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Open AccessArticle
Analyzing the Export Performance of the Horticultural Sub-Sector in Ethiopia: ARDL Bound Test Cointegration Analysis
Horticulturae 2018, 4(4), 34; https://doi.org/10.3390/horticulturae4040034 (registering DOI) -
Abstract
High dependency on traditional primary agricultural commodities and recurrent world market price fluctuations had exposed Ethiopia to foreign earnings instability. To reduce the high dependence on primary agricultural commodities and the associated vulnerability of negative price declines, diversification of trade from primary agricultural
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High dependency on traditional primary agricultural commodities and recurrent world market price fluctuations had exposed Ethiopia to foreign earnings instability. To reduce the high dependence on primary agricultural commodities and the associated vulnerability of negative price declines, diversification of trade from primary agricultural commodities into high-value horticultural commodities has attracted the attention of policy makers. The developments made in this area have brought the sector to the position of fifth largest foreign revenue generator for the country. However, given the comparative advantage in marketing and the potential to achieve trade gains that the country possesses, the benefit from the horticultural sub-sector is far below its potential. In this regard, knowledge of the determinants of the industry’s development is very important. So far, no attempt was made to examine factors influencing the export performance of the sector, taking the long period performance of the sector into consideration. Consequently, this study was proposed to examine the factors that have influenced the horticultural export performance of Ethiopia for the period from 1985–2016. Secondary data collected from National Bank of Ethiopia, Ethiopia Horticulture Producer Exporter Association, Ministry of Agriculture of Ethiopia, FAOSTAT, UNCTAD, and the World Bank were used in this study. The short-run and long-run relationships among the series were investigated using the autoregressive-distributed lag (ARDL) bound test cointegration approach. The model result of the Error Correction Model (ECM (-1)) was revealed as negative and significant, whereby it confirmed the existence of cointegration among the series. Its coefficient value was 0.472, which showed 47% of the adjustment will be made in the first year and it will return to its long-run equilibrium after 2.12 years. The model results also showed that the real effective exchange rate, the real GDP of Ethiopia, foreign direct investment (FDI), prices, and the structural break had significantly influenced the horticultural export performance both in the short-run and the long-run. Foreign GDP and real interest rates were revealed significant only in the long-run. Finally, important policy measures deemed to improve the horticultural export performance of Ethiopia were recommended. Full article
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Open AccessArticle
The Characteristics of Intrinsic Fluorescence of Type I Collagen Influenced by Collagenase I
Appl. Sci. 2018, 8(10), 1947; https://doi.org/10.3390/app8101947 (registering DOI) -
Abstract
The triple helix structure of collagen can be degraded by collagenase. In this study, we explored how the intrinsic fluorescence of type I collagen was influenced by collagenase I. We found that tyrosine was the main factor that could successfully excite the collagen
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The triple helix structure of collagen can be degraded by collagenase. In this study, we explored how the intrinsic fluorescence of type I collagen was influenced by collagenase I. We found that tyrosine was the main factor that could successfully excite the collagen fluorescence. Initially, self-assembly behavior of collagen resulted in a large amount of tyrosine wrapped with collagen, which decreased the fluorescence intensity of type I collagen. After collagenase cleavage, some wrapped-tyrosine could be exposed and thereby the intrinsic fluorescence intensity of collagen increased. By observation and analysis, the influence of collagenase to intrinsic fluorescence of collagen was investigated and elaborated. Furthermore, collagenase cleavage to the special triple helix structure of collagen would result in a slight improvement of collagen thermostability, which was explained by the increasing amount of terminal peptides. These results are helpful and effective for reaction mechanism research related to collagen, which can be observed by fluorescent technology. Meantime, the reaction behaviors of both collagenase and collagenolytic proteases can also be analyzed by fluorescent technology. In conclusion, this research provides a foundation for the further investigation of collagen reactions in different areas, such as medicine, nutrition, food and agriculture. Full article
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Open AccessFeature PaperReview
Quasi-Lie Brackets and the Breaking of Time-Translation Symmetry for Quantum Systems Embedded in Classical Baths
Symmetry 2018, 10(10), 518; https://doi.org/10.3390/sym10100518 (registering DOI) -
Abstract
Many open quantum systems encountered in both natural and synthetic situations are embedded in classical-like baths. Often, the bath degrees of freedom may be represented in terms of canonically conjugate coordinates, but in some cases they may require a non-canonical or non-Hamiltonian representation.
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Many open quantum systems encountered in both natural and synthetic situations are embedded in classical-like baths. Often, the bath degrees of freedom may be represented in terms of canonically conjugate coordinates, but in some cases they may require a non-canonical or non-Hamiltonian representation. Herein, we review an approach to the dynamics and statistical mechanics of quantum subsystems embedded in either non-canonical or non-Hamiltonian classical-like baths which is based on operator-valued quasi-probability functions. These functions typically evolve through the action of quasi-Lie brackets and their associated Quantum-Classical Liouville Equations, or through quasi-Lie brackets augmented by dissipative terms. Quasi-Lie brackets possess the unique feature that, while conserving the energy (which the Noether theorem links to time-translation symmetry), they violate the time-translation symmetry of their algebra. This fact can be heuristically understood in terms of the dynamics of the open quantum subsystem. We then describe an example in which a quantum subsystem is embedded in a bath of classical spins, which are described by non-canonical coordinates. In this case, it has been shown that an off-diagonal open-bath geometric phase enters into the propagation of the quantum-classical dynamics. Next, we discuss how non-Hamiltonian dynamics may be employed to generate the constant-temperature evolution of phase space degrees of freedom coupled to the quantum subsystem. Constant-temperature dynamics may be generated by either a classical Langevin stochastic process or a Nosé–Hoover deterministic thermostat. These two approaches are not equivalent but have different advantages and drawbacks. In all cases, the calculation of the operator-valued quasi-probability function allows one to compute time-dependent statistical averages of observables. This may be accomplished in practice using a hybrid Molecular Dynamics/Monte Carlo algorithms, which we outline herein. Full article
Open AccessFeature PaperArticle
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks
Remote Sens. 2018, 10(10), 1649; https://doi.org/10.3390/rs10101649 (registering DOI) -
Abstract
Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data
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Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. First, extinction profiles are applied to both data sources in order to extract spatial and elevation features from hyperspectral and LiDAR-derived data, respectively. Second, a three-stream CNN is designed to extract informative spectral, spatial, and elevation features individually from both available sources. The combination of extinction profiles and CNN features enables us to jointly benefit from low-level and high-level features to improve classification performance. To fuse the heterogeneous spectral, spatial, and elevation features extracted by CNN, instead of a simple stacking strategy, a multi-sensor composite kernels (MCK) scheme is designed. This scheme helps us to achieve higher spectral, spatial, and elevation separability of the extracted features and effectively perform multi-sensor data fusion in kernel space. In this context, a support vector machine and extreme learning machine with their composite kernels version are employed to produce the final classification result. The proposed framework is carried out on two widely used data sets with different characteristics: an urban data set captured over Houston, USA, and a rural data set captured over Trento, Italy. The proposed framework yields the highest OA of 92.57% and 97.91% for Houston and Trento data sets. Experimental results confirm that the proposed fusion framework can produce competitive results in both urban and rural areas in terms of classification accuracy, and significantly mitigate the salt and pepper noise in classification maps. Full article
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Open AccessArticle
Thermal Degradation Characteristic and Flame Retardancy of Polylactide-Based Nanobiocomposites
Molecules 2018, 23(10), 2648; https://doi.org/10.3390/molecules23102648 (registering DOI) -
Abstract
Polylactide (PLA) is one of the most widely used organic bio-degradable polymers. However, it has poor flame retardancy characteristics. To address this disadvantage, we performed melt-blending of PLA with intumescent flame retardants (IFRs; melamine phosphate and pentaerythritol) in the presence of organically modified
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Polylactide (PLA) is one of the most widely used organic bio-degradable polymers. However, it has poor flame retardancy characteristics. To address this disadvantage, we performed melt-blending of PLA with intumescent flame retardants (IFRs; melamine phosphate and pentaerythritol) in the presence of organically modified montmorillonite (OMMT), which resulted in nanobiocomposites with excellent intumescent char formation and improved flame retardant characteristics. Triphenyl benzyl phosphonium (OMMT-1)- and tributyl hexadecyl phosphonium (OMMT-2)-modified MMTs were used in this study. Thermogravimetric analysis in combination with Fourier transform infrared spectroscopy showed that these nanocomposites release a smaller amount of toxic gases during thermal degradation than unmodified PLA. Melt-rheological behaviors supported the conclusions drawn from the cone calorimeter data and char structure of the various nanobiocomposites. Moreover, the characteristic of the surfactant used for the modification of MMT played a crucial role in controlling the fire properties of the composites. For example, the nanocomposite containing 5 wt.% OMMT-1 showed significantly improved fire properties with a 47% and 68% decrease in peak heat and total heat release rates, respectively, as compared with those of unmodified PLA. In summary, melt-blending of PLA, IFR, and OMMT has potential in the development of high-performance PLA-based sustainable materials. Full article
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Open AccessArticle
Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize
Toxins 2018, 10(10), 416; https://doi.org/10.3390/toxins10100416 (registering DOI) -
Abstract
The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory
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The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks. Full article
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Open AccessFeature PaperArticle
The Ecological Footprint Accounting of Products: When Larger Is Not Worse
Resources 2018, 7(4), 65; https://doi.org/10.3390/resources7040065 (registering DOI) -
Abstract
One of the main goals of any (sustainability) indicator should be the communication of a clear, unambiguous, and simplified message about the status of the analyzed system. The selected indicator is expected to declare explicitly how its numerical value depicts a situation, for
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One of the main goals of any (sustainability) indicator should be the communication of a clear, unambiguous, and simplified message about the status of the analyzed system. The selected indicator is expected to declare explicitly how its numerical value depicts a situation, for example, positive or negative, sustainable or unsustainable, especially when a comparison among similar or competitive systems is performed. This aspect should be a primary and discriminating issue when the selection of a set of opportune indicators is operated. The Ecological Footprint (EF) has become one of the most popular and widely used sustainability indicators. It is a resource accounting method with an area based metric in which the units of measure are global hectares or hectares with world average bio-productivity. Its main goal is to underline the link between the (un)sustainability level of a product, a system, an activity or a population life style, with the land demand for providing goods, energy, and ecological services needed to sustain that product, system, activity, or population. Therefore, the traditional rationale behind the message of EF is: the larger EF value, the larger environmental impact in terms of resources use, the lower position in the sustainability rank. The aim of this paper was to investigate if this rationale is everywhere opportune and unambiguous, or if sometimes its use requires paying a special attention. Then, a three-dimensional modification of the classical EF framework for the sustainability evaluation of a product has been proposed following a previous work by Niccolucci and co-authors (2009). Finally, the potentialities of the model have been tested by using a case study from the agricultural context. Full article
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