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•DME production by methanol dehydration using Fe3O4 and CuO.•Fe3O4/γ–χ–Al2O3 reached selectivity to DME of 100%.•The Ea of the Fe3O4/γ–χ–Al2O3 was lower than the value of ...CuO/γ–χ–Al2O3.
Selective Fe3O4 and CuO catalysts supported on γ–χ–Al2O3 were successfully prepared using the facile controlled precipitation method. The catalysts Fe3O4/γ–χ–Al2O3 and CuO/γ–χ–Al2O3 showed excellent catalytic activity at low temperatures between 250 and 290 °C and 1 atm pressure. According to the experimental works, Fe3O4/γ–χ–Al2O3 showed better activity and selectivity at 250 °C, related to better dispersion of its active phase within the pores of γ–χ–Al2O3 and its lower activation energy, Ea = 46.4 kJ/mol. At 250 °C, Fe3O4/γ–χ–Al2O3 presented a higher theorical partial pressure of DME related to its better catalytic performance in the methanol dehydration reaction.
Online opinions/reviews contain a lot of sentiments and emotions that can be very useful, especially, for Internet suppliers which can know whether their services/products are meeting their ...customers' expectations or not. To detect these sentiments and emotions, most applications resort to lexicon-based approaches. The major issue here is that most well-known emotion lexicons have been developed for English language; nevertheless, in other languages such as Arabic, there are fewer available tools, and many times, the quality of them is poor.
The goal of this study is to compare the performance of two different types of algorithms, shallow machine learning-based and deep learning-based, when dealing with emotion detection in Arabic language. To improve the performance of the algorithms, two lexicons, which were originally developed in other languages and translated into Arabic language, have been used to add emotional features to different information models used to represent opinions. All approaches have been tested using the dataset SemEval 2018 Task 1: Affect in Tweets and the dataset LAMA+DINA. The semantic approaches outperform the classical algorithms, that is, the information provided by the lexicons clearly improves the results of the algorithms. Particularly, the BiLSTM algorithm outperforms the rest of the algorithms using word2vec. On the contrary to other languages, the best results were obtained using the NRC lexicon.
Polymeric nanogels have been widely used as drug carrier systems due to their high drug loading capacity and an improved solubility of hydrophobic drugs. Many studies have shown that hydrophobic ...antioxidants, such as CoQ10 or Resveratrol, might improve healthy skin and be an effective anti-aging treatment by repairing photo-damaged skin. This study was carried out to develop a delivery system based on bio-compatible and water-dispersible nanogels capable of encapsulating Coenzyme Q10 (CoQ10) and Resveratrol. The nanogels are based on Pluronic® P123 coated with polyvinylpyrrolidone plus polyethyleneglycol (P123/PVP-PEG). Their physicochemical properties and morphological characteristics were evaluated in detail, as well as in vitro drug release. These nanogels were able to encapsulate and release Resveratrol and CoQ10 better than pure drugs at skin temperature (32 °C), from 31 to 43 and from 25 to 40%, respectively. The systems exhibited nanometric dimensions and spherical shape shown in the SEM and HRTEM micrographs. The bacterial bioassays studies showed that loaded materials do not affect bacterial growth. In addition, aqueous polymeric dispersions were stable for at least 24 h. The results indicated that the prepared nanogels were water-dispersible, non-toxic in the first tests, and they could be potential candidates for hydrophobic antioxidant release.
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In this study, we synthesized and characterized pH-responsive Chitosan–AgCl-doped ZnO hybrid hydrogels and evaluated their potential for loading aquaculture bioactive compounds, and assessed their ...antimicrobial properties against a threatening pathogen associated with disease across a broad spectrum of warm water fish and invertebrates. Hydrogel characterization consisted of assessing morphology via SEM, composition via EDS, hydrogels’ network components interactions via FT-IR and pH response through swelling behavior determinations. The swelling characterization of the synthesized hydrogels demonstrated a pH-responsive behavior, showing that low pH values caused the hydrogel polymeric network to expand and capture more of the aqueous solution. These characteristics make the synthesized hydrogels suitable for the encapsulation and controlled release of drugs and bioactive compounds in aquaculture. Chitosan_ZnO hybrid hydrogels showed great antimicrobial activity against Vibrio harveyi, even better than that of loaded PB hydrogels. Here, we provide evidence for the potential capacity of Chitosan_ZnO hybrid hydrogels for the preventive and curative treatment of diseases that impact aquaculture animal health and prevent drug resistance by bacteria.
Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling ...the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.
The evaluation of the players’ performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel ...approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that best represent the handball player’s performance in a match and for setting their relevance weights. Our approach consists of a fuzzy model to aggregate expert judgments. This methodology overcomes some drawbacks of classical systems, including the definition of the relevance of each criteria using linguistic labels. A preliminary evaluation analyzes handball players’ performance indicators and their application to a short tournament. Considering the obtained results, we can conclude that the proposal is relevant and provides useful insights regarding player performance in different matches. The proposed methodology has also been compared with a basic plus-minus rating methodology. This comparison illustrates the feasibility of our approach. Results suggest that plus-minus rating is not the best solution to represent the performance of specialized players who only play when their team attack or defense. Our approach demonstrates being more appropriate for sports such as handball because it includes the valuation of a full set of positive actions in defense and attack.
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•PdO and Mn2O3 nanomaterials were prepared by wet impregnation.•Methanol dehydration using PdO and Mn2O3 /γ-χ-Al2O3 microagglomerates.•PdO/γ-χ-Al2O3 reached selectivity to DME of ...100%.•Effect of microagglomerates surface and size on methanol dehydration.
In this study, we synthesize microagglomerates of PdO and Mn2O3 supported over γ-χ-Al2O3 via the impregnation method, for their application in the methanol dehydration to dimethyl ether, under reaction conditions of 240 °C to 290 °C and 1 pressure atm. It was determined by XRD, TEM and XPS that when PdO (Pd2+) is present at 3% by weight over γ-χ-Al2O3, conversions of 57% and selectiveness of 100% towards dimethyl ether can be reached at 250 °C due to at a higher density of moderate acid sites (a moderate acidity difference of 0.5276 mmol/g with respect to Mn2O3/γ-χ-Al2O3). Additionally, our theoretical and experimental results between the correlation of the textural and morphological properties of the PdO/γ-χ-Al2O3 and Mn2O3/γ-χ-Al2O3 materials with the hydrodynamics and kinetics of the reaction, showed that when PdO agglomerates with a size of ∼79.2 µm are used, higher internal mass transfer rates predominate when compared to using Mn2O3 agglomerates of a larger size, ∼124 µm. Finally, the relative stability shown by the PdO/γ-χ-Al2O3 catalyst during 10 h of reaction was correlated with its low apparent activation energy (111 kJ/mol) and with its inactive phase change of PdO measured by XPS after the reaction.
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•Methanol dehydration using Fe2O3/γ–χ–Al2O3 nanocatalysts.•Fe2O3/γ–χ–Al2O3 reached selectivity to DME of 100%.•The Ea of the Fe2O3/γ–χ–Al2O3 was lower than the value of γ–χ–Al2O3.
In ...this work, the low cost synthesis of the Fe2O3/γ–χ–Al2O3 catalyst, carried out by the wet impregnation method, promoted a conversion in methanol dehydration by 46% (an increase of 5% with respect to γ–χ–Al2O3 support), and with a selectivity of 100% towards DME (at 250 °C and 1 atm pressure), due to a higher abundance in the density of moderate acidic sites generated by the synergistic metal-support interaction. The slight decrease in catalytic activity for the Fe2O3/γ–χ–Al2O3 system, compared to the γ–χ–Al2O3 from 260 °C, was linked to the effect of changes in the shape and size of the Fe2O3 nanoparticles. These particles went from semi-spherical to nano-needles at 290 °C reaction temperature. Finally, the great structural stability of Fe3+ measured by XPS, RAMAN spectroscopy and UV–Vis, and the low activation energy of the Fe2O3/γ–χ–Al2O3 material (102.66 kJ/mol), place the Fe2O3/γ–χ–Al2O3 catalyst as an excellent candidate for methanol dehydration, under conditions of 240 to 250 °C and methanol partial pressures between 9.8 and 7.8 kPa, respectively.
The hydrogenation of aromatic hydrocarbon compounds derived of hydrocarbon fuels is crucial to avoid the current environment pollution, harmful to human health. Also, the hydrogenated products can be ...used as chemical intermediates, additives, and in other chemical processes. The search of efficient and cost-effective catalysts for the hydrogenation of aromatic hydrocarbon compounds is a current challenge in the industrial and scientific community. In this study, trimetallic unsupported MMoW (M = Fe, Co, Ni, and Cu) sulfide catalysts were synthetized and tested in the liquid-phase biphenyl (BP) hydrogenation at 300 °C and 5.5 MPa of H2 pressure. The catalysts were characterized by XRD, SEM-EDX, TEM-SAED and adsorption–desorption of N2. The presence of NiS, CoSx, MoFe2S4, Cu9S5 crystalline sulfide phases together with Mo(W)S2 crystalline sulfide phase, as well as the crystallite size and the surface area, influenced the catalytic performance. Ni-based catalysts were the most active in the BP hydrogenation. A BP conversion value of ~99% of biphenyl and selectivity of 52% towards cyclohexylbenzene (CHB) and 47% towards bicyclohexyl were reached using NiMoWS catalyst. The presence of NiS crystals, stacked curve-shape Mo(W)S2 crystalline phases and larger surface area values increased the catalytic activity using NiMoWS catalysts.
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•MMoWS catalysts (M = Fe, Co, Ni, Cu) were tested in the biphenyl hydrogenation.•NiMoWS and CoMoWS improved the biphenyl conversion at higher atomic molar ratio.•Biphenyl was converted (~100%) using Ni(0.70)MoWS.•Ni(0.70)MoWS promoted the clean production of bicyclohexyl and cyclohexylbenzene.•NiS crystals and stacked curve-shape MoWS2 phases promoted high catalytic activity.
Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results ...produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.
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