A Model of Partially Observable State Game and its Optimality

In this paper we present a model of two-players partially observable state-game and study its optimality. The model is inspired by the practical problem of negotiation in a multi-agent system and formulates, from a the game theory point of view, the so-called contract net protocol. It covers a wide variety of real problems including some simple card games such as blackjack, and many negotiation and bargaining situations. The results that follow are valid for non-zero-sum games as well as for zero-sum games. Basically, we establish and prove the relation between partially observable state games and some classical (single-state) bi-matrix games. If the original state game is zero-sum, then the equivalent bi-matrix game is so.     

Membrane Systems with Marked Membranes

Membrane computing is a biologically inspired computational paradigm. Motivated by brane calculi we investigate membrane systems which differ from conventional membrane systems by the following features: (1) biomolecules (proteins) can move through the regions of the systems, and can attach onto (and de-attach from) membranes, and (2) membranes can evolve depending on the attached molecules. The evolution of membranes is performed by using rules that are motivated by the operation of pinocytosis (the pino rule) and the operation of cellular dripping (the drip rule) that take place in living cells. We show that such membrane systems are computationally universal. We also show that if only the second feature is used then one can generate at least the family of Parikh images of the languages generated by programmed grammars without appearance checking (which contains non-semilinear sets of vectors). If, moreover, the use of pino/drip rules is non-cooperative (i.e., not dependent on the proteins attached to membranes), then one generates a family of sets of vectors that is strictly included in the family of semilinear sets of vectors. We also consider a number of decision problems concerning reachability of configurations and boundness.     

Dissimilar Resistance Spot Welding of Q&P and TWIP Steel Sheets

Dissimilar resistance spot welding of twinning induced plasticity (TWIP) and quenching and partitioning (Q&P) steel grades has been investigated by evaluating the effects of clamping force, welding current, and welding time on the microstructure, shear tension strength, and fracture of welded samples. The spot welding of TWIP and Q&P steels promotes the occurrence of an asymmetrical weld nugget with a greater dilution of TWIP steel because of its lower melting temperature and thermal conductivity. As a result, weld nuggets exhibit an austenitic microstructure. TWIP steel undergoes a grain coarsening in the HAZ, whereas Q&P steel undergoes some phase transformations. Welded samples tend to exhibit higher shear tension strength as they are joined at the highest welding current, even though an improper clamping force can promote excessive metal expulsion, thereby reducing the mechanical strength of the welded joints. Shear tension welded samples failed through interfacial fracture with partial thickness fracture mode for a low welding current, while partial thickness with button pull fractures were observed when a high welding current was used. The weld spots predominantly failed at the TWIP side. However, as TWIP steel can work harden significantly in the more resistant welded joints, the failures occur, instead, at the Q&P side.     

Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market

The basis of an efficient functioning of a power grid is an accurate balancing of the electricity demand of all the consumers at any instant with supply. Nowadays, this task involves only the grid operator and retail electricity providers. One of the facets of the Smart Grid vision is that consumers may have a more active role in the problem of balancing demand with supply. With the deployment of intelligent information and communication technologies in domestic environments, homes are becoming smarter and able to play a more active role in the management of energy. We use the term Smart Consumer Load Balancing to refer to algorithms that are run by energy management systems of homes in order to optimise the electricity consumption, to minimise costs and/or meet supply constraints. In this work, we analyse different approaches to Smart Consumer Load Balancing based on (distributed) artificial intelligence. We also put forward a new model of Smart Consumer Load Balancing, where consumers actively participate in the balancing of demand with supply by forming groups that agree on a joint demand profile to be contracted in the market with the mediation of an aggregator. We specify the business model as well as the optimisation model for load balancing, showing the economic benefits for the consumers in a realistic scenario based on the Spanish electricity market.     

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links “on the go” requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.     

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is “robust” within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.     

Mesoporous alumina obtained by combustion synthesis without template

Mesoporous alumina has been synthesised by the solution combustion synthesis method. While often the synthesis of mesoporous materials with a relatively narrow pore distribution requires the use of a template, this technique allows a simple and rapid synthesis of pure alumina without the use of any templating agent: by regulating the synthesis conditions is possible to obtain from relatively low surface area α-alumina to high surface mesoporous γ-alumina. The porosity of the latter was assessed chiefly by density functional method and was found to be in the range 2.8–3.5 nm. Low angle X-ray diffraction experiments demonstrated a partial ordering of the structure, with a periodicity in the range 5.5–6.9 nm. The addition of lanthanum to the alumina was tested and found to confer a good temperature resistance to the alumina, without modifying the mesoporous structure. After a prolonged heat treatment at 900 °C the mesoporosity of lanthanum-modified samples was retained, even if the pores size increased and the specific surface area decreased.     

How “Authentic Intentionality” can be Enabled: a Neurocomputational Hypothesis

According to John Haugeland, the capacity for “authentic intentionality” depends on a commitment to constitutive standards of objectivity. One of the consequences of Haugeland’s view is that a neurocomputational explanation cannot be adequate to understand “authentic intentionality”. This paper gives grounds to resist such a consequence. It provides the beginning of an account of authentic intentionality in terms of neurocomputational enabling conditions. It argues that the standards, which constitute the domain of objects that can be represented, reflect the statistical structure of the environments where brain sensory systems evolved and develop. The objection that I equivocate on what Haugeland means by “commitment to standards” is rebutted by introducing the notion of “florid, self-conscious representing”. Were the hypothesis presented plausible, computational neuroscience would offer a promising framework for a better understanding of the conditions for meaningful representation.     

Robust Multilayer Control for Enhanced Wireless Telemedical Video Streaming

M-health is an emerging area of research and one of the key challenges in future research in this area is medical video streaming over wireless channels. Contrasting requirements of almost lossless compression and low available bandwidth have to be tackled in medical quality video streaming in ultrasound and radiology applications. On one side, compression techniques need to be conservative, in order to avoid removing perceptively important information; on the other side, error resilience and correction should be provided, with the constraint of a limited bandwidth. A quality-driven, network-aware approach for joint source and channel coding based on a controller structure specifically designed for enhanced video streaming in a robotic teleultrasonography system is presented. The designed application based on robotic teleultrasonography is described and the proposed method is simulated in a wireless environment in two different scenarios; the video quality improvement achievable through the proposed scheme in such an application is remarkable, resulting in a peak signal-to-noise ratio (PSNR) improvement of more than 4 dB in both scenarios.     

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19

Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10⁻⁵) at the 3p21.31 locus in public GWAS datasets (6406 COVID-19 hospitalized patients and 902,088 controls) with gene expression data from 208 lung tissues, Hi-C, and Chip-seq data. Through whole exome sequencing (WES), we explored rare coding variants in 147 severe COVID-19 patients. We identified three SNPs (rs9845542, rs12639314, and rs35951367) associated with severe COVID-19 whose risk alleles correlated with low CCR5 expression in lung tissues. The rs35951367 resided in a CTFC binding site that interacts with CCR5 gene in lung tissues and was confirmed to be associated with severe COVID-19 in two independent datasets. We also identified a rare coding variant (rs34418657) associated with the risk of developing severe COVID-19. Our results suggest a biological role of CCR5 in the progression of COVID-19 as common and rare genetic variants can increase the risk of developing severe COVID-19 by affecting the functions of CCR5.