Latest Articles

Open AccessArticle
Aerodynamic Testing of Helicopter Side Intake Retrofit Modifications
Aerospace 2017, 4(3), 33; doi:10.3390/aerospace4030033 (registering DOI) -
Abstract
Aerodynamic characteristics of helicopter engine side air intakes are investigated. The experimental data set is obtained by wind tunnel testing of a full-scale helicopter fuselage section model. For the simulation of realistic engine operation, engine mass flow rates are reproduced. Five-hole pressure probe
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Aerodynamic characteristics of helicopter engine side air intakes are investigated. The experimental data set is obtained by wind tunnel testing of a full-scale helicopter fuselage section model. For the simulation of realistic engine operation, engine mass flow rates are reproduced. Five-hole pressure probe data of the aerodynamic interface plane as well as local surface pressure distributions are compared for different geometries and operation conditions. Previous investigations indicate that unshielded, sideways-facing air intakes yield lowest distortion and highest total pressure levels in low speed conditions. In fast forward flight condition, however, forward-facing intake shapes are more beneficial. On this basis, the current research assesses the optimization potential of retrofit modifications such as a rear spoiler (small scoop) and an intake guide vane. Two optimal configurations of retrofit modifications are identified, combining benefits of the various basic intake and plenum chamber shapes. Full article
Open AccessFeature PaperArticle
Biosynthetic PCL-graft-Collagen Bulk Material for Tissue Engineering Applications
Materials 2017, 10(7), 693; doi:10.3390/ma10070693 (registering DOI) -
Abstract
Biosynthetic materials have emerged as one of the most exciting and productive fields in polymer chemistry due to their widespread adoption and potential applications in tissue engineering (TE) research. In this work, we report the synthesis of a poly(ε-caprolactone)-graft-collagen (PCL-g
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Biosynthetic materials have emerged as one of the most exciting and productive fields in polymer chemistry due to their widespread adoption and potential applications in tissue engineering (TE) research. In this work, we report the synthesis of a poly(ε-caprolactone)-graft-collagen (PCL-g-Coll) copolymer. We combine its good mechanical and biodegradable PCL properties with the great biological properties of type I collagen as a functional material for TE. PCL, previously dissolved in dimethylformamide/dichloromethane mixture, and reacted with collagen using carbodiimide coupling chemistry. The synthesised material was characterised physically, chemically and biologically, using pure PCL and PCL/Coll blend samples as control. Infrared spectroscopy evidenced the presence of amide I and II peaks for the conjugated material. Similarly, XPS evidenced the presence of C–N and N–C=O bonds (8.96 ± 2.02% and 8.52 ± 0.63%; respectively) for PCL-g-Coll. Static contact angles showed a slight decrease in the conjugated sample. However, good biocompatibility and metabolic activity was obtained on PCL-g-Coll films compared to PCL and blend controls. After 3 days of culture, fibroblasts exhibited a spindle-like morphology, spreading homogeneously along the PCL-g-Coll film surface. We have engineered a functional biosynthetic polymer that can be processed by electrospinning. Full article
Open AccessArticle
Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
Appl. Sci. 2017, 7(7), 651; doi:10.3390/app7070651 (registering DOI) -
Abstract
Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia
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Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia. Full article
Open AccessArticle
Effect of Sowing Method and Weed Control on the Performance of Maize (Zea mays L.) Intercropped with Climbing Beans (Phaseolus vulgaris L.)
Agriculture 2017, 7(7), 51; doi:10.3390/agriculture7070051 (registering DOI) -
Abstract
Maize is grown on a large area in Germany and there is a growing concern in society about negative environmental effects related to this. The objective of the study was to test the performance of mixtures of maize and climbing beans as an
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Maize is grown on a large area in Germany and there is a growing concern in society about negative environmental effects related to this. The objective of the study was to test the performance of mixtures of maize and climbing beans as an alternative to monocropped maize under different site and management conditions. The effects of sowing density of maize and beans as well as the sowing time of beans on total dry matter (DM) yield and bean DM contribution to the total yield were investigated. Further, various mechanical and chemical weed control methods were tested and the resulting total DM yield was compared with that of a reference treatment (manual weeding). Hardly any consistent yield difference between maize/bean mixtures and monocropped maize occurred. The proportion of beans varied over a wide range among sites and was consistently higher when beans were sown at an early growth stage of maize. Mixtures did not suppress weeds efficiently and at two of the three sites their yield clearly declined with increasing weed coverage in the mixtures. A weed coverage of up to circa 10% may be tolerated, as the corresponding yield reduction is less than 1 t ha−1. Considering the additional effort (i.e., two sowings, high costs for bean seeds, complicated weed control) in managing such mixtures, it can be concluded that maize/bean mixtures can currently hardly be recommended as an alternative to monocropped maize for feedstock production. Full article
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Open AccessFeature PaperReview
Trends in Microextraction-Based Methods for the Determination of Sulfonamides in Milk
Separations 2017, 4(3), 23; doi:10.3390/separations4030023 (registering DOI) -
Abstract
Sulfonamides (SAs) represent a significant category of pharmaceutical compounds due to their effective antimicrobial characteristics. SAs were the first antibiotics to be used in clinical medicine to treat a majority of diseases, since the 1900s. In the dairy farming industry, sulfa drugs are
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Sulfonamides (SAs) represent a significant category of pharmaceutical compounds due to their effective antimicrobial characteristics. SAs were the first antibiotics to be used in clinical medicine to treat a majority of diseases, since the 1900s. In the dairy farming industry, sulfa drugs are administered to prevent infection, in several countries. This increases the possibility that residual drugs could pass through milk consumption even at low levels. These traces of SAs will be detected and quantified in milk. Therefore, microextraction techniques must be developed to quantify antibiotic residues, taking into consideration the terms of Green Analytical Chemistry as well. Full article
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Open AccessFeature PaperArticle
Highly Efficient 3rd Generation Multi-Junction Solar Cells Using Silicon Heterojunction and Perovskite Tandem: Prospective Life Cycle Environmental Impacts
Energies 2017, 10(7), 841; doi:10.3390/en10070841 (registering DOI) -
Abstract
In this study, the environmental impacts of monolithic silicon heterojunction organometallic perovskite tandem cells (SHJ-PSC) and single junction organometallic perovskite solar cells (PSC) are compared with the impacts of crystalline silicon based solar cells using a prospective life cycle assessment with a time
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In this study, the environmental impacts of monolithic silicon heterojunction organometallic perovskite tandem cells (SHJ-PSC) and single junction organometallic perovskite solar cells (PSC) are compared with the impacts of crystalline silicon based solar cells using a prospective life cycle assessment with a time horizon of 2025. This approach provides a result range depending on key parameters like efficiency, wafer thickness, kerf loss, lifetime, and degradation, which are appropriate for the comparison of these different solar cell types with different maturity levels. The life cycle environmental impacts of SHJ-PSC and PSC solar cells are similar or lower compared to conventional crystalline silicon solar cells, given comparable lifetimes, with the exception of mineral and fossil resource depletion. A PSC single-junction cell with 20% efficiency has to exceed a lifetime of 24 years with less than 3% degradation per year in order to be competitive with the crystalline silicon single-junction cells. If the installed PV capacity has to be maximised with only limited surface area available, the SHJ-PSC tandem is preferable to the PSC single-junction because their environmental impacts are similar, but the surface area requirement of SHJ-PSC tandems is only 70% or lower compared to PSC single-junction cells. The SHJ-PSC and PSC cells have to be embedded in proper encapsulation to maximise the stability of the PSC layer as well as handled and disposed of correctly to minimise the potential toxicity impacts of the heavy metals used in the PSC layer. Full article
Open AccessFeature PaperArticle
Characterisation of the Chemical Composition and Structural Features of Novel Antimicrobial Nanoparticles
Nanomaterials 2017, 7(7), 152; doi:10.3390/nano7070152 -
Abstract
Three antimicrobial nanoparticle types (AMNP0, AMNP1, and AMNP2) produced using the TesimaTM thermal plasma technology were investigated and their compositions were determined using a combination of analytical methods. Scanning electron micrographs provided the morphology of these particles with observed sizes ranging from
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Three antimicrobial nanoparticle types (AMNP0, AMNP1, and AMNP2) produced using the TesimaTM thermal plasma technology were investigated and their compositions were determined using a combination of analytical methods. Scanning electron micrographs provided the morphology of these particles with observed sizes ranging from 10 to 50 nm, whilst FTIR spectra confirmed the absence of polar bonds and organic impurities, and strong Raman active vibrational bands at ca. 1604 and 1311 cm−1 ascribed to C–C vibrational motions were observed. Carbon signals that resonated at δC 126 ppm in the solid state NMR spectra confirmed that sp2 hybridised carbons were present in high concentration in two of the nanoparticle types (AMNP1 and AMNP2). X-ray powder diffraction suggested that AMNP0 contains single phase Tungsten carbide (WC) in a high state of purity and multiple phases of WC/WC1-x were identified in both AMNP1 and AMNP2. Finally, X-ray photoelectron spectral (XPS) analyses revealed and quantified the elemental ratios in these composite formulations. Full article
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Open AccessArticle
Nonlinear Spectral Unmixing for the Characterisation of Volcanic Surface Deposit and Airborne Plumes from Remote Sensing Imagery
Geosciences 2017, 7(3), 46; doi:10.3390/geosciences7030046 -
Abstract
In image processing, it is commonly assumed that the model ruling spectral mixture in a given hyperspectral pixel is linear. However, in many real life cases, the different objects and materials determining the observed spectral signatures overlap in the same scene, resulting in
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In image processing, it is commonly assumed that the model ruling spectral mixture in a given hyperspectral pixel is linear. However, in many real life cases, the different objects and materials determining the observed spectral signatures overlap in the same scene, resulting in nonlinear mixture. This is particularly evident in volcanoes-related imagery, where both airborne plumes of effluents and surface deposit of volcanic ejecta can be mixed in the same observation line of sight. To tackle this intrinsic complexity, in this paper, we perform a pilot test using Nonlinear Principal Component Analysis (NLPCA) as a nonlinear transformation, that projects a hyperspectral image onto a reduced-dimensionality feature space. The use of NLPCA is twofold: (1) it is used to reduce the dimensionality of the original spectral data and (2) it performs a linearization of the information, thus allowing the effective use of successive linear approaches for spectral unmixing. The proposed method has been tested on two different hyperspectral datasets, dealing with active volcanoes at the time of the observation. The dimensionality of the spectroscopic problem is reduced of up to 95% (ratio of the elements of compressed nonlinear vectors and initial spectral inputs), by the use of NLPCA. The selective use of an atmospheric correction pre-processing is applied, demonstrating how individual plume and volcanic surface deposit components can be discriminated, paving the way to future application of this method. Full article
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Open AccessFeature PaperArticle
Simulation versus Optimisation: Theoretical Positions in Energy System Modelling
Energies 2017, 10(7), 840; doi:10.3390/en10070840 -
Abstract
In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems. Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems
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In recent years, several tools and models have been developed and used for the design and analysis of future national energy systems. Many of these models focus on the integration of various renewable energy resources and the transformation of existing fossil-based energy systems into future sustainable energy systems. The models are diverse and often end up with different results and recommendations. This paper analyses this diversity of models and their implicit or explicit theoretical backgrounds. In particular, two archetypes are defined and compared. On the one hand, the prescriptive investment optimisation or optimal solutions approach. On the other hand the analytical simulation or alternatives assessment approach. Awareness of the dissimilar theoretical assumption behind the models clarifies differences between the models, explains dissimilarities in results, and provides a theoretical and methodological foundation for understanding and interpreting results from the two archetypes. Full article
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