Impact Estimation of Emergency Events Using Social Media Streams

In recent years, Social Media platforms have attracted millions of users, becoming a primary communication channel. They offer the possibility to massively ingest and instantly share big volumes of user-generated content before, during, and after emergency events. Being able to accurately quantify the impact of such hazardous events could greatly help all organizations involved in the emergency management cycle to adequately plan the required recovery operations. In this work, we propose a novel Natural Language Processing approach built on rule-based algorithms able to estimate, from tweets posted during natural hazards, the impact of emergency events in terms of affected population and infrastructures. We implement our approach in an operational environment and present its validation on a publicly released dataset of more than 1.4K manually annotated tweets, showing an overall weighted F1 score of 0.77

A 'glocal' approach for real-time emergency event detection in Twitter

Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale

Electrolyzer performance analysis of an integrated hydrogen power system for greenhouse heating a case study

A greenhouse containing an integrated system of photovoltaic panels, a water electrolyzer, fuel cells and a geothermal heat pump was set up to investigate suitable solutions for a power system based on solar energy and hydrogen, feeding a self-sufficient, geothermal-heated greenhouse. The electricity produced by the photovoltaic source supplies the electrolyzer; the manufactured hydrogen gas is held in a pressure tank. In these systems, the electrolyzer is a crucial component; the technical challenge is to make it work regularly despite the irregularity of the solar source. The focus of this paper is to study the performance and the real energy efficiency of the electrolyzer, analyzing its operational data collected under different operating conditions affected by the changeable solar radiant energy characterizing the site where the experimental plant was located. The analysis of the measured values allowed evaluation of its suitability for the agricultural requirements such as greenhouse heating. On the strength of the obtained result, a new layout of the battery bank has been designed and exemplified to improve the performance of the electrolyzer. The evaluations resulting from this case study may have a genuine value, therefore assisting in further studies to better understand these devices and their associated technologies

Hydrogen and renewable energy sources integrated system for greenhouse heating

The environmental impact and the cost of fossil fuel system for greenhouse heating are the major limits for the development of protected horticulture. Recent researches are focusing on greenhouses optimal climate control and reduction of energy consumption. The use of suitable microclimate control systems, energy efficiency strategies and renewable energy sources could improve the environmental performance of the greenhouses. Renewable energy sources can be used to produce hydrogen by electrolysis with very high gas purity. Hydrogen can serve the purpose of storing overproduced energy after meeting the requirements of the greenhouse, and later it can be employed as fuel, achieving a stand-alone power system. Therefore a research is under development at the University of Bari in order to investigate the suitable solutions of a power system based on solar energy (photovoltaic) and hydrogen, integrated with a geothermal heat pump for powering a self sustained heated greenhouse. The tests were carried out at the experimental farm of the University of Bari sited in Valenzano, Bari, Southern Italy, latitude 41° N, where two experimental greenhouses, with the same geometric and constructive characteristics, have been realized; the distance between the two greenhouses is 12 m; therefore there is no mutual shading. One of the two greenhouses is heated using a low enthalpy heat pump combined with a vertical ground heat exchanger, in comparison with the other unheated greenhouse. The electrical energy for heat pump operation is provided by a purpose-built array of solar photovoltaic modules, which supplies also a water electrolyser system controlled by embedded pc; the generated dry hydrogen gas is conserved in suitable pressured storage tank. The hydrogen is used to produce electricity in a fuel cell in order to meet the above mentioned heat pump power demand when the photovoltaic system is inactive during winter night-time or the solar radiation level is insufficient to meet the electrical demand of the heat pump during overcast cold sky. This note reports the main elements regarding the integrated system design and building and it shows preliminary results of testing operation

Mapping of Agriculture Plastic Waste

Abstract The current intensification of the use of plastic materials in agriculture, although has increased significantly the productivity, is also generating growing adverse effects on the environment of the agro-ecosystem. The agriculture is responsible for a massive use of plastic materials, in addition to energy and water inputs, chemical fertilizer and pesticides. Besides the pollution generated during the manufacture, at the end of their lifetime plastic materials used for crop covering, soil mulching, packaging, containers, pots, irrigation and drainage pipes, may became a pollution source when improperly disposed, leaved on the ground or burned. Instead the agricultural plastic waste (APW), if correctly collected, can be used as a new secondary raw material or as an energy source. An adequate APW management can prevent economical losses and environmental damages. The territory of the Barletta, Andria, Trani Province (BAT), in the Apulia Region, South Italy, is an agricultural area characterized by vineyards, olive groves, orchards and vegetables; it represents an area of intense production of plastic wastes and with a widespread problem linked to the application of unacceptable disposal practices. The goal of this study is to define and quantify the different types of plastic waste produced by the agricultural practice in a restricted area of the municipal area of Trani and Barletta, to localize the points where the most remarkable quantities of them are generated, and to provide the local Authorities and the decision makers of a useful tool for implementing an efficient and effective waste management. A dedicated geo-referenced database was designed using land use maps in a GIS environment and applying a methodology that can be functional for any kind of agricultural plastic waste. The resulting database gives updated and complete information on the plastic waste generation, over the land, related to the cultivation kind

A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results. Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels. This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use. Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels. Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation. We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons

Self-supervised and semi-supervised learning for road condition estimation from distributed road-side cameras

Monitoring road conditions, e.g., water build-up due to intense rainfall, plays a fundamental role in ensuring road safety while increasing resilience to the effects of climate change. Distributed cameras provide an easy and affordable alternative to instrumented weather stations, enabling diffused and capillary road monitoring. Here, we propose a deep learning-based solution to automatically detect wet road events in continuous video streams acquired by road-side surveillance cameras. Our contribution is two-fold: first, we employ a convolutional Long Short-Term Memory model (convLSTM) to detect subtle changes in the road appearance, introducing a novel temporally consistent data augmentation to increase robustness to outdoor illumination conditions. Second, we present a contrastive self-supervised framework that is uniquely tailored to surveillance camera networks. The proposed technique was validated on a large-scale dataset comprising roughly 2000 full day sequences (roughly 400K video frames, of which 300K unlabelled), acquired from several road-side cameras over a span of two years. Experimental results show the effectiveness of self-supervised and semi-supervised learning, increasing the frame classification performance (measured by the Area under the ROC curve) from 0.86 to 0.92. From the standpoint of event detection, we show that incorporating temporal features through a convLSTM model both improves the detection rate of wet road events (+10%) and reduces false positive alarms (–45%). The proposed techniques could benefit also other tasks related to weather analysis from road-side and vehicle-mounted cameras

Thermal behaviour of green façades in summer

Building greenery systems can represent a sustainable solution for new buildings design and for existing buildings retrofitting, in order to improve the thermal energy performance of buildings, to decrease building energy loads and to contrast the Urban Heat Island. Green façades can influence thermal properties of a building by means of different important mechanisms: the shading, the cooling, the insulating and the wind barrier effect. Moreover, green façades accomplish heating effect in the cold season and at nighttime. An experimental test was developed at small scale at the University of Bari (Italy) from 2014 to 2016 for testing two different green façades. The plant species chosen were Pandorea jasminoides variegated and Rhyncospermum jasminoides, two evergreen climbing plants. A third uncovered wall was used as control. The thermal behaviour of the plants was analysed during the 2016 summer season, by keeping in consideration the external surface temperature of the building and the temperature of the airgap behind the green vertical systems. The daylight temperatures observed on the plant-covered walls during representative days were lower than the respective temperatures of the uncovered wall up to 7.0°C. During nighttime, the temperatures behind the plants were higher than the respective temperatures of the control wall up to 2.2°C. The results shown in the present research allow delineating the behaviour of the two plant species during summer in the Mediterranean climate region

Identification and characterization of the sucrose synthase 2 gene (Sus2) in durum wheat

Sucrose transport is the central system for the allocation of carbon resources in vascular plants. Sucrose synthase (SUS), which reversibly catalyzes sucrose synthesis and cleavage, represents a key enzyme in the control of the flow of carbon into starch biosynthesis. In the present study the genomic identification and characterization of the Sus2-2A and Sus2-2B genes coding for SUS in durum wheat (cultivars Ciccio and Svevo) is reported. The genes were analyzed for their expression in different tissues and at different seed maturation stages, in four tetraploid wheat genotypes (Svevo, Ciccio, Primadur, and 5-BIL42). The activity of the encoded proteins was evaluated by specific activity assays on endosperm extracts and their structure established by modeling approaches. The combined results of sucrose synthase 2 expression and activity levels were then considered in the light of their possible involvement in starch yield

Study of a pilot photovoltaic-electrolyser-fuel cell power system for a geothermal heat pump heated greenhouse and evaluation of the electrolyser efficiency and operational mode

The intrinsic factor of variability of renewable energy sources often limits their broader use. The photovoltaic solar systems can be provided with a power back up based on a combination of an electrolyser and a fuel cell stack. The integration of solar hydrogen power systems with greenhouse heating equipment can provide a possible option for powering stand-alone greenhouses. The aim of the research under development at the experimental farm of Department of Agro-Environmental Sciences of the University of Bari Aldo Moro is to investigate on the suitable solutions of a power system based on photovoltaic energy and on the use of hydrogen as energy vector, integrated with a ground source heat pump for greenhouse heating in a self sustained way. The excess energy produced by a purpose-built array of solar photovoltaic modules supplies an alkaline electrolyser; the produced hydrogen gas is stored in pressured storage tank. When the solar radiation level is insufficient to meet the heat pump power demand, the fuel cell starts converting the chemical energy stored by the hydrogen fuel into electricity. This paper reports on the description of the realised system. Furthermore the efficiency and the operational mode of the electrolyser were evaluated during a trial period characterised by mutable solar radiant energy. Anyway the electrolyser worked continuously in a transient state producing fluctuations of the hydrogen production and without ever reaching the steady-state conditions. The Faradic efficiency, evaluated by means of an empirical mathematic model, highlights that the suitable working range of the electrolyser was 1.5÷2.5 kW and then for hydrogen production more than 0.21 Nm3h–1