Bargaining rationale for cooperative generic advertising

The beggar-thy-neighbour aspect of commodity advertising means that benefits to one commodity from advertising come at the expense of other commodities. The effect can be mitigated by cooperation among groups as shown by Alston, Freebairn and James (AFJ). A drawback to AFJ’s analysis is that some cooperative outcomes require side payments from one producer group to another. This paper offers a bargaining solution as an alternative to cooperation in the case where cooperative side payments would be needed. We show that while bargaining without side payments is not as effective as cooperation at reducing beggar-thy-neighbour effects, it is a welfare improving alternative to non-cooperation and is likely more practical in many situations.Marketing,

Beyond operator-precedence grammars and languages

Operator Precedence Languages (OPL) are deterministic context-free and have desirable properties. OPL are parallely parsable, and, when structurally compatible, are closed under Boolean operations, concatenation and star; they include the Input Driven languages. OPL use three relations between two terminal symbols, to assign syntax structure to words. We extend such relations to k-tuples of consecutive symbols, in agreement with strictly locally testable regular languages. For each k, the new corresponding class of Higher-order Operator Precedence languages properly includes the OPL and enjoy many of their properties. OPL are a strict hierarchy based on k, which contains maximal languages

APERIODICITY, STAR-FREENESS, AND FIRST-ORDER LOGIC DEFINABILITY OF OPERATOR PRECEDENCE LANGUAGES

A classic result in formal language theory is the equivalence among non-counting, or aperiodic, regular languages, and languages defined through star-free regular expressions, or first-order logic. Past attempts to extend this result beyond the realm of regular languages have met with difficulties: for instance it is known that star-free tree languages may violate the non-counting property and there are aperiodic tree languages that cannot be defined through first-order logic. We extend such classic equivalence results to a significant family of deterministic context-free languages, the operator-precedence languages (OPL), which strictly includes the widely investigated visibly pushdown, alias input-driven, family and other structured context-free languages. The OP model originated in the ’60s for defining programming languages and is still used by high performance compilers; its rich algebraic properties have been investigated initially in connection with grammar learning and recently completed with further closure properties and with monadic second order logic definition. We introduce an extension of regular expressions, the OP-expressions (OPE) which define the OPLs and, under the star-free hypothesis, define first-order definable and non-counting OPLs. Then, we prove, through a fairly articulated grammar transformation, that aperiodic OPLs are first-order definable. Thus, the classic equivalence of star-freeness, aperiodicity, and first-order definability is established for the large and powerful class of OPLs. We argue that the same approach can be exploited to obtain analogous results for visibly pushdown languages too

Seismic Vulnerability Evaluation of a Historical Masonry Tower: Comparison between Different Approaches

Throughout the last few decades, the scientific community has paid great attention to the structural safety of historical masonry constructions, which have high vulnerability with respect to seismic activities. Masonry towers are very widespread in Italy and represent an important part of the built heritage to be preserved. Different numerical methods with different levels of refinement were developed in the literature to evaluate their seismic performance. The present study shows a practical application of the seismic vulnerability evaluation of a masonry tower using different approaches. The aim is to provide practical suggestions to engineers for the successful evaluation of the performance of masonry towers under seismic loads. An in situ survey was performed to characterize the geometry of the structure and its constitutive material. All the collected information was introduced in a building information model, later used to generate different finite element models for the structural analyses. The global capacity of the structure was evaluated using three different models with different levels of complexity: the first simplified model is made of beam elements with cross-sections discretized in fibers; the second model is made of shell elements and uses a concrete damage plasticity model to describe the nonlinear masonry behavior; the third model adopts solid elements with a concrete smeared crack constitutive law. A preliminary eigen-frequency analysis is performed on the shell model to obtain some basic information about the structural behavior. Nonlinear static analyses were carried out for each model to understand the response of the tower under seismic loads, highlighting the main differences between the approaches. The behavior factor was evaluated on the basis of the analyses results and compared with the ones suggested by the Italian building code. The results showed that the towers do not satisfy the seismic demand required by the standards for all the considered models. Furthermore, the behavior factor calculated according to the Italian design code is overestimated, while the one evaluated by the simplified model is underestimated due to the neglection of the shear behavior. From all the analyzed configurations, the shell model resulted as a good compromise between reliable results and computation efficiency

Exploitation of dynamic simulation to investigate the effectiveness of the Smart Readiness Indicator: application to the Energy Center building of Turin

To achieve the energy and emissions reduction goals for the building sector, actions are needed to improve energy efficiency and occupants’ wellbeing. To increase the uptake of smart technologies and the awareness upon their benefits, in line with the smart building revolution that is starting, the EPBD recast introduced the Smart Readiness Indicator (SRI) as a tool to evaluate the capability of buildings to easily adapt to both energy systems and occupants’ needs. However, there is a growing interest in studying the SRI features in terms of performance assessment, and, thus, dynamic simulation models can be exploited to better analyze its points of strength and weakness. The Energy Center building of Turin was chosen as case study. By means of EnergyPlus modeling, the current situation was simulated, as well as different scenarios of energy management and control, evaluating to what extent these actions can influence the overall SRI assessment. The analysis allowed to deepen and comment on the effectiveness of the SRI of being a real tool of building behavior assessment, able to link the indicator itself with the energy needs of the building and to understand if and how the indicator is sensible to energy needs variations

Laboratory evaluation of commercial interferon preparations

The antiviral, antiproliferative and natural killer-cell (NKC) stimulatory activities of four commercial therapeutic interferon preparations were assayed in our laboratory. The antiviral and antiproliferative activities of each preparation were relatively similar, but an unexpectedly high NKC stimulatory activity was foupd in one ofthem. In-house determination of antiviral activity and evaluation of the antiprolifera'tive and NKC stimulation potential of-"interferon preparations are. essential before rational clinical trials of this agent are carried out

Augmenting photometric redshift estimates using spectroscopic nearest neighbours

As a consequence of galaxy clustering, close galaxies observed on the plane of the sky should be spatially correlated with a probability that is inversely proportional to their angular separation. In principle, this information can be used to improve photometric redshift estimates when spectroscopic redshifts are available for some of the neighbouring objects. Depending on the depth of the survey, however, this angular correlation is reduced by chance projections. In this work, we implement a deep-learning model to distinguish between apparent and real angular neighbours by solving a classification task. We adopted a graph neural network architecture to tie together photometry, spectroscopy, and the spatial information between neighbouring galaxies. We trained and validated the algorithm on the data of the VIPERS galaxy survey, for which photometric redshifts based on spectral energy distribution are also available. The model yields a confidence level for a pair of galaxies to be real angular neighbours, enabling us to disentangle chance superpositions in a probabilistic way. When objects for which no physical companion can be identified are excluded, all photometric redshift quality metrics improve significantly, confirming that their estimates were of lower quality. For our typical test configuration, the algorithm identifies a subset containing ~75% high-quality photometric redshifts, for which the dispersion is reduced by as much as 50% (from 0.08 to 0.04), while the fraction of outliers reduces from 3% to 0.8%. Moreover, we show that the spectroscopic redshift of the angular neighbour with the highest detection probability provides an excellent estimate of the redshift of the target galaxy, comparable to or even better than the corresponding template-fitting estimate.Comment: 9 pages, 12 figures, matching the accepted version. NezNet is available at https://github.com/tos-1/NezNe

Exploring functional regression for dynamic modeling of shallow landslides in South Tyrol, Italy

Shallow landslides are ubiquitous hazards in mountainous regions worldwide that arise from an interplay of static predisposing factors and dynamic preparatory and triggering conditions. Modeling shallow landslides at regional scales has leveraged data-driven approaches to separately investigate purely spatial landslide susceptibility and temporally varying conditions. Yet, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. Furthermore, dynamic factors have been typically included in data-driven landslide models as scalar predictors by employing aggregated descriptors over time (e.g., mean, maximum, or total precipitation over a defined time window), where many choices are possible for the considered time scales and aggregation operators. Therefore, incorporating the time-varying behavior of dynamic factors remains difficult.This study addresses these challenges by exploring Functional Generalized Additive Models (FGAMs) to predict the occurrence of shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In contrast to conventional techniques, we test the benefits of using functional predictors to describe dynamic factors (e.g., precipitation and temperature) leading to landslide events. In other words, we evaluate dynamic factors as collections of measurements over time (i.e., time series). To do so, our approach uses a binomial FGAM to analyze the statistical associations between the static factors (scalar predictors), the dynamic weather conditions prior to a potential landslide occurrence (functional predictors), and the occurrence of shallow landslides in space and time.Potential outcomes of this novel approach show an overview of the added value of using functional predictors for space and time shallow landslide modeling. These research findings are positioned within the context of the PROSLIDE project, which has received financial support from the Research Südtirol/Alto Adige 2019 research program of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige