Dynamic modeling of the secondary drying stage of freeze drying reveals distinct desorption kinetics for bound water

In freeze drying, the desorption step for reaching a low target moisture content may take a significant fraction of the total process duration. Because the long-term stability of freeze-dried biological products strongly depends on the current moisture content, modeling the desorption process may help safely optimize the secondary drying step. Most published models assume a first-order desorption kinetic, but experimental evidence shows that strongly bound water in the monolayer takes a much longer time to be desorbed than less bound water in multilayer. The proposed model for desorption of freeze-dried lactic acid bacteria preparation accounts for monolayer and multilayer water state in the solid matrix, with very different desorption kinetics. Results showed that the ratio of characteristic desorption times (monolayer/multilayer) was almost 30. Temperature dependence was adequately described by an Arrhenius law in the range of 15 to 40°C. Model parameter identification used simultaneously gravimetric measurements with high time resolution and direct Karl-Fisher titration, from several experiments at different, time-varying temperatures.     

Thermal regimes of Li‐ion conductivity in β‐eucryptite

While it is well‐established that ionic conduction in lithium aluminosilicates proceeds via hopping of Li ions, the nature of the various hoping‐based mechanisms in different temperature regimes has not been fully elucidated. The difficulties associated with investigating the conduction have to do with the presence of grains and grain boundaries of different orientations in these usually polycrystalline materials. Herein, we use electrochemical impedance spectroscopy (EIS) to investigate the ion conduction mechanisms in β‐eucryptite, which is a prototypical lithium aluminosilicate. In the absence of significant structural transitions in grain boundaries, we find that there are three conduction regimes for the one‐dimensional ionic motion along the c axis channels in the grains, and determine the activation energies for each of these temperature regimes. Activation energies computed from molecular statics calculations of the potential energy landscape encountered by Li ions suggest that at temperatures below 440°C conduction proceeds via cooperative or correlated motion, in agreement with established literature. Between 440°C and 500°C, the activation barriers extracted from EIS measurements are large and consistent with those from atomistic calculations for uncorrelated Li ion hopping. Above 500°C the activation barriers decrease significantly, which indicates that after the transition to the Li‐disordered phase of β‐eucryptite, the Li ion motion largely regains the correlated character.     

Incremental model-based estimation using geometric constraints

We present a model-based framework for incremental, adaptive object shape estimation and tracking in monocular image sequences. Parametric structure and motion estimation methods usually assume a fixed class of shape representation (splines, deformable superquadrics, etc.) that is initialized prior to tracking. Since the model shape coverage is fixed a priori, the incremental recovery of structure is decoupled from tracking, thereby limiting both processes in their scope and robustness. In this work, we describe a model-based framework that supports the automatic detection and integration of low-level geometric primitives (lines) incrementally. Such primitives are not explicitly captured in the initial model, but are moving consistently with its image motion. The consistency tests used to identify new structure are based on trinocular constraints between geometric primitives. The method allows not only an increase in the model scope, but also improves tracking accuracy by including the newly recovered features in its state estimation. The formulation is a step toward automatic model building, since it allows both weaker assumptions on the availability of a prior shape representation and on the number of features that would otherwise be necessary for entirely bottom-up reconstruction. We demonstrate the proposed approach on two separate image-based tracking domains, each involving complex 3D object structure and motion.     

A universal design resource for rich Internet applications based on design patterns

Rich Internet applications have removed most of the constraints of Web 1.0 while giving users more responsiveness and advanced browsing and interaction experiences. These new horizons, however, raise many challenges for people with disabilities or using limited hardware and software technologies, whose risk to be excluded from the benefits deriving from advanced web applications. To address this problem, WCAG 2.0 guidelines have been released as the newest World Wide Web Consortium recommendation for accessible web content, and WAI-ARIA is a candidate recommendation which provides reference specifications for accessible rich Internet applications. However, both specifications contain a huge amount of information that often discourages most web designers from dealing with accessibility issues. Moreover, guidelines are suitable and usually adopted to judge a design solution a posteriori, but they do not suggest how to face a design problem constructively. This paper proposes a design pattern language for accessibility. The language can be regarded as a universal design resource for helping web designers create accessible rich Internet applications compliant with the most recent standards. Knowledge representation through design patterns reflects the problem-solving approach usually followed by software and web designers, while pattern organization in a structured language aims to guide web designers throughout the design process. The language has been implemented as an accessible rich Internet application itself, thus allowing designers with disabilities to participate in web design. In order to evaluate the design pattern language, a three-step process was carried out including: (1) a heuristic analysis with a group of human–computer interaction experts, (2) a survey study with a group of web designers, and (3) a validation on the field with two designers who have been requested to apply the language in real design cases.     

Disjunctive databases for representing repairs

This paper addresses the problem of representing the set of repairs of a possibly inconsistent database by means of a disjunctive database. Specifically, the class of denial constraints is considered. We show that, given a database and a set of denial constraints, there exists a (unique) disjunctive database, called canonical, which represents the repairs of the database w.r.t. the constraints and is contained in any other disjunctive database with the same set of minimal models. We propose an algorithm for computing the canonical disjunctive database. Finally, we study the size of the canonical disjunctive database in the presence of functional dependencies for both subset-based repairs and cardinality-based repairs.     

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.     

Explanations for over-constrained problems using QuickXPlain with speculative executions

Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s QuickXPlain is a divide-and-conquer based algorithm for the detection of preferred minimal conflicts. In this article, we present a novel approach to the detection of such conflicts which is based on speculative programming. We introduce a parallelization of QuickXPlain and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized QuickXPlain version.

     

Random 3-SAT: The Plot Thickens

This paper presents an experimental investigation of the following questions: how does the average-case complexity of random 3-SAT, understood as a function of the order (number of variables) for fixed density (ratio of number of clauses to order) instances, depend on the density? Is there a phase transition in which the complexity shifts from polynomial to exponential in the order? Is the transition dependent or independent of the solver? Our experiment design uses three complete SAT solvers embodying different algorithms: GRASP, CPLEX, and CUDD. We observe new phase transitions for all three solvers, where the median running time shifts from polynomial in the order to exponential. The location of the phase transition appears to be solver-dependent. GRASP shifts from polynomial to exponential complexity near the density of 3.8, CPLEX shifts near density 3, while CUDD exhibits this transition between densities of 0.1 and 0.5. This experimental result underscores the dependence between the solver and the complexity phase transition, and challenges the widely held belief that random 3-SAT exhibits a phase transition in computational complexity very close to the crossover point.