Environmental performances in green labels for hotels – a critical review

The global attention towards climate change has led national governments and the international community to the definition of plans aiming to reduce greenhouse gas emissions in all economic sectors. Recently, attention has focused also on the tourism sector, and especially on the lodging industry, which consumes high amounts of resources and energy to satisfy guests expectations in terms of offered services and comfort conditions. In this sector, eco-certifications or green labels are spreading, perceived as useful marketing tools to communicate the hoteliers’ environmental efforts to consumers, who are becoming more and more sensitive to ecological matters. However, the wide offer of green labels and the lack of appropriate information are contributing to increase costumers’ confusion and perception of real “green”. The present paper focuses its attention on a set of currently available tools to evaluate the environmental performances of hotels, in order to enquire if and to which extent they are able to inform about the sustainability of accommodation structures. Starting from the wide number of certification schemes available on the market, 19 multi-attribute, third-party green labels were compared, aiming to explore the role that energy efficiency measures play in the certification procedure

Defect Engineering: Graphene Gets Designer Defects

An extended one-dimensional defect that has the potential to act as a conducting wire has been embedded in another perfect graphene sheet.Comment: 2 pages, 1 figur

Nanoengineering Carbon Allotropes from Graphene

Monolithic structures can be built into graphene by the addition and subsequent re-arrangement of carbon atoms. To this end, ad-dimers of carbon are a particularly attractive building block because a number of emerging technologies offer the promise of precisely placing them on carbon surfaces. In concert with the more common Stone-Wales defect, repeating patterns can be introduced to create as yet unrealized materials. The idea of building such allotropes out of defects is new, and we demonstrate the technique by constructing two-dimensional carbon allotropes known as haeckelite. We then extend the idea to create a new class of membranic carbon allotropes that we call \emph{dimerite}, composed exclusively of ad-dimer defects.Comment: 5 pages, 5 figure

When Traffic Flow Prediction and Wireless Big Data Analytics Meet

In this article, we verify whether or not prediction performance can be improved by fitting the actual data to optimize the parameter values of a prediction model. The traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). The traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the realtime transportation data from correlative roads and vehicles. The verification in this article is conducted by comparing the optimized and the normal time series prediction models. With the verification, we can learn that the era of big data is here and will become an important aspect for the study of traffic flow prediction to solve the congestion problem. Experimental results of a case study are provided to verify the existence of the performance improvement in the prediction, while the research challenges of this data-analytics-based prediction are presented and discussed

d_{x^2-y^2} Symmetry and the Pairing Mechanism

An important question is if the gap in the high temperature cuprates has d_{x^2-y^2} symmetry, what does that tell us about the underlying interaction responsible for pairing. Here we explore this by determining how three different types of electron-phonon interactions affect the d_{x^2-y^2} pairing found within an RPA treatment of the 2D Hubbard model. These results imply that interactions which become more positive as the momentum transfer increases favor d_{x^2-y^2} pairing in a nearly half-filled band.Comment: 9 pages and 2 eps figs, uses revtex with epsf, in press, PR

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

Fluctuating Cu-O-Cu Bond model of high temperature superconductivity in cuprates

Twenty years of extensive research has yet to produce a general consensus on the origin of high temperature superconductivity (HTS). However, several generic characteristics of the cuprate superconductors have emerged as the essential ingredients of and/or constraints on any viable microscopic model of HTS. Besides a Tc of order 100K, the most prominent on the list include a d-wave superconducting gap with Fermi liquid nodal excitations, a d-wave pseudogap with the characteristic temperature scale T*, an anomalous doping-dependent oxygen isotope shift, nanometer-scale gap inhomogeneity, etc.. The key role of planar oxygen vibrations implied by the isotope shift and other evidence, in the context of CuO2 plane symmetry and charge constraints from the strong intra-3d Coulomb repulsion U, enforces an anharmonic mechanism in which the oxygen vibrational amplitude modulates the strength of the in-plane Cu-Cu bond. We show, within a Fermi liquid framework, that this mechanism can lead to strong d-wave pairing and to a natural explanation of the salient features of HTS