Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.     

Payoff-monotonic game dynamics and the maximum clique problem

Evolutionary game-theoretic models and, in particular, the so-called replicator equations have recently proven to be remarkably effective at approximately solving the maximum clique and related problems. The approach is centered around a classic result from graph theory that formulates the maximum clique problem as a standard (continuous) quadratic program and exploits the dynamical properties of these models, which, under a certain symmetry assumption, possess a Lyapunov function. In this letter, we generalize previous work along these lines in several respects. We introduce a wide family of game-dynamic equations known as payoff-monotonic dynamics, of which replicator dynamics are a special instance, and show that they enjoy precisely the same dynamical properties as standard replicator equations. These properties make any member of this family a potential heuristic for solving standard quadratic programs and, in particular, the maximum clique problem. Extensive simulations, performed on random as well as DIMACS benchmark graphs, show that this class contains dynamics that are considerably faster than and at least as accurate as replicator equations. One problem associated with these models, however, relates to their inability to escape from poor local solutions. To overcome this drawback, we focus on a particular subclass of payoff-monotonic dynamics used to model the evolution of behavior via imitation processes and study the stability of their equilibria when a regularization parameter is allowed to take on negative values. A detailed analysis of these properties suggests a whole class of annealed imitation heuristics for the maximum clique problem, which are based on the idea of varying the parameter during the imitation optimization process in a principled way, so as to avoid unwanted inefficient solutions. Experiments show that the proposed annealing procedure does help to avoid poor local optima by initially driving the dynamics toward promising regions in state space. Furthermore, the models outperform state-of-the-art neural network algorithms for maximum clique, such as mean field annealing, and compare well with powerful continuous-based heuristics.     

Diet supplementation with donkey milk upregulates liver mitochondrial uncoupling,reduces energy efficiency and improves antioxidant and antiinflammatory defences in rats

Dietary PUFA, mainly those of the n‐3 family, are known to play essential roles in the maintenance of energy balance and in the reduction of body fat deposition through the upregulation of mitochondrial uncoupling that is the main source of reactive oxygen species. We hypothesized that rat supplementation with raw donkey's milk (DM), characterized by low‐fat content and higher n3:n6 ratio, may affect energy balance, lipid metabolism, and prooxidant status as compared to animals treated with cow's milk. In the present study, the effects of drinking raw DM (for 4 weeks) on energy balance, lipid metabolism, antiinflammatory, and antioxidant/detoxifying defences was compared to that produced by rat intake of an iso‐energetic amount of raw cow's milk. The hypolipidemic effect produced by DM paralleled with the enhanced mitochondrial activity/proton leakage and with the increased activity or expression of mitochondrial markers namely, carnitine palmitoyl transferase and uncoupling protein 2. The association of decreased energy efficiency with reduced proinflammatory signs (TNF‐α and LPS levels) with the significant increase antioxidant (total thiols) and detoxifying enzyme activities (glutathione‐S‐transferase NADH quinone oxidoreductase) in DM‐treated animals, indicated that beneficial effects were attributable, at least in part, to the activation of nuclear factor 2 erythroid‐related factor 2 pathway.     

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.     

Control of Convection Patterning and Intensity in Shallow Cavities by Harmonic Vibrations

In this article the interplay among different types of flow (i.e. induced by driving forces of a different nature) is presented as a possible and “natural” means to control convection patterning and strength in shallow rectangular cavities of finite extent (A =length/height =4) filled with a low Prandtl number liquid (silicon, Pr =0.01). A variety of results concerning the possible spatial structure of the “mixed” states of steady Buoyant, Marangoni and Vibrational convection are discussed with the express intent of supporting the optimization of future experiments to be performed onboard the International Space Station.     

Nanoporous microtubes obtained from a Cu-Ni metallic wire

Nanoporous microtubes of a nickel-copper alloy were obtained from a Cu-44Ni-1Mn (wt%) commercial wire (200 μm diameter). A new synthesis method was established through three steps: 1) partial oxidation of the wire at 1173 K in air, 2) removal of the inner unoxidized core by chemical etching, 3) reduction in 10 bar hydrogen atmosphere. During oxidation, the segregation of Cu and Ni occurred because of their different diffusion coefficients in the corresponding oxides. As a consequence, pores were formed by Kirkendall effect and due to selective chemical etching of the different oxides. Additional porosity formed because of volume contraction during reduction with hydrogen. After reduction, the microtube shows a composition gradient from the inner wall (almost pure nickel) to the outer wall (almost pure copper). The process allowed to obtain microtubes with tuneable wall thickness and inner pores around 180 ± 80 nm. The morphological features developed suggest improved capillarity properties for applications in MEMS.     

Computer-aided activity planning (CAAP) in large-scale projects with an application in the yachting industry

The present paper provides the schema for an innovative and modular computer-based approach to the planning of activities in large-scale projects. Such projects are characterized by tens of thousands of tasks, which are consequently burdensome and difficult to plan manually. This is true to the point that in many shipyards only a low level of detail is used and poor planning is generally performed. The proposed approach is called computer-aided activity planning (CAAP), and an application in the yachting industry is shown to demonstrate its effectiveness. In particular, the so-called outfitting planning problem is faced. The CAAP system, taking into account the available shipyard resources and the knowledge on the building rules is able to automatically define, sequence, and schedule the activities of the whole outfitting process acting as a “planning configurator”. Moreover, it allows the industry-specific knowledge to be stored, maintained and shared within the (extended) organization. Owing to these “building blocks”, plans can be defined accurately and in a shorter time starting from pre-defined templates, with particular impact on lead times whenever variations to complex projects are needed. Finally, to verify the actual capabilities of the approach, the CAAP was implemented within a prototypical software called NautiCAAP.