Making random permutations from physically unclonable constants

The recent availability of reliable schemes for physically unclonable constants (PUC) opens interesting possibilities in the field of security. In this paper, we explore the possibility of using PUCs to embed in a chip random permutations to be used, for example, as building blocks in cryptographic constructions such as sponge functions, substitution–permutation networks, and so on. We show that the most difficult part is the generation of random integers using as the only randomness source the bit-string produced by the PUC. In order to solve the integer generation problem, we propose a partial rejection method that allows the designer to trade-off between entropy and efficiency. The results show that the proposed schemes can be implemented with reasonable complexity.     

Dealing with logical omniscience: Expressiveness and pragmatics

We examine four approaches for dealing with the logical omniscience problem and their potential applicability: the syntactic approach, awareness, algorithmic knowledge, and impossible possible worlds. Although in some settings these approaches are equi-expressive and can capture all epistemic states, in other settings of interest (especially with probability in the picture), we show that they are not equi-expressive. We then consider the pragmatics of dealing with logical omniscience—how to choose an approach and construct an appropriate model.     

An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.     

On‐Chip Magnetic Platform for Single‐Particle Manipulation with Integrated Electrical Feedback

Methods for the manipulation of single magnetic particles have become very interesting, in particular for in vitro biological studies. Most of these studies require an external microscope to provide the operator with feedback for controlling the particle motion, thus preventing the use of magnetic particles in high‐throughput experiments. In this paper, a simple and compact system with integrated electrical feedback is presented, implementing in the very same device both the manipulation and detection of the transit of single particles. The proposed platform is based on zig‐zag shaped magnetic nanostructures, where transverse magnetic domain walls are pinned at the corners and attract magnetic particles in suspension. By applying suitable external magnetic fields, the domain walls move to the nearest corner, thus causing the step by step displacement of the particles along the nanostructure. The very same structure is also employed for detecting the bead transit. Indeed, the presence of the magnetic particle in suspension over the domain wall affects the depinning field required for its displacement. This characteristic field can be monitored through anisotropic magnetoresistance measurements, thus implementing an integrated electrical feedback of the bead transit. In particular, the individual manipulation and detection of single 1‐μm sized beads is demonstrated.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Potential of the Stromal Matricellular Protein Periostin as a Biomarker to Improve Risk Assessment in Prostate Cancer

Prostate cancer (PCa) ranges from indolent to aggressive tumors that may rapidly progress and metastasize. The switch to aggressive PCa is fostered by reactive stroma infiltrating tumor foci. Therefore, reactive stroma-based biomarkers may potentially improve the early detection of aggressive PCa, ameliorating disease classification. Gene expression profiles of PCa reactive fibroblasts highlighted the up-regulation of genes related to stroma deposition, including periostin and sparc. Here, the potential of periostin as a stromal biomarker has been investigated on PCa prostatectomies by immunohistochemistry. Moreover, circulating levels of periostin and sparc have been assessed in a low-risk PCa patient cohort enrolled in active surveillance (AS) by ELISA. We found that periostin is mainly expressed in the peritumoral stroma of prostatectomies, and its stromal expression correlates with PCa grade and aggressive disease features, such as the cribriform growth. Moreover, stromal periostin staining is associated with a shorter biochemical recurrence-free survival of PCa patients. Interestingly, the integration of periostin and sparc circulating levels into a model based on standard clinico-pathological variables improves its performance in predicting disease reclassification of AS patients. In this study, we provide the first evidence that circulating molecular biomarkers of PCa stroma may refine risk assessment and predict the reclassification of AS patients.     

A decision-support system for the design and management of warehousing systems

The issue of material handling involves the design and operative control of warehousing systems (i.e., distribution centres), which allow matching vendors and demands, smoothing with seasonality, consolidating products and arranging distribution activities. Warehousing systems play a crucial role in providing efficiency and customer satisfaction. The warehouse design entails a wide set of decisions, which involve layout constraints and operative issues that seriously affect the performances and the overall logistics costs.     

Linear optimal control strategies for production systems with a discrete-event demand pattern

A class of production systems are considered in this paper with the aim of determining closed-loop strategies providing the production effort as functions of the system state. The key feature of the considered class of systems is that the demand and the way it is satisfied are asynchronous sequences of part requests and instantaneous withdrawals, respectively, generated by discrete-event processes, whereas the production process has continuous-time dynamics. The optimization problem, whose objective is to minimize inventory and lateness costs, is restated as an optimal control problem by exploiting some structural properties of the optimal solution. Then, optimal closed-loop strategies are found with dynamic programming techniques.