GraftFast Surface Engineering to Improve MOF Nanoparticles Furtiveness

Controlling the outer surface of nanometric metal–organic frameworks (nanoMOFs) and further understanding the in vivo effect of the coated material are crucial for the convenient biomedical applications of MOFs. However, in most studies, the surface modification protocol is often associated with significant toxicity and/or lack of selectivity. As an alternative, how the highly selective and general grafting GraftFast method leads, through a green and simple process, to the successful attachment of multifunctional biopolymers (polyethylene glycol (PEG) and hyaluronic acid) on the external surface of nanoMOFs is reported. In particular, effectively PEGylated iron trimesate MIL‐100(Fe) nanoparticles (NPs) exhibit suitable grafting stability and superior chemical and colloidal stability in different biofluids, while conserving full porosity and allowing the adsorption of bioactive molecules (cosmetic and antitumor agents). Furthermore, the nature of the MOF–PEG interaction is deeply investigated using high‐resolution soft X‐ray spectroscopy. Finally, a cell penetration study using the radio‐labeled antitumor agent gemcitabine monophosphate (³H‐GMP)‐loaded MIL‐100(Fe)@PEG NPs shows reduced macrophage phagocytosis, confirming a significant in vitro PEG furtiveness.     

On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets

When performing a classification task, we may find some data-sets with a different class distribution among their patterns. This problem is known as classification with imbalanced data-sets and it appears in many real application areas. For this reason, it has recently become a relevant topic in the area of Machine Learning.The aim of this work is to improve the behaviour of fuzzy rule based classification systems (FRBCSs) in the framework of imbalanced data-sets by means of a tuning step. Specifically, we adapt the 2-tuples based genetic tuning approach to classification problems showing the good synergy between this method and some FRBCSs.Our empirical results show that the 2-tuples based genetic tuning increases the performance of FRBCSs in all types of imbalanced data. Furthermore, when the initial Rule Base, built by a fuzzy rule learning methodology, obtains a good behaviour in terms of accuracy, we achieve a higher improvement in performance for the whole model when applying the genetic 2-tuples post-processing step. This enhancement is also obtained in the case of cooperation with a preprocessing stage, proving the necessity of rebalancing the training set before the learning phase when dealing with imbalanced data.     

Effect of physical vapor deposition metallic thin films on micro injection molded weld line mechanical properties

Micro injection molded polymeric parts coated with functional thin films/layers show off the promising applications in microsystems area. But the unfavorable and unavoidable defect of weld line in micro injection molding part leads to detrimental mechanical and surface properties. The possibility of the functional thin film for enhancing micro injection molded weld lines was investigated. Two typical coating materials (aluminum and titanium) with various film thicknesses (400, 600, 800 nm) were deposited on one side of the micro injection molded weld line tensile sample via physical vapor deposition (PVD) method. The coated micro weld line samples were characterized by tensile tests. The results show that PVD films of aluminum and titanium can reinforce the strength and stiffness of micro injection molded weld line, even at thin thickness levels. But when the film thickness is increasing, the weaker adhesion between metallic films and polymers decreased the PVD films’ enhancing performance for micro weld line mechanical properties due to the degradation of polymers related to longer time exposure under high temperature.     

Model Checking Games for the Quantitative μ-Calculus

We investigate quantitative extensions of modal logic and the modal μ-calculus, and study the question whether the tight connection between logic and games can be lifted from the qualitative logics to their quantitative counterparts. It turns out that, if the quantitative μ-calculus is defined in an appropriate way respecting the duality properties between the logical operators, then its model checking problem can indeed be characterised by a quantitative variant of parity games. However, these quantitative games have quite different properties than their classical counterparts, in particular they are, in general, not positionally determined. The correspondence between the logic and the games goes both ways: the value of a formula on a quantitative transition system coincides with the value of the associated quantitative game, and conversely, the values of quantitative parity games are definable in the quantitative μ-calculus.     

A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach

Nowadays, the patients and physicians use the health-related websites as an important information source and, therefore, it is critical the quality evaluation of health- related websites. The quality assessment of health-related websites becomes especially relevant because their use imply the existence of a wide range of threats which can affect people’s health. Additionally, website quality evaluation can also contribute to maximize the exploitation of invested resources by organizations in the development of user-perceived quality websites. But there is not yet a clear and unambiguous definition of the concept of website quality and the debate about quality evaluation on the Web remains open. In this paper, we present a qualitative and user-oriented methodology for assessing quality of health-related websites based on a 2-tuple fuzzy linguistic approach. To identify the quality criteria set, a qualitative research has been carried out using the focus groups technique. The measurement method generates linguistic quality assessments considering the visitors’ judgements with respect to those quality criteria. The combination of the linguistic judgements is implemented without a loss of information by applying a 2-tuple linguistic weighted average operator. This methodology means an improvement on quality evaluation of health websites through the commitment to put users first.     

Surface plasmon resonance imaging (SPRi) as an alternative technique for rapid and quantitative screening of small molecules, useful in drug discovery

Surface plasmon resonance imaging (SPRi) is a label free technology for biomolecular interaction, which gives access to binding kinetic parameters from real time acquisition. It offers the possibility to test in a single run a large number of interactions, allowing rapid identification of the most suitable compounds toward a given biological entity. Until now, this technique has proven to be relevant for interaction between relatively large molecules (protein, antibodies, DNA) but has not been challenged yet for the screening of small molecules that can be of interested in the field of drug discovery. As a proof a principle, we have used SPRi to screen for interaction of several small molecules, referred to as G4-ligands on G-quadruplex DNA. This technology allowed to easy discrimination of the binding properties of four G4-ligands on quadruplex DNA models.     

Pattern analysis of dermoscopic images based on Markov random fields

In this paper a method for detecting different patterns in dermoscopic images is presented. In order to diagnose a possible skin cancer, physicians assess the lesion based on different rules. While the most famous one is the ABCD rule (asymmetry, border, colour, diameter), the new tendency in dermatology is to classify the lesion performing a pattern analysis. Due to the colour textured appearance of these patterns, this paper presents a novel method based on Markov random field (MRF) extended for colour images that classifies images representing different dermatologic patterns. First, each image plane in L^*a^*b^* colour space is modelled as a MRF following a finite symmetric conditional model (FSCM). Coupling of colour components is taken into account by supposing that features of the MRF in the three colour planes follow a multivariate Normal distribution. Performance is analysed in different colour spaces. The best classification rate is 86% on average.     

MPC for battery/fuel cell hybrid vehicles including fuel cell dynamics and battery performance improvement

In this paper, the performance and durability of hybrid PEM fuel cell vehicles are investigated. To that end, a hybrid predictive controller is proposed to improve battery performance and to avoid fuel cell and battery degradation. Such controller deals with this complex control problem by handling binary and continuous variables, piecewise affine models and constraints. Moreover, the control strategy is to track motor power demand and keep batteries close to a desired battery state of charge which is appropriately chosen to minimize hydrogen consumption. It is important to highlight the consideration of constraints which are directly related to the goals of this paper, such as minimum fuel cell power threshold and time limitation between fuel cell startups and shutdowns. Furthermore, different models have been elaborated and particularized for a vehicle prototype. These models include few innovations such as a reference governor which smooths fuel cell power demand during sharp power profiles, forcing batteries to supply such peaks and resulting a longer fuel cell lifetime. Battery thermal dynamics are also taken into account in these models in order to analyze the effect of battery temperature on its degradation. Finally, this paper studies the feasibility of the real implementation, presenting an explicit formulation as a solution to reduce execution time. This explicit controller exhibits the same performance as the hybrid predictive controller does with a reduced computational effort. All the results have been validated in several simulations.     

Bayesian networks for enhancement of requirements engineering: a literature review

Requirements analysis is the software engineering stage that is closest to the users’ world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.     

LPS: A Language Prototyping System Using Modular Monadic Semantics

This paper describes LPS, a Language Prototyping System that facilitates the modular development of interpreters from semantic building blocks. The system is based on the integration of ideas from Modular Monadic Semantics and Generic Programming.To define a new programming language, the abstract syntax is described as the fixpoint of non-recursive pattern functors. For each functor an algebra is defined whose carrier is the computational monad obtained from the application of several monad transformers to a base monad. The interpreter is automatically generated by a catamorphism or, in some special cases, a monadic catamorphism.The system has been implemented as a domain-specific language embedded in Haskell and we have also implemented an interactive framework for language testing.