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McCormick (Math Prog 10(1):147–175, 1976 ) provides the framework for convex/concave relaxations of factorable functions, via rules for the product of functions and compositions of the form F ∘ f , where F is a univariate function. Herein, the composition theorem is generalized to allow multivariate outer functions F , and theory for the propagation of subgradients is presented. The generalization interprets the McCormick relaxation approach as a decomposition method for the auxiliary variable method. In addition to extending the framework, the new result provides a tool for the proof of relaxations of specific functions. Moreover, a direct consequence is an improved relaxation for the product of two functions, at least as tight as McCormick’s result, and often tighter. The result also allows the direct relaxation of multilinear products of functions. Furthermore, the composition result is applied to obtain improved convex underestimators for the minimum/maximum and the division of two functions for which current relaxations are often weak. These cases can be extended to allow composition of a variety of functions for which relaxations have been proposed.

The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. Here we present a protein–DNA network between Arabidopsis thaliana transcription factors and secondary cell wall metabolic genes with gene expression regulated by a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. These interactions will serve as a foundation for understanding the regulation of a complex, integral plant component. The full complement of transcriptional regulators that affect synthesis of the plant secondary cell wall remains largely undetermined; here, the network of protein–DNA interactions controlling secondary cell wall synthesis of Arabidopsis thaliana is determined, showing that gene expression is regulated by a series of feed-forward loops to ensure that the secondary cell wall is deposited at the right time and in the right place. Secondary cell wall synthesis in Arabidopsis The plant cell wall determines cell shape and mediates communication with the cellular environment. But it is the secondary cell wall — deposited in various cell types including xylem — that is the main source of biomass used for biofuels and in the pulp and paper industry. The full complement of transcriptional regulators that affect biosynthesis of secondary cell wall remains largely undetermined. Siobhan Brady and colleagues describe a gene regulatory network involving hundreds of transcription factors that controls the formation of xylem in the plant Arabidopsis through protein–DNA interactions. Gene expression is regulated by a series of feed-forward loops to ensure that secondary cell wall is deposited at the right time and in the right place. The authors use their gene regulatory network to develop new hypotheses about the effect of abiotic stress, such as salinity and iron deprivation, on secondary cell wall gene regulation, and to validate these hypotheses.

We propose an algorithm for solving the surrogate dual of a mixed integer program. The algorithm uses a trust region method based on a piecewise affine model of the dual surrogate value function. A new and much more flexible way of updating bounds on the surrogate dual’s value is proposed, in which numerical experiments prove to be advantageous. A proof of convergence is given and numerical tests show that the method performance is better than a state of the art subgradient solver. Incorporation of the surrogate dual value as a cut added to the integer program is shown to greatly reduce solution times of a standard commercial solver on a specific class of problems.

The feasibility pump is a recent, highly successful heuristic for general mixed integer linear programming problems. We show that the feasibility pump heuristic can be interpreted as a discrete version of the proximal point algorithm. In doing so, we extend and generalize some of the fundamental results in this area to provide new supporting theory. We show that feasibility pump algorithms implicitly minimize a weighted combination of the objective and a term which penalizes lack of integrality. This function has many local minima, some of which correspond to feasible integral solutions; the feasibility pump's use of random restarts can be viewed as seeking to escape these local minima when they are not feasible integral solutions. This interpretation suggests alternative ways of incorporating restarts, one of which is the application of cutting planes. Numerical experiments with cutting planes show encouraging results on standard test libraries. [PUBLICATION ABSTRACT]

Animated demonstrations are increasingly used for presenting the functionality of various computer applications. Nevertheless, our understanding of whether and how students integrate this technology into their learning strategies remains limited. Although, several studies have examined animated demonstrations' learning efficiency, this study aims at investigating users' initial attitudes towards animated demonstrations as computer learning tools. Attitudes about knowledge sources play a determinative role for their acceptance. Quantitative and qualitative information was collected from forty-six interviews with students who used animated demonstrations for the first time. Students appraised animated demonstrations with regard to their authentic representation of task sequences, arguing that comprehension of the demonstrations did not entail intensive metacognitive burdens. On the contrary, students claimed that animated demonstrations had browsing inefficiencies and sometimes failed to satisfy individual learning needs. Interview transcripts revealed that students' attitudes were influenced by several factors, such as the nature of the computer application to be learnt, students' prior knowledge of that application, their prior learning practices, narrator's characteristics, simulated practice options and the procedural segmentation of the presentation. Results of the study can be exploited to enhance the design of educational applications that incorporate animated demonstrations.

This study investigates the effectiveness of two variants of a prompting strategy that guides students to focus on important issues when learning in an ill-structured domain. Students in three groups studied individually Software Project Management (SPM) cases for a week, using a web-based learning environment designed especially for this purpose. The first group (control) studied the cases without any prompting. The second group (\"writing mode\") studied the same cases, while prompted to provide written answers to a set of knowledge integration prompts meant to engage students in deeper processing of the material. The third group (\"thinking mode\") studied the cases, while prompted only to think of possible answers to the same question prompts. Results indicated that students in the writing condition group outperformed the others in both domain knowledge acquisition and knowledge transfer post-test items. Several students in the thinking condition group skipped the question prompts, while those that reported having reflected on the material were unable to achieve high performance comparable to the writing condition group. Overall, the study provides evidence that the implementation of prompting techniques in technology-enhanced learning environments may lead to improved outcomes, when combined with the requirement that students provide their answers in writing.

This study was designed to investigate the impact of question prompts that guide students to focus on context-related issues when learning through cases in an illstructured domain. Three groups of undergraduate students studied cases during a labsession time period using a web-based environment. The first group studied without any question prompts. The second group studied the same material while prompted to provide written answers to embedded questions in the cases. The third group studied while having only to think of possible answers for the question prompts. In this study, we explored how the questioning intervention affected students' conceptual knowledge of the domain and their problem-solving ability. Post-tests did not reveal significant statistical differences in the groups' performance, indicating that under specific study conditions the prompting impact is not traceable in the learning outcomes. This result, however, is discussed in the light of a previous study, which showed that this context-oriented prompting method had a beneficial effect on student learning in a prolonged study-time setting, where students were able to self-regulate their study activity.

In this paper the development methodology of a collaboration environment for public servants is presented. The methodology defines the importance of the specific environment for both the delivery of non-automated public services through one-stop e-government portals and for the self-maturation of Public Administration in digital transactions. The use of the collaborative environment can establish the bottom-up design of e-Government, supporting the discovery, evaluation, improvement and delivery of public services. On the other hand, the installation of a collaborative environment in Public Administration has many requirements such as the development of a proper legal framework for guiding cooperation and service execution. The use of the environment can support the diffusion of e-Government to both citizens and civil servants, while it can succeed in the modernization of Public Administration.

This work studies the influence of the Ag admixtures on the crystallization of the amorphous Fe75Si9B16 alloy, with the aid of electric and magnetic measurements. It is concluded that the solid solubility of the Ag in these alloys is very small, reaching 2 at% at most. Because of microsegregation, the presence of even these minimal admixtures accelerates the crystallization procedure.

The accurate identification of flow regimes in multiphase flow systems is of paramount importance in many engineering applications. This thesis explores the significance of flow regime identification using neural networks, specifically employing a self-organizing map (SOM) algorithm. The focus of this research is on the determination of bubble void fraction probability density function (PDF) using local conductivity probe measurements. The thesis begins by providing an overview of the importance of flow regime identification in understanding and predicting the behavior of multiphase flows. Various flow regimes such as bubbly flow, slug flow, annular flow, and others, are discussed highlighting their distinct characteristics and implications for system performance. The self-organizing map is introduced as a powerful neural network technique capable of identifying and classifying different flow regimes based on input parameters obtained from local conductivity probe measurements. The SOM algorithm is explained in detail, emphasizing its ability to learn and adapt to complex patterns in the data. To validate the effectiveness of the proposed approach, experimental measurements of local conductivity probe signals were conducted in a multiphase flow system. The obtained data was used to train and optimize a self-organizing map for flow regime identification. The bubble void fraction probability density function was calculated based on the local time-averaged void fraction measurements from the droplet-capable conductivity probe (DCCP-4). The analysis of the PDF provides valuable insights into the distribution and characteristics of bubbles within the multiphase flow system. These insights can enhance the understanding of bubble behavior, droplet behavior, interfacial phenomena and overall system performance. The thesis concludes with the classification results along with an error analysis conducted to highlight potential discrepancies in the tested results. Additionally, future research directions and potential improvements in the flow regime identification methodology are outlined.