91 research outputs found
End-Of-Use Fly Ash as an Effective Reinforcing Filler in Green Polymer Composites
No full textThe aim of this study is to use fly ash powder in an environmentally friendly matrix, in a novel way, addressing environmental and disposal problems. Fly ash/epoxy composites were prepared and studied varying the filler content. An investigation of structural and morphological characteristics was conducted using of X-ray diffraction patterns and scanning electron microscopy images, which revealed the successful fabrication of composites. Thermomechanical properties were studied via dynamic mechanical analysis and static mechanical tests. The composites exhibited an improved mechanical response. Broadband dielectric spectroscopy was used to investigate the dielectric response of the composite systems over the frequency range from 10−1 to 107 Hz and the temperature range from 30 to 160 °C. The analysis revealed the presence of three relaxation processes in the spectra of the tested systems. Interfacial polarization, the glass-to-rubber transition of the polymer matrix, and the rearrangement of polar side groups along the polymer chain are the processes that occur under a descending relaxation time. It was found that dielectric permittivity increases with filler content. Finally, the influence of filler content and the applied voltage under dc conditions was analyzed to determine the ability of the composites to store and retrieve electric energy. Fly ash improved the efficiency of the storing/retrieving energy of the composites
Real world, big data cost of pharmaceutical treatment for rheumatoid arthritis in Greece
No full textIntroduction Rheumatoid Arthritis (RA) is a highly prevalent autoimmune disease associated with joint inflammation and destruction. Treatment for RA, especially with biologic agents (biologics), improves patient functionality and quality of life and averts costly complications or disease progression. Cost of RA pharmaceutical treatment has rarely been reported on the basis of real-world, big data. This study reports on the real-world, big data RA pharmaceutical treatment cost in Greece. Methods The Business Intelligence database of the National Organization for Healthcare Services Provision (EOPYY) was used to identify and provide analytics on patients on treatment for RA. EOPYY is responsible for funding healthcare and pharmaceutical care services for approximately 95% of the population in the country. ICD-10 codes were applied to identify patients with RA and at least one reimbursed prescription between 1 June 2014 and 31 May 2015. Results 35,873 unique patients were recorded as undergoing treatment for RA. Total reimbursed treatment cost for the study period was €81,206,363.70, of which €52,732,142.18 (64.94%) was for treatment with biologics. Of that cost, €39,724,489.71 (48.32%) accounted for treatment with anti-TNFs and/or methotrexate/corticosteroids. Conclusion Real world, big data analysis confirms that the major driver of RA pharmaceutical cost is, as expected, the cost of treatment with biologics. It is critical to be able to match this cost to the treatment outcome it produces to ensure an optimal, no-waste, evidence-based allocation of healthcare resources to need. © 2019 Souliotis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Using big data to assess prescribing patterns in Greece: The case of chronic obstructive pulmonary disease
No full textIntroduction: Chronic Obstructive Pulmonary Disease (COPD) is one of the top leading causes of death and disability, and its management is focused on reducing risk factors, relieving symptoms, and preventing exacerbations. The study aim was to describe COPD prescribing patterns in Greece by using existing health administrative data for outpatients. Methods: This is a retrospective cross-sectional study based on prescriptions collected by the largest social insurance fund, during the first and last trimester of 2012. Selection criteria were the prescription of specific active substances and a COPD diagnosis. Extracted information included active substance, strength, pharmaceutical form and number of packages prescribed, diagnosis, time of dispensing, as well as insurees' age, gender, percentage of copayment and social security unique number. Statistical analysis included descriptive statistics and logistic regression. Results: 174,357 patients received medicines for COPD during the study period. Patients were almost equally distributed between male and female, and age above 55 years was strongly correlated with COPD. Most patients received a long-acting beta agonist plus inhaled corticosteroid combination (LABA +ICS), followed by long-acting muscarinic agonist (LAMA). 63% patients belonging in the 35-54 age received LABA+ICS. LAMA was prescribed more frequently among males and was strongly correlated with COPD. Conclusion: The study provides big data analysis of Greek COPD prescribing patterns. It highlights the need for appropriate COPD classification in primary care illustrating the role of electronic prescribing in ensuring appropriate prescribing. Moreover, it indicates possible gender differences in treatment response or disease severity, and the impact of statutory co-payments on prescribing. © 2016 Souliotis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Machine learning techniques for load monitoring of offshore wind turbines
No full textIn the race for cost reduction in the offshore wind industry, support structure optimization leading to weight reduction plays a prominent role. The fatigue limit state is often the driving consideration for support structure design. Monitoring the monopile loads can offer an accurate knowledge of its consumed and remaining fatigue lifetime, which in turn has the potential benefits of lifetime extension and feedback on current design practices among others. Since within an offshore wind farm not all turbines are subjected to the same loading, a turbine or cluster-specific internal load monitoring scheme is investigated using data-driven approaches and specifically feedforward artificial neural networks (ANNs) and linear regression (LR). The purpose of this thesis is to examine whether it is possible to accurately estimate the actual loading of the offshore wind turbine at the monopile mudline fatigue sensitive location by utilizing standard signals and/or sensor measurements and machine learning techniques instead of a structural model. Data simulated with the Siemens Gamesa Renewable Energy in-house aeroelastic code - Bonus Horizontal axis wind turbine Code (BHawC) – is used, including operation and idling fatigue load cases. Ten minute time and frequency-domain statistics of signals that are collected in turbines are used as inputs to the data-driven models, whereas 10-minute damage equivalent loads (DELs) of monopile moments are used as targets. By using statistics of rotor rotational velocity, electrical power output, blade pitch angle, hub wind speed and nacelle accelerations, a mean absolute error of DEL estimation smaller than 4% in both operation and idling load cases is achieved, under the condition that the training set properly reflects the entire variation in environmental conditions. Furthermore, errors average out over multiple 10-minute intervals, resulting in accurate long-term estimation with residual errors between -1% and +1%. The accuracy of the estimation can be further improved by including additional sensors; accelerometers placed at the tower bottom (TB) can help reduce ANN mean absolute error by up to 40% in operational load cases. In addition, if inclinometers are installed at TB they can allow 10-minute equivalent ranges of rotation signals at TB to be used as inputs to a LR scheme to accurately estimate DELs. When the 10-minute intervals are binned based on whether the mean wind speed is below, around, or above the turbine rated wind speed, this method gives mean absolute errors below 2.5%. As a result, TB inclinometers are recommended as extra sensors for load monitoring, provided that factors such as their accuracy level and durability compared to alternatives are considered. The data-driven models examined in this thesis show potential not only for online internal load monitoring, but also for fast estimation of past load histories using recorded data. Offshore and Dredging Engineerin
Modeling and Simulation of Solar Systems Employing Collectors with Colored Absorber
No full textTo avoid the monotony of the black colored flat plate solar
collectors we can use absorbers of blue, red-brown, green or other color. Because
of the lower collector absorptance these collectors have lower thermal efficiency
than that of the usual black type collectors, they are however of more interest to
architects for applications on traditional or modern buildings. In this paper
applications of solar collectors with colored absorbers are presented and analyzed
with respect to their performance, aiming to give guidelines for their wider use on
buildings. These systems are simulated with TRNSYS on an annual basis at two
different locations, Nicosia, Cyprus and Athens, Greece. The results show that the
energy output depends on the absorber darkness. For a medium value of the
coefficient of absorptance, the colored collectors give satisfactory results with
respect to the drop of the amount of collected energy, compared to collectors with
black absorbers. This implies the use of slightly larger collector aperture area to
have the same energy output as that of typical black colored collectors
Performance of solar systems employing collectors with colored absorber
No full textFlat plate solar collectors are of black appearance because of the color of the absorber, which is employed to maximize the absorption of solar spectrum. Generally, to avoid the monotony of the black color we can use collectors with absorbers of blue, red–brown, green or other color. These collectors are of lower thermal efficiency than that of the usual black type collectors, because of the lower collector absorptance, but they are of more interest to architects for applications on traditional or modern buildings. In this paper, applications of solar collectors with colored absorbers in a large hot water system suitable for multi-flat residential or office buildings, a house heating system, and an industrial process heat system are presented. The collectors are analyzed with respect to their performance and practical applications, aiming to give guidelines for their wider use on buildings. These systems are simulated on an annual basis at three different locations at different latitudes, Nicosia, Cyprus (35°), Athens, Greece (38°) and Madison, Wisconsin (43°). All simulations are carried out with TRNSYS. The results show that although the colored collectors present lower efficiency than the typical black type collectors, the difference in energy output depends on the absorber darkness. For a medium value of the coefficient of absorptance (α = 0.85), the colored collectors give satisfactory results regarding the drop of the amount of collected energy for the three locations (about 7–18%), compared to collectors with black absorbers (α = 0.95). This implies the use of proportionate larger collector aperture area to have the same energy output as that of typical black colored collectors. Additionally, the economic figures obtained for the systems investigated are very promising
Thermosiphonic Hybrid PV/T Solar Systems
No full textThermosiphon solar water heaters and Photovoltaic (PV) devices are well known solar systems that provide heat and electricity, respectively. In this work, these two systems are combined into a hybrid hotovoltaic/Thermal (PV/T) solar system which can simultaneously provide electricity and heat, thus achieving a higher conversion rate of the absorbed solar radiation than standard PV modules. When properly designed, PV/T systems can extract heat from PV modules which can be used to heat water or air. By doing so the operating temperature of PV modules is reduced, which is beneficial, as it keeps their electrical efficiency at a sufficient level. In this paper, the design considerations and experimental results of a thermosiphonic hybrid PV/T solar system are presented. The electrical and thermal energy output for a pc-Si PV/T module type under the climatic conditions of Patras are presented
Modelling of an ICS solar water heater using artificial neural networks and TRNSYS
No full textA study, in which a suitable artificial neural network (ANN) and TRNSYS are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype, is presented. Experimental data that have been collected from outdoor tests of an ICS solar water heater with cylindrical water storage tank inside a CPC reflector trough were used to train the ANN. The ANN is then used through the Excel interface (Type 62) in TRNSYS to model the annual performance of the system by running the model with the values of a typical meteorological year for Athens, Greece. In this way the specific capabilities of both approaches are combined, i.e., use of the radiation processing and modelling power of TRNSYS together with the “black box” modelling approach of ANNs. The details of the calculation steps of both methods that aim to perform an accurate prediction of the system performance are presented and it is shown that this new method can be used effectively for such predictions
ICS solar water heater study using artificial neural networks
No full textIn this paper we present a study in which a suitable Artificial Neural Network (ANN) and TRNSYS
are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype.
We use the experimental data that have been collected from outdoor tests of an ICS solar water heater
with cylindrical water storage tank inside a CPC reflector trough, to train the ANN. The ANN is then
used though the Excel interface (Type 62) in TRNSYS to model the annual performance of the system
by running the model with the values of a typical meteorological year for Athens, Greece. In this way
the specific capabilities of both approaches are combined, i.e., use of the radiation processing and
modelling power of TRNSYS together with the “black box” modelling approach of ANNs. We present
the details of the calculation steps of both methods that aim to the accurate prediction of the system
performance and we show that this new method can be used effectively for such prediction
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