A knowledge-based decision support system for shipboard damage control

The operational complexity of modern ships requires the use of advanced applications, called damage control systems (DCSs), able to assist crew members in the effective handling of dangerous events and accidents. In this article we describe the development of a knowledge-based decision support system (KDSS) integrated within a DCS designed for a national navy. The KDSS uses a hybrid design and runtime knowledge model to assist damage control operators through a kill card function which supports damage identification, action scheduling and system reconfiguration. We report a fire fighting scenario as illustrative application and discuss a preliminary evaluation of benefits allowed by the system in terms of critical performance measures. Our work can support further research aimed to apply expert systems to improve shipboard security and suggest similar applications in other contexts where situational awareness and damage management are crucial.     

eHDECAY: An implementation of the Higgs effective Lagrangian into HDECAY

We present eHDECAY, a modified version of the program HDECAY which includes the full list of leading bosonic operators of the Higgs effective Lagrangian with a linear or non-linear realization of the electroweak symmetry and implements two benchmark composite Higgs models.     

Pulse and Continuous Radiolyses of the Europium and Neodymium Decatungstate Anions in Aqueous Solution

We have studied the behavior of aqueous solutions of the [M⁽³⁺⁾(W₅-O₁₈^(6-))₂]⁹⁻ complexes (M = Eu or Nd) under pulse and continuous radiolysis conditions. Both complexes react with e⁻_aq at a diffusion controlled rate. For the Nd³⁺ complex in solution containing 1% 2-propanol, the decay of the e⁻_aq absorption was accompanied by the formation of a broad absorption band in the visible (λ_max 600–650 nm), with a further increase in absorption up to 2 ms. For the Eu³⁺ complex under the same conditions, the decay of the e⁻_aq absorption was accompanied by the formation of an absorption maximum at ∼370 nm, which is characteristic of Eu²⁺ species. On a longer time scale, a relatively intense absorption arose in the visible (λ_max 600–650 nm) according to a complicated kinetic behavior. The two complexes exhibited a quite similar behavior in continuous radiolysis experiments. Blue products (λ_max 600–650 nm) were formed and, for the Eu³⁺ complex, the disappearance of the Eu³⁺ emission was observed. Even after high irradiation doses, the absorption (and, for Eu³⁺, emission) spectra of the solutions were quantitatively regenerated by air oxidation. The behavior of the Nd³⁺ complex is interpreted on the basis of the reduction of a W₅ O₁₈⁶⁻ ligand by e⁻_aq and alcohol radicals, followed by protonation. For the Eu³⁺ complex, the reduction by e⁻_aq causes mainly the formation of a Eu²⁺ complex. On a longer time scale, protonation and/or reaction with alcohol radicals drive back the electron from Eu²⁺ to the W₅O₁₈⁶⁻ ligands, with formation of stable blue products.     

RELATIONAL DATA MINING AND ILP FOR DOCUMENT IMAGE UNDERSTANDING

Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on domain-specific knowledge that can be acquired automatically by applying data mining techniques. The spatial dimension of page layout makes classification methods developed in inductive logic programming (ILP) and multi-relational data mining (MRDM) the most suitable candidates for this specific task. In this paper, both approaches are considered and empirically compared on three different data sets consisting of multi-page articles published in an international journal and historical documents. The ILP method is able to learn recursive logical theories that express dependencies between logical components, while the MRDM method extends the naïve Bayesian classifier to data stored in multiple tables of a relational database. Experimental results confirm the importance of the spatial dimension for this application and show that the ILP method tends to be conservative with a high (low) percentage of omission (commission) errors, while the probabilistic nature of the MRDM method allows us to tradeoff between the two types of error.     

β-Hexachlorocyclohexane Drives Carcinogenesis in the Human Normal Bronchial Epithelium Cell Line BEAS-2B

Organochlorine pesticides constitute the majority of the total environmental pollutants, and a wide range of compounds have been found to be carcinogenic to humans. Among all, growing interest has been focused on β-hexachlorocyclohexane (β-HCH), virtually the most hazardous and, at the same time, the most poorly investigated member of the hexachlorocyclohexane family. Considering the multifaceted biochemical activities of β-HCH, already established in our previous studies, the aim of this work is to assess whether β-HCH could also trigger cellular malignant transformation toward cancer development. For this purpose, experiments were performed on the human normal bronchial epithelium cell line BEAS-2B exposed to 10 µM β-HCH. The obtained results strongly support the carcinogenic potential of β-HCH, which is achieved through both non-genotoxic (activation of oncogenic signaling pathways and proliferative activity) and indirect genotoxic (ROS production and DNA damage) mechanisms that significantly affect cellular macroscopic characteristics and functions such as cell morphology, cell cycle profile, and apoptosis. Taking all these elements into account, the presented study provides important elements to further characterize β-HCH, which appears to be a full-fledged carcinogenic agent.     

MICROFOUNDATIONS OF KNOWLEDGE DYNAMICS WITHIN THE FIRM

A distinctive feature of human activity is the creation of knowledge, and the firm is one of the many places in which knowledge is created. This paper deals with some aspects of creating knowledge in the firm: its dynamics, underlying cognitive models, dependence on context, role of human resources in the firm and the effects of their renewal. Introducing the cognitive dimension of the individual in a theory of the firm is compelling in order to understand the new role of intangibles in actual firms. It also offers a base for aligning the resource-based view with the knowledge-based view of the firm. Understanding the ways in which individual, and, much more importantly, collective knowledge evolves offers the opportunity to give human beings the central role they deserve. The firm may thus be seen as a knowledge-creating entity and human resources as the pivot of the organization.     

Curcumin-Loaded Nanoparticles Impair the Pro-Tumor Activity of Acid-Stressed MSC in an In Vitro Model of Osteosarcoma

In the tumor microenvironment, mesenchymal stromal cells (MSCs) are key modulators of cancer cell behavior in response to several stimuli. Intratumoral acidosis is a metabolic trait of fast-growing tumors that can induce a pro-tumorigenic phenotype in MSCs through the activation of the NF-κB-mediated inflammatory pathway, driving tumor clonogenicity, invasion, and chemoresistance. Recent studies have indicated that curcumin, a natural ingredient extracted from Curcuma longa, acts as an NF-κB inhibitor with anti-inflammatory properties. In this work, highly proliferating osteosarcoma cells were used to study the ability of curcumin to reduce the supportive effect of MSCs when stimulated by acidosis. Due to the poor solubility of curcumin in biological fluids, we used spherical polymeric nanoparticles as carriers (SPN-curc) to optimize its uptake by MSCs. We showed that SPN-curc inhibited the release of inflammatory cytokines (IL6 and IL8) by acidity-stimulated MSCs at a higher extent than by free curcumin. SPN-curc treatment was also successful in blocking tumor stemness, migration, and invasion that were driven by the secretome of acid-stressed MSCs. Overall, these data encourage the use of lipid–polymeric nanoparticles encapsulating NF-κB inhibitors such as curcumin to treat cancers whose progression is stimulated by an activated mesenchymal stroma.     

Wet Lay-Up Manufactured FRPs for Concrete and Masonry Repair: Influence of Water on the Properties of Composites and on Their Epoxy Components

The assessment of durability of fiber-reinforced polymers (FRPs), used to strengthen masonry or concrete structures, if subjected to weathering is a knotty problem. Environmental factors can have a significant effect on their performance in service. In order to investigate on this aspect, the mechanical behavior of two commercial composites, reinforced with unidirectional carbon and glass fibers, was analyzed after a long-term immersion in distilled water. For comparison purposes, three different commercial epoxy resins, used as primer, putty, or adhesive to manufacture and apply the composites through the wet lay-up technique to the surfaces to strengthen, were subjected to the same treatment. In order to take into account the peculiarities of the three cold-curing epoxy resins, a novel procedure to dry the specimens before the immersion treatment was used. The mechanical tests, performed on the composites before and after their immersion in water, evidenced that this agent has a limited effect only on the in-plane tensile strength of wet lay-up manufactured glass FRP, while the mechanical properties of carbon FRP are substantially unaffected by water. On the other hand, the effect of water on the thermal and mechanical characteristics of the three epoxy resins is quite severe, with significant reductions in the glass transition temperature, stiffness, and strength.     

Genetic approach helps to speed classical Price algorithm for global optimization

In this paper is presented an hybrid algorithm for finding the absolute extreme point of a multimodal scalar function of many variables. The algorithm is suitable when the objective function is expensive to compute, the computation can be affected by noise and/or partial derivatives cannot be calculated. The method used is a genetic modification of a previous algorithm based on the Prices method. All information about behavior of objective function collected on previous iterates are used to chose new evaluation points. The genetic part of the algorithm is very effective to escape from local attractors of the algorithm and assures convergence in probability to the global optimum. The proposed algorithm has been tested on a large set of multimodal test problems outperforming both the modified Prices algorithm and classical genetic approach.