On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

Monolayer graphene is now produced at significant yields, by liquid phase exfoliation of graphites in solvents. This has increased the interest in molecular simulation studies to give new insights in the field. We use decoupling simulations to compute the exfoliation free energy of graphenes in a liquid environment. Starting from a bilayer graphene configuration, we decouple the Van der Waals interactions of a graphene monolayer in the presence of saline water. Then, we introduce the monolayer back into water by coupling its interactions with water molecules and ions. A different approach to compute the graphene exfoliation free energy is to use umbrella sampling. We apply umbrella sampling after pulling the graphene monolayer on the shear direction up to a distance from a bilayer. We show that the decoupling and umbrella methods give highly consistent free energy results for three bilayer graphene samples with different size. This strongly suggests that the systems in both methods remain closely in equilibrium as we move between the states before and after the exfoliation. Therefore, the amount of nonequilibrium work needed to peel the two layers apart is minimized efficiently.     

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods     

Broadband multimedia on the move with DVB-H

DVB-H is the newly standardized extension to DVB-T, aiming at the provision of IP datacasting (IPDC) services to mobile terminals. This tutorial paper outlines the structure of an interactive DVB-H platform and presents in brief the technical advances of the new specification. It also discusses some interesting scenarios of interactive and non-interactive services which can be directly deployed with the use of the DVB-H technology either as a stand-alone broadcast network or as a complement to existing cellular (2G/3G/WLAN) infrastructures.     

Microfabricated Ion-Selective Transistors with Fast and Super-Nernstian Response

Transistor-based ion sensors have evolved significantly, but the best-performing ones rely on a liquid electrolyte as an internal ion reservoir between the ion-selective membrane and the channel. This liquid reservoir makes sensor miniaturization difficult and leads to devices that are bulky and have limited mechanical flexibility, which is holding back the development of high-performance wearable/implantable ion sensors. This work demonstrates microfabricated ion-selective organic electrochemical transistors (OECTs) with a transconductance of 4 mS, in which a thin polyelectrolyte film with mobile sodium ions replaces the liquid reservoir. These devices are capable of selective detection of various ions with a fast response time (≈1 s), a super-Nernstian sensitivity (85 mV dec⁻¹), and a high current sensitivity (224 µA dec⁻¹), comparing favorably to other ion sensors based on traditional and emerging materials. Furthermore, the ion-selective OECTs are stable with highly reproducible sensitivity even after 5 months. These characteristics pave the way for new applications in implantable and wearable electronics.     

Prediction of student’s mood during an online test using formula-based and neural network-based method

Building computerized mechanisms that will accurately, immediately and continually recognize a learner’s affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence could be combined so as to optimize inferences about affective states during an online self-assessment test. A formula-based method has been developed for the prediction of students’ mood, and it was tested using data emanated from experiments made with 153 high school students from three different regions of a European country. The same set of data is analyzed developing a neural network method. Furthermore, the formula-based method is used as an input parameter selection module for the neural network method. The results vindicate to a great degree the formula-based method’s assumptions about student’s mood and indicate that neural networks and conventional algorithmic methods should not be in competition but complement each other for the development of affect recognition systems. Moreover, it becomes apparent that neural networks can provide an alternative for and improvements over tutoring systems’ affect recognition methods.     

Exploiting semantic proximities for content search over p2p networks

In this paper, we address the issue of content search over peer-to peer networks. We use the concept of semantic proximity that exploits the commonalities of interests exhibited among peer users so as to decompose the network into semantic clusters. We initially define search entropy, as a metric indicating the average number of packets required to locate the requested content. Then, spectral clustering is used to organize the peer nodes into semantic clusters so that (a) the probability that a node locates content within its own cluster is maximized, while simultaneously; (b) the respective probability of finding this content outside this cluster is minimized. The proposed semantic partitioning algorithm is then extended into a hierarchical two-tier scheme, in which practical issues arising for the deployment of a peer-to-peer (p2p) application can be more easily addressed. After the system has been initialized, a dynamic algorithm places new users that join the p2p network into appropriately selected clusters and also handles peer departures without the need for matrix eigen decomposition process which is necessary for the assessment of the initial static partitioning. Our experimental results validate that (a) our static partitioning outperforms traditional and novel search techniques and (b) our dynamic algorithm is able to efficiently track the system’s progression maintaining the search entropy close to the initially assessed levels.     

Online shape learning using binary search trees

In this paper we propose an online shape learning algorithm based on the self-balancing binary search tree data structure for the storage and retrieval of shape templates. This structure can also be used for classification purposes. We introduce a similarity measure with which we can make decisions on how to traverse the tree and even backtrack through the search path to find more candidate matches. Then we describe every basic operation a binary search tree can perform adapted to such a tree of shapes. Note that as a property of binary search trees, all operations can be performed in O(logn)$O(\log n)$ time and are very efficient. Finally, we present experimental data evaluating the performance of the proposed algorithm and demonstrating the suitability of this data structure for the purpose it was designed to serve.     

Hybrid control synthesis for eventuality specifications using level set methods

This paper is concerned with the extraction of controllers for hybrid systems with respect to eventuality specifications. Given a hybrid system modelled by a hybrid automaton and a target set of states, the objective is to compute the maximal set of initial states together with the hybrid control policy such that all the trajectories of the controlled system reach the target in finite time. Due to the existence of set-valued disturbance inputs, the problem is studied in a game-theoretic framework. Having shown that a least restrictive solution does not exist, we propose a dynamic programming algorithm that computes the maximal initial set and a controller with the desired property. To implement the algorithm, reachable sets of pursuit-evasion differential games need to be computed. For that reason level set methods are employed, where the boundary of the reachable set is characterized as the zero level set of a Hamilton–Jacobi equation. The procedure for the numerical extraction of the controller is presented in detail and examples illustrate the methodology. Finally, to demonstrate the practical character of our results, a control design problem in the benchmark system of the batch evaporator is considered as an eventuality synthesis problem and solved using the proposed methodology.     

Supervisory target control for hybrid systems

The problem of systematically synthesizing supervisory control laws that satisfy eventuality and efficiency requirements for hybrid systems modelled by hybrid automata is considered. Here, the efficiency requirement is specified by weighting the discrete transitions of the system. The optimization of the efficiency requirement is considered in the min-max sense due to the existence of disturbance inputs. Adopting a game theoretic approach, the high priority eventuality requirement is considered first and the class of controls, in which the low priority efficiency requirement should be optimized, is obtained. Then, a dynamic programming algorithm, which determines the value function of the optimization problem, is derived. A synthesis problem on a simplified batch process plant is considered to illustrate a potential application of the approach.     

Truncated chlorophyll antenna size of the photosystems—a practical method to improve microalgal productivity and hydrogen production in mass culture

Unicellular microalgae hold the promise of commercial exploitation in mass culture for hydrogen and biomass production. In any microalgal production system, the achievable photosynthetic productivity and light utilization efficiency of the algae are the single most important factors in the determination of cost. Microalgal mass cultures growing under full sunlight have a low per chlorophyll (Chl) productivity since, at high photon flux densities, the rate of photon absorption by the Chl antenna far exceeds the rate at which photons can be utilized for photosynthesis. Excess photons are dissipated as fluorescence or heat. Up to 80% of absorbed photons could thus be wasted, reducing light conversion efficiencies and cellular productivity to fairly low levels. This shortcoming could possibly be alleviated by the development of microalgal strains with a limited number of Chl molecules in the light-harvesting antenna of their photosystems, i.e., strains that have a truncated Chl antenna size. It is expected that individually, such microalgae will not be able to saturate rates of photosynthesis and, thus, will not be subject to wasteful dissipation of excitation energy. In turn, the productivity of the mass culture will be improved. The method of choice to reach the objective of a “truncated light-harvesting Chl antenna” size (tla) employed DNA insertional and chemical mutagenesis of the unicellular green algae Chlamydomonas reinhardtii and Dunaliella salina, followed by a rigorous screening protocol to identify mutants with a smaller light-harvesting Chl antenna size. Molecular and genetic analyses of isolated tla strains were performed. Biochemical and physiological analyses in terms of photosynthetic productivity and light conversion efficiencies are presented. The results show that a truncated Chl antenna size of PSII is more important than that of PSI in terms of the photosynthetic productivity of a mass culture. A list of genes that confer a “truncated light-harvesting Chl antenna” size to green algae is being compiled for future application in algal hydrogen and biomass production.