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In this study, sulfonated poly(arylene ether sulfone) (SPAES) and sulfonated poly(arylene ether ketone) (SPAEK) were randomly synthesized, employing a presulfonation process. This presulfonation process resulted in a more controlled and reproducible sulfonation level. The respective polymers were prepared using 2,2‐Bis(4‐hydroxyphenyl) propane at 50% molar ratio, which also provided some membrane elasticity. The resulting polymers, each had 25% of the block containing the sulfonic domains (SPAES A 25 and SPAEK A 25). Better conductive membranes were achieved for the random sulfone polymers than for the random ketone polymers, with values, respectively, of 0.24 and 0.07 S cm−1 at 80°C. The lower proton conductivity from the ketone‐based polymer was compensated with very low methanol permeability (0.25 × 10−6 cm2 s−1) and outstanding oxidative stability. The selectivity of both polymer membranes exceeded the reported values for the state‐of‐the‐art Nafion® 117 and other commercially available options. Both polymer membranes, with their unique combination of ionic domains, elastomeric blocks, and resulting morphology, could be viable candidates for fuel cell applications. [ABSTRACT FROM AUTHOR]

This study discusses the synthesis of sulfonated amine block copolymers and the effect of multiple ionic domains and counter‐ion substitution on polymeric membranes' morphology and transport properties for chemical protective clothing (CPC) applications. The monomers 2‐(tert‐butylamino) ethyl methacrylate, 2‐ethoxy ethyl methacrylate, and styrene were used to prepare the block copolymers by atom transfer radical polymerization (ATRP). The copolymers were then sulfonated by chemical grafting with pendants sulfobutyl groups onto the polymer structure. Properties of the resulting membranes were evaluated as a function of block composition, incorporation of sulfonic groups, and counter‐ion substitution. Blended membranes with sulfonated poly(styrene‐isobutylene‐styrene) (SIBS) were also studied. A series of materials characterization techniques (e.g., Fourier‐transform infrared spectroscopy [FT‐IR], thermogravimetric analysis [TGA], atomic force microscopy [AFM], Small‐angle X‐ray scattering [SAXS]/wide‐angle X‐ray scattering [WAXS]) were performed to describe the changes to the membranes. The results indicate that synthesized copolymers lack phase segregation mainly because of a low sulfonation on the central amine block, leading the material to have a low Water/DMMP selectivity. As the sulfonated copolymers were blended with SIBS 75, the breathability of the membranes were enhanced 1.5 times, and almost all candidates achieved the water vapor transport rate limit of 1500 g m−2 day−1. Finally, the better‐found candidates for DMMP CPC were Mg2+ membranes, achieving selectivity between 20 and 90 range. [ABSTRACT FROM AUTHOR]

Business analytics refers to the application of sophisticated tools to obtain valuable information from a large dataset that is generated by a company. Among these tools, fuzzy optimisation stands out because it helps decision-makers to solve optimisation problems considering the uncertainty that commonly occurs in application domains. This paper presents a bibliometric analysis following the PRISMA statement on the Dimensions database to obtain publications related to fuzzy optimisation applied to business domains. The purpose of this analysis is to gather useful information that can help researchers in this area. A total of 2,983 publications were analysed using VOSviewer to identify the trend in the number of publications per year, relationships in terms in both the title and abstract of these publications, most influential publications, and relationships among journals, authors, and institutions. [ABSTRACT FROM AUTHOR]

In this study, two series of poly(arylene ether sulfone)s (PAES) with hydrophobic blocks made of 4,4′‐dihydroxyphenyl (BP) and 2,2‐bis(4‐hydroxyphenyl) propane (BPA) were synthesized. The copolymers were prepared by a coupling reaction between decafluorobiphenyl (DFBP) end‐capped nonsulfonated block (PAES A or B) and sulfonated block (SPAES B). The reactive behavior of DFBP facilitates the low‐temperature coupling reaction (below 105°C). The final copolymers were blended at distinct weight percent ratios (0, 20, 30, and 40) with poly(styrene‐b‐isobutylene‐b‐styrene) (SIBS) at a sulfonation level of 88% to obtain strong and flexible membranes. Morphological, thermal stability, and conduction properties of the resulting membranes were measured and reported here. Results suggest that phase segregation between hydrophobic and hydrophilic domains occurs below 1 meq. g−1 ion exchange capacity. These membranes have low water absorption and high thermal stability. Incorporating SIBS 88 into the blend added elastomeric behavior enhancing transport properties and additional ionic domains, achieving proton conductivities of 0.21 S cm−1 for BPA‐based membranes at 50°C (40% SIBS 88). The selectivity of the studied membranes surpasses Nafion® 117, making them strong candidates for proton exchange membranes in direct methanol fuel cell applications. [ABSTRACT FROM AUTHOR]

Today it is crucial to have up-to-date information for companies to be more competitive in this business world. There are applications based on speech recognition that allows access to data stored in databases. However, the proper functioning of these applications lies in good pronunciation, a skill that most people do not have. In this paper, the architecture of an English mispronunciation detection module integrated into a chatbot is proposed. It allows users to enter the audio of the phrases in which they want to evaluate their pronunciation. The output is the mispronounced words, thus helping the user to practice their English language pronunciation. The proposed architecture consists of an Automatic Speech Recognizer (ASR) model based on a Transformer network that converts the audio signal to text and an algorithm for string alignment that identifies mispronounced words using the Levenshtein distance. The Transformer network was trained using the LibriSpeech and L2-ARTIC datasets. The module was evaluated using the Accuracy metrics, reaching 90%, and the Character Error Rate metric, reaching 9.5%. Additionally, its performance was evaluated on a group of real users, showing promising results. [ABSTRACT FROM AUTHOR]

'El Diario de Juárez' is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish. [ABSTRACT FROM AUTHOR]

One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust. [ABSTRACT FROM AUTHOR]

Root border cells separate from plant root tips and disperse into the soil environment. In most species, each root tip can produce thousands of metabolically active cells daily, with specialized patterns of gene expression. Their function has been an enduring mystery. Recent studies suggest that border cells operate in a manner similar to mammalian neutrophils: Both cell types export a complex of extracellular DNA (exDNA) and antimicrobial proteins that neutralize threats by trapping pathogens and thereby preventing invasion of host tissues. Extracellular DNases (exDNases) of pathogens promote virulence and systemic spread of the microbes. In plants, adding DNase I to root tips eliminates border cell extracellular traps and abolishes root tip resistance to infection. Mutation of genes encoding exDNase activity in plant-pathogenic bacteria ( Ralstonia solanacearum) and fungi ( Cochliobolus heterostrophus) results in reduced virulence. The study of exDNase activities in plant pathogens may yield new targets for disease control. [ABSTRACT FROM AUTHOR]

The selection of a public project portfolio among a set of good portfolios directly impacts public organizations in a decisive manner. This task depends on the criteria of the decision maker and is especially hard when the solution is evaluated on many objectives. This combinational optimization problem has been addressed by multicriteria optimization algorithms which do not generate a single solution, but instead they generate a set of good solutions in the Pareto frontier. In this paper we propose to aid the recommendation of public projects with the utilization of simplified decision rules to explain the construction of the recommended project portfolio. Due to most decision problems can be represented in decision tables, we can form a decision table -- in portfolio selection problem -- with rules defined by the features of the projects (condition attributes) and the decision of support (the decision attribute). To facilitate the recommendation process, we simplify the decision rules via a hybrid rough set-based method that combines a genetic algorithm with an exact method. Less decision rules help the decision maker to faster analyze why some projects are accepted or rejected in a particular portfolio and --by this mean-- get more certainty about his/her decision. With the proposed approach, there is a significant reduction of the attributes of the decision rules with a high accuracy of classification, which was proven on a set of the UCI repository for machine learning test. [ABSTRACT FROM AUTHOR]

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM's preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier. [ABSTRACT FROM AUTHOR]

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named "genetic algorithm for scheduling optimization" (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use. [ABSTRACT FROM AUTHOR]

Reduced water resources are of increasingly urgent global concern. One potential strategy to address the crisis is the use of drought tolerant crops in agriculture. Barley varieties developed for reduced irrigation ("Solum" and "Solar") use significantly less water than conventional varieties ("Cochise" and "Kopious"). The underlying mechanism of this drought tolerance is unknown but root structure and function play a key role in plant water uptake. In this study, an empirical survey compared early root development between drought tolerant and conventional varieties. Traits associated with root meristem-regulated cell division including rate of seed germination, border cell number and root cap mucilage production, and root hair emergence were quantified during root emergence. Preliminary results revealed that drought tolerant varieties exhibited faster seed germination and root hair production than conventional varieties. Border cell number and mucilage production in the drought tolerant varieties also were higher than in the conventional variety "Kopious," but lower than in "Cochise". Each trait, if found to be linked to the observed drought tolerance, could yield a simple, rapid, and inexpensive tool to screen for new crop varieties. Further detailed studies are needed. [ABSTRACT FROM AUTHOR]

Premise of the Study: Lead (Pb) is a contaminant whose removal from soil remains a challenge. In a previous study, border cells released from root tips were found to trap Pb, alter its chemistry, and prevent root uptake. Rhodizonic acid (RA) is a forensic tool used to reveal gunshot residue, and also to detect Pb within plant tissues. Here we report preliminary observations to assess the potential application of RA in exploring the dynamics of Pb accumulation at the root tip surface. Methods and Results: Corn root tips were immersed in Pb solution, stained with RA, and observed microscopically. Pb trapping by border cells was evident within minutes. The role of extracellular DNA was revealed when addition of nucleases resulted in dispersal of RA‐stained Pb particles. Conclusions: RA is an efficient tool to monitor Pb–root interactions. Trapping by border cells may control Pb levels and chemistry at the root tip surface. Understanding how plants influence Pb distribution in soil may facilitate its remediation. [ABSTRACT FROM AUTHOR]

Histone-linked extracellular DNA (exDNA) is a component of neutrophil extracellular traps (NETs). NETs have been shown to play a role in immune response to bacteria, fungi, viruses, and protozoan parasites. Mutation of genes encoding group A Streptococcus extracellular DNases (exDNases) results in reduced virulence in animals, a finding that implies that exDNases are deployed as counter defense against host DNA-containing NETs. Is the exDNA/exDNase mechanism also relevant to plants and their pathogens? It has been demonstrated previously that exDNA is a component of a matrix secreted from plant root caps and that plants also carry out an extracellular trapping process. Treatment with DNase I destroys root tip resistance to infection by fungi, the most abundant plant pathogens. We show that the absence of a single gene encoding a candidate exDNase results in significantly reduced virulence of a fungal plant pathogen to its host on leaves, the known infection site, and on roots. Mg 2+ -dependent exDNase activity was demonstrated in fungal culture filtrates and induced when host leaf material was present. It is speculated that the enzyme functions to degrade plant-secreted DNA, a component of a complex matrix akin to neutrophil extracellular traps of animals. IMPORTANCE We document that the absence of a single gene encoding a DNase in a fungal plant pathogen results in significantly reduced virulence to a plant host. We compared a wild-type strain of the maize pathogen Cochliobolus heterostrophus and an isogenic mutant lacking a candidate secreted DNase-encoding gene and demonstrated that the mutant is reduced in virulence on leaves and on roots. There are no previous reports of deletion of such a gene from either an animal or plant fungal pathogen accompanied by comparative assays of mutants and wild type for alterations in virulence. We observed DNase activity, in fungal culture filtrates, that is Mg 2+ dependent and induced when plant host leaf material is present. Our findings demonstrate not only that fungi use extracellular DNases (exDNases) for virulence, but also that the relevant molecules are deployed in above-ground leaves as well as below-ground plant tissues. Overall, these data provide support for a common defense/counter defense virulence mechanism used by animals, plants, and their fungal and bacterial pathogens and suggest that components of the mechanism might be novel targets for the control of plant disease., (Copyright © 2019 Park et al.)