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Aug 28, 2024

Beyond Cerdá: Public housing in the Barcelona periphery

Mass migration into the city of Barcelona in the 1950s created an estimated deficit of 100,000 dwellings by the close of the decade, and by the early 1960s an estimated 100,000 people were living in shanty towns in and around the city. In an effort to address this crisis, central State and local housing authorities constructed a series of housing estates in the urban periphery, beyond the central ensanche (expansion), the centrepiece of Ildefonso Cerdá’s 1860 master plan for the city. Using secondary sources, original photographs taken at the end of the Franco era, and more recent Google Earth images, this article discusses the role of the main housing authorities at the time, and examines the planning process that allowed the construction of these housing estates and their subsequent demolition or remodelling in the post-Franco era. The article finds that the public authorities allowed planning regulations to be contravened, or used loopholes and special measures within the planning laws, to construct housing units of minimal dimensions and generally poor quality in areas often zoned for other uses. More recently, “special plans” have been used effectively to improve or replace the majority of these estates. The article concludes that political will was the dominant factor in determining housing construction in the Franco era with the planning process being largely an irrelevance, and that residents’ associations have led the struggle for improvements in these estates in the post-Franco era, with the revised planning laws providing an effective framework for change.

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Jan 2, 2025

Advanced sentiment analysis techniques to detect users' perceptions related to the "health" of a railway company: Some evidence from European countries

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

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Jun 5, 2024

Fire safety of building integrated photovoltaic systems—State of the art

The main long-term goal of international communities is to achieve sustainable development. This issue is currently highly topical in most European Union (EU) countries due to the ongoing energy crisis. Building Integrated Photovoltaics (BIPV), which can be integrated into the building surface (roof or facade), thereby replacing conventional building materials, contributes significantly to achieving zero net energy buildings. However, fire safety is important when using BIPV as a structural system in buildings, and it is essential that the application of BIPV as building facades and roofs does not adversely affect the safety of the buildings, their occupants, or the responding firefighters. As multifunctional products, BIPV modules must meet fire safety requirements in the field of electrical engineering as well as in the construction industry. In terms of building regulations, the fire safety requirements of the BIPV must comply with national building regulations. Within this article, aspects and fire hazards associated with BIPV system installations will be defined, including proposals for installation and material requirements that can help meet fire safety.

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Sept 11, 2024

Study of the impact of polymeric solution preparation parameters on the production of nanofibers based on PAN and PVA

The search for the development of nanostructured materials has led to the study of the properties of their precursors. For the production of nanofibers by the electrospinning process, it is necessary to determine the rheological parameters of the precursor solutions. Since these properties can be influenced by the processing variables and chemical composition of the polymer, this study aims to elucidate the effect of the addition of vinyl monomers in the formulation of nanofibers based on polyacrylonitrile and to determine the optimal parameters for the production of the precursor polymer solution. The effects of temperature and addition of vinyl monomers were evaluated by rheometry, from the analysis of the variation of the viscosity of the solutions, and by microscopy, the morphology of the nanofibers produced. It was observed that the increase in the temperature used to produce the solutions improves the fibers’ properties. Still, there is a relationship between the time of exposure of the polymeric solution to the temperature and the homogeneity of the fibers, which cannot exceed 45 min. The addition of vinyl monomers, to produce PAN-PVA co-polymeric fibers, increases the conductivity and reduces the viscosity of the solutions, resulting in more refined and homogeneous fibers.

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Oct 11, 2023

Development of stock price prediction system using Flask framework and LSTM algorithm

Stock investment in Indonesia has been steadily growing in the past five years, offering profit potential alongside the risk of loss. Stockholders must analyze the stocks they intend to purchase. Stockholders often analyze stocks by observing patterns that occurred in the previous days to predict future prices. Therefore, a method is needed to simplify the process of analyzing the stock pattern. Although there are already several websites that have the concept of predicting stock prices, these websites do not utilize deep learning algorithms. This research aims to develop a stock price prediction website using deep learning algorithms, specifically the Long Short-Term Memory (LSTM) algorithm to help users predict stock prices. This research focuses on five banks with the highest market capitalization in Indonesia, namely Bank Central Asia, Bank Rakyat Indonesia, Bank Mandiri, Bank Negara Indonesia, and Bank Syariah Indonesia. The website utilizes Flask framework and LSTM. Flask is used to apply LSTM model to the website, while the LSTM can capture long-term dependencies in high-complexity data. The result of this research is a stock price prediction website application, where the prediction results are displayed through the website. The LSTM model for each stock has a Mean Absolute Percentage Error (MAPE) of less than 10%, which indicates that the model is “Highly accurate” based on the MAPE accuracy scale judgment.

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Apr 7, 2020

Financing infrastructure in Asia through bonds and capital markets

The project finance scenario has changed significantly around the world after the 2008 financial crisis and following the subsequent Basel III recommendations. Project finance loans from commercial banks and financial institutions have largely dried up, leaving it mostly to the export credit agencies and the bilateral and multilateral development banks to provide the institutional credit. Unfortunately, those sources are not enough, given the huge needs for construction of new infrastructure and renovation of the old ones across Asia, Africa and Latin America. The need for capital markets, through market listed financial products across asset class, unlocking a large part of domestic and corporate savings, has never been felt as strongly before. This article seeks to analyze the development story of various Asian capital markets and examine financial products, which have succeeded in their short history in receiving investor interest. The article also delves into the challenges to market development, policy imperatives and the issues relating to market liquidity and credit rating, which are the most significant influencers for public market float and investor interest.

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