Minimize the Maximum Duty in Multi-interface Networks

We consider devices equipped with multiple wired or wireless interfaces. By switching of various interfaces, each device might establish several connections. A connection is established when the devices at its endpoints share at least one active interface. Each interface is assumed to require an activation cost. In this paper, we consider two basic networking problems in the field of multi-interface networks. The first one, known as the Coverage problem, requires to establish the connections defined by a network. The second one, known as Connectivity problem, requires to guarantee a connecting path between any pair of nodes of a network. Both are subject to the constraint of keeping as low as possible the maximum cost set of active interfaces at each single node. We study the problems of minimizing the maximum cost set of active interfaces among the nodes of the network in order to cover all the edges in the first case, or to ensure connectivity in the second case. We prove that the Coverage problem is NP-hard for any fixed Δ≥5 and k≥16, with Δ being the maximum degree, and k being the number of different interfaces among the network. We also show that, unless P=NP, the problem cannot be approximated within a factor of ηln?Δ, for a certain constant η. We then provide a general approximation algorithm which guarantees a factor of O((1+b)ln?Δ), with b being a parameter depending on the topology of the input graph. Interestingly, b can be bounded by a constant for many graph classes. Other approximation and exact algorithms for special cases are presented. Concerning the Connectivity problem, we prove that it is NP-hard for any fixed Δ≥3 and k≥10. Also for this problem, the inapproximability result holds, that is, unless P=NP, the problem cannot be approximated within a factor of ηln?Δ, for a certain constant η. We then provide approximation and exact algorithms for the general problem and for special cases, respectively.     

Influence of participation, facilitator styles, and metacognitive reflection on knowledge building in online university courses

Understanding how to foster knowledge building in online and blended learning environments is a key for computer-supported collaborative learning research. Knowledge building is a deeply constructivist pedagogy and kind of inquiry learning focused on theory building. A strong indicator of engagement in knowledge building activity is the socio-cognitive dynamic of epistemic agency, in which students exercise a higher level of agency for setting forth their ideas and negotiating fit with those of others rather than relying on their teacher. The purpose of this paper is to investigate the influence of (a) levels of participation, (b) facilitator styles and (c) metacognitive reflection on knowledge building in two blended, post-secondary education contexts. A study of a total of 67 undergraduate students suggest that high levels of participation, a supportive facilitator style, and ample opportunities for metacognitive reflection on the students’ own participation strategies are most conducive for fostering epistemic agency for knowledge building. Implications of these results for research and instructional design of online courses are discussed.     

Quantification of secondary structure prediction improvement using multiple alignments

The use of multiple sequence alignments for secondary structurepredictions is analysed. Seven different protein families, containingonly sequences of known structure, were considered to providea range of alignment and prediction conditions. Using alignmentsobtained by spatial superposition of main chain atoms in knowntertiary protein structures allowed a mean of 8% in secondarystructure prediction accuracy, when compared to those obtainedfrom the individual sequences. Substitution of these alignmentsby those determined directly from an automated sequence alignmentalgorithm showed variations in the prediction accuracy whichcorrelated with the quality of the multiple alignments and distanceof the primary sequence. Secondary structure predictions canbe reliably improved using alignments from an automatic alignmentprocedure with a mean increase of 6.87percnt;, giving an overallprediction accuracy of 68.5%, if there is a minimum of 25% sequenceidentity between all sequences in a family.     

Nanostructured Na-ion and Li-ion anodes for battery application: A comparative overview

This paper offers a comprehensive overview on the role of nanostructures in the development of advanced anode materials for application in both lithium and sodium-ion batteries. In particular, this review highlights the differences between the two chemistries, the critical effect of nanosize on the electrode performance, as well as the routes to exploit the inherent potential of nanostructures to achieve high specific energy at the anode, enhance the rate capability, and obtain a long cycle life. Furthermore, it gives an overview of nanostructured sodium- and lithium-based anode materials, and presents a critical analysis of the advantages and issues associated with the use of nanotechnology.

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An Overview and Future Perspectives of Aluminum Batteries

A critical overview of the latest developments in the aluminum battery technologies is reported. The substitution of lithium with alternative metal anodes characterized by lower cost and higher abundance is nowadays one of the most widely explored paths to reduce the cost of electrochemical storage systems and enable long‐term sustainability. Aluminum based secondary batteries could be a viable alternative to the present Li‐ion technology because of their high volumetric capacity (8040 mAh cm^?3 for Al vs 2046 mAh cm^?3 for Li). Additionally, the low cost aluminum makes these batteries appealing for large‐scale electrical energy storage. Here, we describe the evolution of the various aluminum systems, starting from those based on aqueous electrolytes to, in more details, those based on non‐aqueous electrolytes. Particular attention has been dedicated to the latest development of electrolytic media characterized by low reactivity towards other cell components. The attention is then focused on electrode materials enabling the reversible aluminum intercalation‐deintercalation process. Finally, we touch on the topic of high‐capacity aluminum‐sulfur batteries, attempting to forecast their chances to reach the status of practical energy storage systems.     

Recommendation of RILEM Technical Committee 250-CSM: Test method for Textile Reinforced Mortar to substrate bond characterization

Textile Reinforced Mortar (TRM), also known as Fabric Reinforced Mortar or Fabric Reinforced Cementitious Matrix, composites are an emerging technology for the external repair and strengthening of existing structures. For most applications, the effectiveness of the TRM reinforcement relies on its bond performance. This recommendation identifies the best practice to characterize the bond behaviour of TRM. A shear bond test method is proposed to determine the peak axial stress (associated with the maximum load that can be transferred from the structural member to the externally bonded TRM reinforcement), the stress–slip relationship and the failure mode that controls the TRM-to-substrate load transfer capacity. Guidelines on specimen manufacturing, experimental setup, test execution, and determination of test results are provided.     

Plasmon hybridization engineering in self-organized anisotropic metasurfaces

Nano Research - The engineering of self-organized plasmonic metasurfaces is demonstrated using a maskless technique with defocused ion-beam sputtering and kinetically controlled deposition. The...     

Learning optimal trajectories for non-holonomic systems

Many advanced robotic systems are subject to non-holonomic constraints, e.g. wheeled mobile robots, space manipulators and multifingered robot hands. Steering these mechanisms between configurations in the presence of perturbations is a difficult problem. In fact, the divide et impera strategy (first plan a trajectory, then track it by feedback) has a fundamental drawback in this case: due to the peculiar control properties of non-holonomic systems, smooth feedback cannot provide tracking of the whole trajectory. As a result, it would be necessary to give up either accuracy in the final positioning or predictability of the actual motion. We pursue here a different approach which does not rely on a separation between planning and control. Based on the learning control paradigm, a robust steering scheme is devised for systems which can be put in chained form, a canonical structure for non-holonomic systems. By overparametrizing the control law, other performance goals can be met, typically expressed as cost functions to be minimized along the trajectory. As a case study, we consider the generation of robust optimal trajectories for a car-like mobile robot, with criteria such as total length, maximum steering angle, distance from workspace obstacles, or error with respect to an offline planned trajectory.     

Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster

This study is meant to characterize the serum proteome in a small geographical cluster of sporadic ALS subjects originating from a restricted geographical area and sharing the same environmental exposure, in a broader context of evaluating the relevance of environmental factors to disease onset, status, and progression. An Artificial Neural Network based software is used to compare the relative abundance of proteins identified as different (by means of bi‐dimensional electrophoresis and mass spectrometry) in the serum proteome of patients and age‐matched healthy controls. The patient's group is characterized by altered levels of acute phase reactants and of proteins involved in lipid homeostasis, along with over‐representation of the APOE*4 allele. Characterization of the serum proteome in a small cluster of sporadic ALS patients, originating from a geographically restricted area with a high prevalence of the disease and evaluation of the results with software based on artificial neural networks, highlights the association of the relative abundance of some proteins (most notably, acute phase reactants and lipid homeostasis proteins) with the disease presence and status.