current efficiency (60–90%). The calixarenes did not change their extraction features during the electrowinning process and can be reused for extraction many times. [Copyright &y& Elsevier]

Microseismic monitoring has been used in geo-energy related activities, such as shale-gas exploitation, mining, deep geothermal exploitation, geotechnical and structural engineering, for detecting and locating fractures, rock failures and micro-earthquakes. The success of microseismic monitoring depends on reliable detection and location of the recorded microseismicity. Multichannel coherence migration (MCM) is a detection and location waveform migration-based approach which does not require phase picking, identification and association and performs well on noisy data. Its caveat is a high computational cost, which impedes its application of MCM on large data sets or for real-time monitoring. To address this issue, we propose an improved approach, the multichannel coherence migration grid search (MCMgs), by introducing an adaptive grid optimization technique. Based on results from synthetic and real data, we show that MCMgs reduces the computation time up to 64 times. In addition, MCMgs generates multiple maximum coherence values with various grid sizes instead of a single (maximum) coherence value that links to a single gridpoint and size, thus resulting in more accurate locations. Our simulation results on different deployment geometries demonstrate that MCMgs is effective even with a small number of recordings available—a minimum of seven. We conduct a sensitivity analysis to assess how the detectability of events is affected by the spatial arrangement of the deployed monitoring array. If a limited number of seismometers are available for deployment, our analysis favours a patch array deployment geometry. We show that 12 seismometers deployed at a patch array geometry can have similar detection and localization capability as a large rectangular array of more than 100 seismometers but at a much lower computational and deployment cost. [ABSTRACT FROM AUTHOR]

The food industry is one of the world's largest contributors to carbon emissions, due to energy consumption throughout the food life cycle. This paper is focused on the residential consumption phase of the food life cycle assessment (LCA), i.e., energy consumption during home cooking. Specifically, while much effort has been placed on improving appliance energy efficiency, appliance models used in various applications, including the food LCA, are not updated regularly. This process is hindered by the fact that the cooking appliance models are either very cumbersome, requiring knowledge of parameters which are difficult to obtain or dependent on manufacturers' data which do not always reflect variable cooking behaviour of the general public. This paper proposes a methodology for generating accurate appliance models from energy consumption data, obtained by smart meters that are becoming widely available worldwide, without detailed knowledge of additional parameters such as food being prepared, mass of food, etc. Furthermore, the proposed models, due to the nature of smart meter data, are built incorporating actual usage patterns reflecting specific cooking practice. We validate our results from large, geographically spread energy datasets and demonstrate, as a case study, the impact of up-to-date models in the consumption phase of food LCA. [ABSTRACT FROM AUTHOR]

The authors propose two practical power- and bandwidth-efficient systems based on amplify-and-forward and decode-and-forward schemes to address the problem of information exchange via a relay. The key idea is to channel encode each source's message by using a high-performance non-binary turbo code based on partial unit memory codes to enhance the bit-error-rate performance, then reduce the energy consumption and increase spectrum efficiency by using network coding (NC) to combine individual nodes' messages at the relay before forwarding to the destination. Two simple and low complexity physical layer NC schemes are proposed based on combinations of received source messages at the relay. The authors also present the theoretical limits and numerical analysis of the proposed schemes. Simulation results under additive white Gaussian noise confirm that the proposed schemes achieve significant bandwidth savings and fewer transmissions over the benchmark systems which do not resort to NC. Theoretical limits for capacity and signal-to-noise ratio behaviour for the proposed schemes are derived. This study also proposes a cooperative strategy that is useful when insufficient combined messages are received at a node to recover the desired source messages, thus enabling the system to retrieve all packets with significantly fewer retransmission request messages. [ABSTRACT FROM AUTHOR]

The availability of smart metering and smart appliances enables detecting and characterising appliance use in a household, quantifying energy savings through efficient appliance use and predicting appliance-specific demand from load measurements is possible. With growing electric kettle ownership and usage, lack of any efficiency labelling guidelines for the kettle, slow technological progress in improving kettle efficiency relative to other domestic appliances, and current consumer attitudes, urgent investigation into consumer kettle usage patterns is warranted. From an efficiency point of view, little can be done about the kettle, which is more efficient than other methods of heating water such as the stove top kettle. However, since a majority households use the kettle inefficiently by overfilling, in order to meet energy targets, it is imperative to quantify inefficient usage and predict demand. For the purposes of scalability, we propose tools that depend only on load measurement data for quantifying and visualising kettle usage and energy consumption, assessing energy wastage through overfilling via our proposed electric kettle model, and predicting kettle-specific demand, from which we can estimate potential energy savings in a household and across a housing stock. This is demonstrated using data from a longitudinal study across a sample of 14 UK households for a two-year period. 1 The open-access REFIT Electrical Load Measurements dataset of the 14 UK households used in this study can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4 . 1 [ABSTRACT FROM AUTHOR]