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Optimal energy management of a small-size building via hybrid model predictive control
No full textAbstract This paper presents the design of a Model Predictive Control (MPC) scheme to optimally manage the thermal and electrical subsystems of a small-size building (“smart house”), with the objective of minimizing the expense for buying energy from the grid, while keeping the room temperature within given time-varying bounds. The system, for which an experimental prototype has been built, includes {PV} panels, solar collectors, a battery pack, an electrical heater in a thermal storage tank, and two pumps on the solar collector and radiator hydraulic circuits. The presence of binary control inputs together with continuous ones naturally leads to using a hybrid dynamical model, and the {MPC} controller solves a mixed-integer linear program at each sampling instant, relying on weather forecast data for ambient temperature and solar irradiance. The procedure for controller design is reported with focus on the specific application, and the proposed method is successfully tested on the experimental site
Agriculture and Food Global Value Chains in Sub-Saharan Africa: Does bilateral trade policy impact on backward and forward participation?
No full textThe most recent literature on international trade highlights the key role of global value chains (GVCs) in structural transformation, development and growth. The common perception is that Africa, unlike most Latin American and Asian countries, has neither been able to intercept the main changes in trade patterns nor enter massively into global production networks. This work provides some insight into this topic. Using the EORA Input-Output Tables, we analyze whether bilateral import tariffs and shifts in trade regimes associated with regional trade agreements affect the backward participation (i.e., the use of foreign inputs for exports) and forward participation (i.e., the use of domestic intermediates in third country exports) of the SSA countries’ agriculture and food GVCs. Our results show that, despite their low world trade shares, GVC participation in SSA economies is increasing over time, mainly upstream as suppliers of unprocessed inputs. Furthermore, we show that the value added demand for SSA agricultural products primarily originates from the EU and emerging countries rather than from regional partners. Finally, by making use of a “gravity-like” identification strategy, we also find evidence that bilateral trade protection significantly affects GVC backward and forward participation in agriculture and food. These results call for a refinement of trade policy priorities in SSA
Knowledge Spillovers through Networks of Scientists
Get PDFIn this paper I directly test the hypothesis that interactions between inventors of different
firms drive knowledge spillovers. I construct a network of publicly traded companies
in which each link is a function of the relative proportion of two firms’ inventors who
have former patent collaborators in both organizations. I use this measure to weigh the
impact of R&D performed by each firm on the productivity and innovation outcomes
of its network linkages. An empirical concern is that the resulting estimates may reflect
unobserved, simultaneous determinants of firm performance, network connections and
external R&D. I address this problem with an innovative IV strategy, motivated by a
game-theoretic model of firm interaction. I instrument the R&D of one firm’s connections
with that of other firms that are sufficiently distant in network space. With the
resulting spillover estimates, I calculate that among firms connected to the network
the marginal social return of R&D amounts to approximately 112% of the marginal
private retur
The Indirect Effects of FDI on Trade: A Network Perspective
No full textThe relationship between international trade and foreign direct invest-
ment (FDI) is one of the main features of globalization. In this paper
we investigate the effects of FDI on trade from a network perspective,
since FDI takes not only direct but also indirect channels from origin to
destination countries because of firms' incentive to reduce tax burden,
to minimize coordination costs, and to break barriers to market entry.
We use a unique data set of international corporate control as a measure
of stock FDI to construct a corporate control network (CCN) where the
nodes are the countries and the edges are the corporate control relation-
ships. Based on the CCN, the network measures, i.e., the shortest path
length and the communicability, are computed to capture the indirect
channel of FDI. Empirically we find that corporate control has a positive
effect on trade both directly and indirectly. The result is robust with dif-
ferent specifications and estimation strategies. Hence, our paper provides
strong empirical evidence of the indirect effects of FDI on trade. More-
over, we identify a number of interplaying factors such as regional trade
agreements and the region of Asia. We also find that the indirect effects
are more pronounced for manufacturing sectors than for primary sectors
such as oil extraction and agriculture
Not in one metric: Neuroticism modulates different resting state metrics within distinctive brain regions
No full textIntroduction Neuroticism is a complex personality trait encompassing diverse aspects. Notably, high levels of neuroticism are related to the onset of psychiatric conditions, including anxiety and mood disorders. Personality traits are stable individual features; therefore, they can be expected to be associated with stable neurobiological features, including the Brain Resting State (RS) activity as measured by fMRI. Several metrics have been used to describe {RS} properties, yielding rather inconsistent results. This inconsistency could be due to the fact that different metrics portray different {RS} signal properties and that these properties may be differently affected by neuroticism. To explore the distinct effects of neuroticism, we assessed several distinct metrics portraying different {RS} properties within the same population. Method Neuroticism was measured in 31 healthy subjects using the Zuckerman-Kuhlman Personality Questionnaire; {RS} was acquired by high-resolution fMRI. Using linear regression, we examined the modulatory effects of neuroticism on {RS} activity, as quantified by the Amplitude of low frequency fluctuations (ALFF, fALFF), regional homogeneity (REHO), Hurst Exponent (H), global connectivity (GC) and amygdalae functional connectivity. Results Neuroticism modulated the different metrics across a wide network of brain regions, including emotional regulatory, default mode and visual networks. Except for some similarities in key brain regions for emotional expression and regulation, neuroticism affected different metrics in different ways. Discussion Metrics more related to the measurement of regional intrinsic brain activity (fALFF, {ALFF} and REHO), or that provide a parsimonious index of integrated and segregated brain activity (HE), were more broadly modulated in regions related to emotions and their regulation. Metrics related to connectivity were modulated across a wider network of areas. Overall, these results show that neuroticism affects distinct aspects of brain resting state activity. More in general, these findings indicate that a multiparametric approach may be required to obtain a more detailed characterization of the neural underpinnings of a given psychological trait
School Infrastructure Spending and Educational Outcomes in Northern Italy
No full textWe explore whether investment in public school infrastructure affects
students' achievement. We use data on extra funding to public
high schools after the 2012 Northern Italy earthquake and apply a
quasi-experimental design and an instrumental variable strategy. We find that spending on school infrastructure increases standardized test
scores in mathematics and Italian language, and the effect is stronger
for lower-achieving students and in mathematics. These results provide
evidence in favor of a positive impact of capital spending in improving
the learning environment and performances of high school
students
Inferring monopartite projections of bipartite networks: an entropy-based approach
No full textBipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain a (monopartite) projection on the layer of interest, which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any two nodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, here we consider a set of four null models, defined within the exponential random graph framework. Our algorithm outputs a matrix of link-specific p -values, from which a validated projection is straightforwardly obtainable, upon running a multiple hypothesis testing procedure. Finally, we test our method on an economic network (i.e. the countries-products World Trade Web representation) and a social network (i.e. MovieLens, collecting the users’ ratings of a list of movies). In both cases non-trivial communities are detected: while projecting the World Trade Web on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on the films layer allows clusters of movies whose affinity cannot be fully accounted for by genre similarity to be individuated
ERODE: A Tool for the Evaluation and Reduction of Ordinary Differential Equations
No full textWe present ERODE, a multi-platform tool for the solution and exact reduction of systems of ordinary differential equations (ODEs). ERODE supports two recently introduced, complementary, equivalence relations over ODE variables: forward differential equivalence yields a self-consistent aggregate system where each ODE gives the cumulative dynamics of the sum of the original variables in the respective equivalence class. Backward differential equivalence identifies variables that have identical solutions whenever starting from the same initial conditions. As back-end ERODE uses the well-known Z3 SMT solver to compute the largest equivalence that refines a given initial partition of ODE variables. In the special case of ODEs with polynomial derivatives of degree at most two (covering affine systems and elementary chemical reaction networks), it implements a more efficient partition-refinement algorithm in the style of Paige and Tarjan. ERODE comes with a rich development environment based on the Eclipse plug-in framework offering: (i) seamless project management; (ii) a fully-featured text editor; and (iii) importing-exporting capabilities
Mapping social dynamics on Facebook: The Brexit debate
No full textAbstract Nowadays users get informed and shape their opinion through social media. However, the disintermediated access to contents does not guarantee quality of information. Selective exposure and confirmation bias, indeed, have been shown to play a pivotal role in content consumption and information spreading. Users tend to select information adhering (and reinforcing) their worldview and to ignore dissenting information. This pattern elicits the formation of polarized groups – i.e., echo chambers – where the interaction with like-minded people might even reinforce polarization. In this work we address news consumption around Brexit in {UK} on Facebook. In particular, we perform a massive analysis on more than 1 million users interacting with Brexit related posts from the main news providers between January and July 2016. We show that consumption patterns elicit the emergence of two distinct communities of news outlets. Furthermore, to better characterize inner group dynamics, we introduce a new technique which combines automatic topic extraction and sentiment analysis. We compare how the same topics are presented on posts and the related emotional response on comments finding significant differences in both echo chambers and that polarization influences the perception of topics. Our results provide important insights about the determinants of polarization and evolution of core narratives on online debating
