Optimizing rheological performance of unsaturated polyester resin with biobased reactive diluents: A comprehensive analysis of viscosity and thermomechanical properties

[EN] Bio-based reactive diluents (RD) have been explored as alternative to styrene (STY) in unsaturated polyester resin (UPR). Among the different candidates, acrylated epoxidized soybean oil (AESO) and epoxidized linseed oil (ELO) stand out as triglyceride derivatives. Aditionally, methyl methacrylate (MMA), trimethylolpropane triacrylate (TMPTA) with acrylic functionality, limonene (LIM), and cinnamates (CINN), has been tested in different percentages. Firstly, their efficiency in viscosity reduction has been studied. Best results were obtained after the addition of MMA, LIM, and CINN at 5 wt%. These RD achieve a viscosity reduction of 48.9 %, 76.7 %, and 22.9 %, respectively, compared to the reference sample. The industrial utilization of CINN as RD is impeded by its reactivity, as has been evidenced by its prolonged reaction time (24 min) and low reaction enthalpy. The thermo-mechanical properties studied through flexural tests, Shore D hardness, Charpy¿s Impact test, and heat deflection temperature (HDT), show that the developed UPRs exhibit a decrease in resistant mechanical properties while doubling their ductility by using LIM and MMA as bio-based RD (1.88 and 2.15 kJ m¿ 2 , respectively). The HDT study results demonstrate a certain level of thermal stability when MMA is employed (56 ¿C), which is 15 % lower in the case of LIM. Therefore, it is observed that UPRs with bio-based RD exhibit balanced and improved thermo-mechanical properties in terms of ductility and strength, especially with the use of a 5 wt % of LIM and MMA.This work has been developed in the context of EOCENE project (Circular Economy in the Thermostable Composites Industry) supported by the Ministry of Science and Innovation of Spain through the Misiones Ciencia e Innovacion" ¿ program in its 2019 call. The program Misiones is managed by the Center for Industrial Technological Development (CDTI), and is co-founded with FEDER funds through the Plurirregional Operational Program of Spain 2014 2020 (POPE). On the other hand, the UPV would like to thank the funding received by Ministry of Science and Innovation of Spain through the Retos de la Sociedad . Project references: PID2020- 119142RA-I00.Grimalt, J.; Frattini, L.; Carreras, P.; Fombuena, V. (2023). Optimizing rheological performance of unsaturated polyester resin with biobased reactive diluents: A comprehensive analysis of viscosity and thermomechanical properties. Polymer Testing. 129(108264). https://doi.org/10.1016/j.polymertesting.2023.10826412910826

Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities

Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201

Building an outward-oriented social family legacy: rhetorical history in family business foundations

Scholars have recently paid growing attention to the transfer of family legacies across generations, but existing work has been mainly focused on an inward-oriented, intra-family, perspective. In this article, we seek to understand how family firms engage in rhetorical history to transfer their social family legacy to external stakeholders, what we call “outward-oriented social legacy.” By carrying out a 12-months field study in three Italian family business foundations, our findings unveil three distinctive narrative practices—founder foreshadowing, emplacing the legacy within the broader community, and weaving family history with macro—history—that contribute to transferring outward-oriented social legacies

A homozygous contiguous gene deletion in chromosome 16p13.3 leads to autosomal recessive osteopetrosis in a Jordanian patient

Human malignant autosomal recessive osteopetrosis (ARO) is a genetically heterogeneous disorder caused by reduced bone resorption by osteoclasts. Mutations in the CLCN7 gene are responsible not only for a substantial portion of ARO patients, but also for other forms of osteopetrosis characterized by different severity and inheritance. The lack of a clear genotype/phenotype correlation makes genetic counselling a tricky issue for CLCN7-dependent osteopetrosis. Here we characterize the first homozygous interstitial deletion in 16p13.3, detected by array Comparative Genomic Hybridization (a-CGH) in an ARO patient of Jordanian origin. The deletion involved other genes beside CLCN7, while the proband displayed a classic ARO phenotype; however her early death did not allow more extensive clinical investigations. The identification of this novel genomic deletion involving a large part of the CLCN7 gene is of clinical relevance, especially in prenatal diagnosis, and suggests the possibility that this kind of mutation has been underestimated so far. This data highlights the need for alternative approaches to genetic analysis also in other ARO-causative genes

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes