Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Pattern Analysis and Applications - Efficient high-dimensional analyses of hyperspectral datasets and their utilization within classification algorithms is a popular topic in the field of data...     

Effect of fortification with asparagus powder on the qualitative properties of processed cheese

The qualitative properties of processed cheese (PC) fortified with different levels of asparagus powder (AP) (0.5%, 1% and 1.5% wt/wt) were evaluated during storage. AP decreased the pH and lipolysis indexes and increased the phenolic content, antioxidant activity and proteolysis of the processed cheeses. AP made the structure of the cheese more elastic, increased the rigidity and decreased the spreadability compared with the control sample, which corresponded to the results obtained using dynamic oscillatory rheometry. The results showed that AP as a rich source of bioactive components could be used for the fortification of processed cheeses.     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

An Approach to Synthesize Thick α- and γ-Al2O3 Coatings by High-Speed Physical Vapor Deposition

Crystalline α- and γ-Al₂O₃ exhibit in many applications high wear resistance, chemical resistance, and hot hardness, making them interesting materials for production engineering. To synthesize α-Al₂O₃ with high coating thickness of s ≥ 10 μm, chemical vapor deposition at temperatures T > 1000 °C is well established. However, there are almost no studies dealing with the synthesis of thick α-Al₂O₃ by physical vapor deposition (PVD) at high temperatures T > 700 °C. High-temperature deposition of thick coatings can be realized by means of the dense hollow cathode plasma, combined with the transport function of the plasma gas in high-speed (HS) PVD. Herein, crystalline α- and γ-Al₂O₃ films are deposited on cemented carbides at substrate temperatures T _s ≈ 570 °C and T _s ≈ 780 °C by HS-PVD. These coatings exhibit a thickness up to s = 20 μm. Moreover, phase analysis presents α-phases in coatings synthesized at substrate temperature of T _s ≈ 780 °C with significant higher hardness than films by T _s ≈ 570 °C. These release the potential of HS-PVD to synthesize α-Al₂O₃ coatings with high thickness. Thereby, a higher thickness of these coatings is beneficial for the wear protection of turning and die casting tools.     

Influence of Drying Temperature and Harvesting Season on Phenolic Content and Antioxidant and Antiproliferative Activities of Olive (Olea europaea) Leaf Extracts

Interest in plant compounds has increased, given recent evidence regarding their role in human health due to their pleiotropic effects. For example, plant bioactive compounds present in food products, including polyphenols, are associated with preventive effects in various diseases, such as cancer or inflammation. Breast and colorectal cancers are among the most commonly diagnosed cancers globally. Although appreciable advances have been made in treatments, new therapeutic approaches are still needed. Thus, in this study, up to 28 olive leaf extracts were obtained during different seasons and using different drying temperatures. The influence of these conditions on total polyphenolic content (measured using Folin–Ciocalteu assays), antioxidant activity (using Trolox Equivalent Antioxidant Capacity and Ferric Reducing Ability of Plasma assays) and antiproliferative capacity (using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, MTT assays) was tested in breast and colorectal cancer cells. Increased phenolic composition and antioxidant and antiproliferative capacity are noted in the extracts obtained from leaves harvested in autumn, followed by summer, spring and winter. Regarding drying conditions, although there is not a general trend, conditions using the highest temperatures lead to the optimal phenolic content and antioxidant and antiproliferative activities in most cases. These results confirm previously published studies and provide evidence in support of the influence of both harvesting and drying conditions on the biological activity of olive leaf extracts.     

Preparation and study of a thermo-responsive membrane using binary liquid crystal mixtures of cholesteryl cetyl ether and cholesteryl oleyl carbonate

A facile method for the synthesis of thermotropic liquid crystalline cholesteryl cetyl ether (CCE) was carried out from cholesterol and cetyl alcohol using montmorillonite K-10 as an acid catalyst. The aim of this study was to investigate the use of liquid crystalline blends of CCE and cholesteryl oleyl carbonate (COC) with appropriate crystal to smectic phase temperature (T _c?Cs) just above body temperature as a temperature-modulated drug permeation system. Using 30/70?mol ratio of COC/CCE, a mixture of desirable phase transition temperature was obtained. The phase transition behavior of COC/CCE binary liquid crystalline mixture was established by differential scanning calorimetry and polarizing optical microsopy. The COC/CCE-embedded cellulose nitrate membrane was used by an in vitro drug penetration studies. Paracetamol and mesalazine were chosen as hydrophobic and hydrophilic drug models, respectively. Paracetamol permeability through the membrane was higher at temperatures above the phase transition of liquid crystal (LC) blends (39?°C) than its permeability below the phase transition temperature of liquid crystal blends (30?°C). The drug penetration through LC-embedded cellulose membrane was influenced by the pore size of the membrane and therefore the adsorbed amount of LC. There was no penetration of mesalazine through that membrane presumably, due to the differences in hydrophilicity of LC-embedded membrane and permeated drug.     

Experimental investigation on the mean drop size and drop size distribution in an L-shaped pulsed sieve-plate column

Vertical pulsed extraction columns cannot be employed in applications with height limitations. On the other hand, the horizontal extraction columns have low throughput, which affects their applicability in industrial applications. Therefore, there is a need to design a new type of extractors for such circumstances. In this paper, an experimental study on drop sizes has been implemented in a novel L-shaped pulsed sieve-plate extraction column in the absence and presence of mass transfer. Moreover, new correlations are developed for prediction of the mean drop size and size distribution using the log-normal probability density function.     

Core–shell structured Cu@Pt nanoparticles as effective electrocatalyst for ethanol oxidation in alkaline medium

Core–shell nanoparticles of Cu@Pt/C electrocatalysts were synthesized using various Pt:Cu atomic ratios with NaBH₄ as a reducing agent. The crystal structure and surface morphology were studied using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The incorporation of copper in Cu@Pt/C electrocatalysts was found to shift all Pt diffraction planes in the negative direction with expanding the crystal lattice dimensions. The electrocatalytic activity of various Cu@Pt/C electrocatalysts was investigated using cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy. Cu@Pt/C electrocatalysts containing Pt:Cu atomic ratios of 1:0.5 and 1:2 showed an enhanced electrochemical performance for ethanol oxidation when related to Pt/C.