An Innovative Methodological Approach for Monitoring and Chemical Characterization of Odors around Industrial Sites

This study aims to highlight the potentialities of an innovative methodological approach for monitoring and chemical characterization of odors, especially in high concern and complex industrial areas. The proposed approach was developed in order to monitor and identify odor-active compounds responsible for odor annoyance coming from different industrial activities such as landfills, wastewater treatment plants, and petroleum plants. The methodology's strengths are as follows: (1) the tailored approach for each typology of industrial areas/sites; (2) integration of technologies able to provide real-time information about the emissive sources; (3) mapping of air pollutants on the territory aimed to identify and discriminate among different fugitive emissions responsible for odor annoyance; (4) collection of more representative air samples only during the nuisance events, thanks to the implementation of innovative sampling systems and citizens' involvement; and (5) increased analytical sensitivity in odor-active VOCs detection. This methodology reveals to be a useful tool to collect real-time information about the emission sources and their impacts on the surrounding area giving credit to citizens' complaints. Moreover, it allows to overcome the limitations of the conventional approaches related to the lack of instrumental sensitivity and to identify the chemical compounds contributing to the odor annoyance

Breath analysis for early detection of malignant pleural mesothelioma: Volatile organic compounds (VOCs) determination and possible biochemical pathways

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output

[pain]Byte VR Storytelling & Classical Ballet

This initial stage paper focuses on the Virtual Reality (VR) experience of the [pain]Byte ballet. The live and VR experience debut October 1st 2017, as part of the Brighton digital festival. Specifically, the development of the VR environment to compliment live performance by using the same choreography to create an option capture element of the VR story telling experience. Reviewing Virtual & Alternative reality gaming & storytelling works and the use of VR for chronic pain management (Chen, Win). Does the VR experience compare to that of the live theatre for the audience? The data visualisations and VR environment will be continuations of the Network Simulator, [data]Storm 2015. We are visualising and comparing the pain pathway system to that of a social network. Linking pain signals to viral/negative messaging for some of the visuals. The main purpose of the pieces links to how “we" present ourselves online, these better or veiled versions of ourselves. For chronic pain sufferers, this can be daily activity in the real world. The paper concludes by identifying some future directions for the research project. The Ballet: [pain]Byte is a data driven dance classical ballet performance and VR (virtual reality) experience. [pain]Byte, is about chronic pain and biomedical engineering, in particular the use of implanted technology - neuromodulation (Al-Kaisey et al). Using data as a medium for storytelling, what it means to be in chronic pain. The live augmented theatre and VR experience research focuses on how an audience’s exposure and understanding are impacted by the difference mediums used for [pain]byte

Study of trunk asymmetry in normal children and adolescents

The scoliometer readings in both standing and sitting position of 2071 children and adolescents (1099 boys and 972 girls) aged from 5 to 18 years old were studied. The angle of trunk rotation (ATR) was measured, in order to quantify the existing trunk asymmetry. Children and adolescents were divided in two groups according to the severity of trunk asymmetry. In the first group asymmetry was 1 to 6 degrees and in the second group was 7 or more degrees. Radiographic and leg length inequality evaluation were also performed in a number of children. The mean frequency of symmetric (ATR = 0 degrees) boys and girls was 67.06% and 65.01% for the standing screening position and 76.5% and 75.1% for the sitting position, respectively. The mean difference of frequency of asymmetry (ATR > 0 degrees) at standing minus sitting forward bending position for boys and girls was 10.22% and 9.37%, respectively. The mean frequency of asymmetry of 7 or more degrees was 3.23% for boys and 3.92% for girls at the standing forward bending position and 1.62% and 2.21% at the sitting, respectively. Girls are found to express higher frequency of asymmetry than boys. Right trunk asymmetry was more common than left. The sitting position is the preferred screening position for examining the rib or loin hump during school screening as it demonstrates the best correlation with the spinal deformity exposing the real trunk asymmetry

Data Network Simulator with Classical Ballet

[data]storm, from readysaltedcode CIC, a data driven dance performance. The development of a social network simulator to demonstrate network growth and message propagation. The underpinning theory of piece stems from social network theory (SNT), graph theory, computer mediated communication (CMC) through to social information processing (SIP) and Computational Thinking (CT). The data visualisation is linked to the physical ballet movements of the dancers, they are a manifestation of the data. The data visualisations on screen link to the live dancers performance patterns and modify to create the visuals and movements of data transmission across a network. Network growth. The first of the simulations shows network growth. Each node in the network represents a user who has the following characteristics: • friendliness (how often they're likely to make friends with another user) • chattiness (how often they send out messages) • category (the subject area they're most interested in) At random time intervals things occur: New users are added to the network depending on the above characteristics, users become friends with each other. All the rules stay the same throughout the simulation. At the same time the dance (ballet) movements and wearables (LEDS) were choreographed/coded to accompany the data visualisation using network mapping techniques. The choreography and wearables elements link to the friendliness and chattiness of each of the nodes in the simulated network. This network simulation is further utilised in the Virus section of the performance using the same rules to simulate how a virus can spread through a network. Further work on this simulation will look at two things 1. Message propagation and viral messaging within a social network like Twitter. 2. Pain signals within the body and how they compare to data transfer within a social network

Pattern recognition and anomaly detection by self-organizing maps in a multi month e-nose survey at an industrial site

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses