1,721,176 research outputs found
Hemplime Blocks: Innovative Solution for Green Buildings in Italy
No full textIn the last years, the attention to an eco-friendly development of the building sector has increased the use of green materials, mainly because the construction sector is one of the most polluting. The hemplime block represents a valid option in this direction: it is a valuable product to improve the sustainability of the building. Like any new product, the main issues are given by the absence of specific rules, starting from the production phase until the installation. For this reason, the main objective of this work is to take a first step toward the long process of identifying possible guidelines for the production and the testing phase of the products to achieve a CE (European Commission) certification. Another essential aspect to clarify is the definition of indications for the laying phase of the prefabricated blocks. This study significantly contributes to reduce uncertainties and skepticism about this technology. With these objectives, some experimental tests have been carried out to verify the reliability of the data declared in the datasheets of hemplime blocks produced in Italy, justifying any incongruity. This study also investigates other aspects of hemplime block, such as the main pathologies that may affect this technology during its lifetime and the maintenance operations necessary to restore the product. Furthermore, the thermal performance of a wall was studied in a climatic chamber to study its behavior in conditions similar to service life
Urban Green Spaces and Their Role in Responding to the Heat Island Effect in Historical Urban Context
No full textUrban vegetation is a proven and natural solution for cooling cities and providing comfort, clean air, and social, health, and economic benefits. Among many ecosystem services (ES), urban vegetation plays a major role in protecting biodiversity and providing passive thermal comfort conditions. Urban parameters, such as urban typology, building materials, and density interact with urban vegetation, which influences the microclimate of the built urban environment. The multiple services offered by urban vegetation are lessened because of the urban heat island (UHI), an extensive phenomenon caused by urbanization, which leads to higher air temperatures in urban centers. This occurs because of the prevalence of low-albedo surfaces, construction materials with high thermal capacity, and a lack of transpiring vegetation, which results in greater absorption of solar radiation. Heat accumulates and causes an increase in air temperature. While dense and large urban green areas have several effects on their surroundings, the extent to which vegetation lowers local temperatures has not been quantified yet, especially in future weather. This paper presents a microclimatic evaluation of two green areas in the city centers of two dense and compact cities in Italy. The evaluation will compare the current situation with a projected future scenario (2050). Air temperature, wind speed, mean radiant temperature (MRT), and the physiologically equivalent temperature (PET) are simulated using ENVI-met to investigate the current and future role of urban green spaces in mitigating the urban heat island effect
Portfolio Optimization Through Elastic Maps: Some Evidence from the Italian Stock Exchange
No full textIn this paper we discuss the use of elastic maps as support tool in the decision process underlying the selection, optimization, and management of financial portfolios. In particular, we suggest an allocation scheme which is interely driven by neural networks, in contrast to the traditional model where investors distribute their money among assets chosen according to the mean and variance of their returns. Our optimization procedure is based on the selection of assets from clusters originated by the nets, according to their proximity to the nodes of the map; this, in turn, is the criterion thanks to which we assign the proper weight to each asset into the portfolio. In order to check the profitability of the approach, we have empirically tested the method with stocks from the Italian Stock Exchange; market reference index has been then used to build proper performance benchmarks. Our main results may be summarised as follows: (i) our approach has revealed to be generally more informative than classical mean−variance method, since it allows to take into account additional variables in the selection procedure; (ii) our procedure can work both in a static framework (i.e. for one time choice), and into a dynamic context (i.e. to the purpose of re−calibration of original decisions). The overall performances appear to be superior to the benchmark in both the static and dynamic case
On the Profitability of Scalping Strategies Based on Neural Networks
No full textWe analyze the potential of unsupervised neural networks when they are employed to support intraday trading activity on financial markets. Several time frequencies have been considered: from five minutes to daily trades. At the current stage our major findings may be summarized as follows: a) unsupervised neural networks are helpful to localize profitable intraday patterns, and they make possible to achieve higher performances than common trading rules; b) trading strategies based on neural networks make exploitable with profits almost continuous trades (i.e. scalping), until transaction costs maintain below proper thresholds
Using multilayer perceptron in computer security to improve intrusion detection
No full textNowadays computer and network security has become a major cause of concern for experts community, due to the growing number of devices connected to the network. For this reason, optimizing the performance of systems able to detect intrusions (IDS - Intrusion Detection System) is a goal of common interest. This paper presents a methodology to classify hacking attacks taking advantage of the generalization property of neural networks. In particular, in this work we adopt the multilayer perceptron (MLP) model with the back-propagation algorithm and the sigmoidal activation function. We analyse the results obtained using different configurations for the neural network, varying the number of hidden layer sand the number of training epochs in order to obtaina low number of false positives. The obtained results will be presented in terms of type of attacks and training epochs and we will show that the best classification is carried out for DOS and Probe attacks. © Springer International Publishing AG 2018
Towards a near-zero energy landmark building using building integrated photovoltaics: the case of the Van Unnik building at Utrecht Science Park
No full textWe assess the feasibility of renovation of a 22-story high-rise building from the 1960s to realize a near-zero energy building by cladding all usable parts of facades and roof using building integrated photovoltaic (BIPV) components. With the present building electricity demand, which includes all energy demand of the building except for heating, it is not possible to generate all demand by BIPV: an annual self-sufficiency ratio of 0.666 or 0.756 is found, using two different roof designs, and 60% coverage of all facades by highly efficient (20%) BIPV modules. Analysis of energy yield on different typical days in summer and winter reveals that the building is self-sufficient for many hours of the day. As on such days, self-consumption is relatively low, which leads to considerable feed-in of surplus electricity to the grid, application of local storage would increase self-sufficiency considerably. Furthermore, it is imperative to lower the electricity demand of the building to reach high self-sufficiency ratios
Lay the Foundations for Building a Robust Eco-Design Methodology
Full text linkThis paper presents and discusses the possible theoretical bases of a comprehensive approach of robust eco-design to reduce the variations of the environmental impact of a product, compared to the baseline. The goal is to overcome the main limitations of contributions to the state of the art, i.e. the lack of a single approach to treat all possible causes, practical application and rigor in discussing the issues of environmental sustainability. The proposal is the intersection between eco-assessment, design theories and robust design. The eco-assessment provides the basis for an initial formulation of the environmental problems to be faced, which are correlated to the variation of the impacts. The design theories allow, through their ontology, to reformulate environmental problems in a more appropriate way to be addressed by the designer and at the same time provide, together with the robust design methods, suggestions to search the solutions. The analysis presented and the application proposal help to show the complexity and heterogeneity of the topic and reinforce the idea of introducing a systematic methodology to select the most appropriate method and favour its targeted use
A comparison of character and word embeddings in bidirectional LSTMs for POS tagging in Italian
No full textWord representations are mathematical items capturing a word’s meaning and its grammatical properties in a machine-readable way. They map each word into equivalence classes including words sharing similar properties. Word representations can be obtained automatically by using unsupervised learning algorithms that rely on the distributional hypothesis, stating that the meaning of a word is strictly connected to its context in terms of surrounding words. This assessed notion of context has been recently reconsidered in order to include both distributional and morphological features of a word in terms of characters co-occurrence. This approach has evidenced very promising results, especially in NLP tasks, e.g, POS Tagging, where the representation of the so-called Out of Vocabulary (OOV) words represents a partially solved issue. This work is intended to face the problem of representing OOV words for a POS Tagging task, contextualized to the Italian language. Potential benefits and drawbacks of adopting a Bidirectional Long Short Term Memory (bi-LSTM) fed with a joint character and word embeddings representation to perform POS Tagging also considering OOV words have been investigated. Furthermore, experiments have been performed and discussed by estimating qualitative and quantitative indicators, and, thus, suggesting some possible future direction of the investigation
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