Modulating drug release from poly(lactic-co-glycolic) acid microparticles by the addition of alginate and pectin

The aim of the present work is the characterization of PLGA microparticles including biopolymers for the controlled release of tilmicosin, a broad-spectrum antibiotic. Microparticles were prepared using the double-emulsion solvent evaporation technique. The effect of alginate and pectin incorporation over particle size and porosity, encapsulation efficiency (EE) and pH-responsive drug release was evaluated. Formulations presented a mean particle size of 5.5 μm approximately and a drug EE ranged from 22%–57%. PLGA-Alginate particles showed an increased porosity. Tilmicosin release profiles from PLGA and PLGA-biopolymer microparticles were affected by the particular combination of polymers and the pH of the release medium. The experimental data was simulated using a mathematical model, which takes into account the autocatalytic polymer degradation and the different mechanisms of drug transport. The combination of PLGA and biopolymers strongly influenced the morphology of the particles, offering the possibility of controlling the drug release profiles according to the therapy.     

Simple approach for the plasma treatment of polymeric membranes and investigation of the aging effect

The modification of the surface characteristics after treatment with plasma in polymeric materials, such as the aging phenomenon, calls the attention of research in the area of nonthermal plasma technology. In this work, a direct treatment with dielectric-barrier discharge plasma was used on the surfaces of ultrafiltration membranes. The measurements of the contact angle with water, attenuated total reflectance accessory, zeta potential, atomic force microscopy and scanning electron microscopy-MEV were performed on the surfaces to verify changes after plasma treatment and to understand the occurrence and timing of the plasma aging effect. In the analysis of the membrane performance, hydraulic permeation and protein retention tests were performed. The results showed an improvement in wettability and hydrophilic properties in the post-treated membranes. The study of reversibility/aging of the post-plasma surface is important for research that deals with the modification of polymeric membranes. Changes in surface morphology, topography and wettability of the membranes were observed up to seven days after treatment, with a tendency to return to the initial characteristics of the membranes.     

The effect of adding devulcanized rubber on the thermomechanical properties of recycled polypropylene

This work aims to investigate the effect of adding vulcanized or partially devulcanized rubbers on recycled polypropylene (PPr), considering thermomechanical and morphological properties. The study proposes to better understand how structural changes underwent by rubber (after the devulcanization) contributed to improving the mechanical properties of the PPr. The PPr/rubber blends were prepared by a co-rotating twin-screw extruder and then were injected. The blends composed of the most devulcanized rubbers by microwaves with refined microstructure showed higher values of elongation at break and toughness. Data showed that the devulcanization process applied to the rubber interfered positively in its adhesion to the PPr. Data from dynamic mechanical analysis and atomic force microscopy indicated that the most devulcanized rubbers presented an interface more connected to PPr. These chemical interactions possibly impacted the mechanical properties of the PPr. Moreover, dilatation processes favored the fracture mechanisms of the PPr when rubber was added to it.     

Analysis of surface integrity for minimum quantity lubricant—MQL in grinding

The quality of machined components is currently of high interest, for the market demands mechanical components of increasingly high performance, not only from the standpoint of functionality but also from that of safety. Components produced through operations involving the removal of material display surface irregularities resulting not only from the action of the tool itself, but also from other factors that contribute to their superficial texture. This texture can exert a decisive influence on the application and performance of the machined component. This article analyzes the behavior of the minimum quantity lubricant (MQL) technique and compares it with the conventional cooling method. To this end, an optimized fluid application method was devised using a specially designed nozzle, by the authors, through which a minimum amount of oil is sprayed in a compressed air flow, thus meeting environmental requirements. This paper, therefore, explores and discusses the concept of the MQL in the grinding process. The performance of the MQL technique in the grinding process was evaluated based on an analysis of the surface integrity (roughness, residual stress, microstructure and microhardness). The results presented here are expected to lead to technological and ecological gains in the grinding process using MQL.     

Beyond multi-adjoint logic programming

In ‘multi-adjoint logic programming’, MALP in brief, each fuzzy logic program is associated with its own ‘multi-adjoint lattice’ for modelling truth degrees beyond the simpler case of true and false, where a large set of fuzzy connectives can be defined. On this wide repertoire, it is crucial to connect each implication symbol with a proper conjunction thus conforming constructs of the form (←_i, &_i) called ‘adjoint pairs’, whose use directly affects both declarative and operational semantics of the MALP framework. In this work, we firstly show how the strong dependence of adjoint pairs can be largely weakened for an interesting ‘sub-class’ of MALP programs. Then, we reason in a similar way till conceiving a ‘super-class’ of fuzzy logic programs beyond MALP, which definitively drops out the need for using adjoint pairs, since the new semantics behaviour relies on much more relaxed lattices than multi-adjoint ones.     

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.     

A Closed-Loop Automatic Data-Mining Approach for Preventive Network Monitoring

As telecommunication networks grow in size and complexity, monitoring systems need to scale up accordingly. Alarm data generated in a large network are often highly correlated. These correlations can be explored to simplify the process of network fault management, by reducing the number of alarms presented to the network-monitoring operator. This makes it easier to react to network failures. But in some scenarios, it is highly desired to prevent the occurrence of these failures by predicting the occurrence of alarms before hand. This work investigates the usage of data mining methods to generate knowledge from historical alarm data, and using such knowledge to train a machine learning system, in order to predict the occurrence of the most relevant alarms in the network. The learning system was designed to be retrained periodically in order to keep an updated knowledge base.     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.