Rapid discrimination of four salmonella enterica serovars: A performance comparison between benchtop and handheld raman spectrometers

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES

Methane gas management continues to be a challenge concerning underground coal mine safety and productivity worldwide despite the extraordinary effort of the mining industry, governmental agencies, and academia to develop new technologies to monitor and control methane gas emissions more efficiently. The risk of hazardous methane gas concentrations in underground environments cannot be underestimated. Statistical data for the last 100 years indicate that around 80% of the accidents and 90% of the fatalities in the underground coal mining industry in the US were related to methane gas explosions. Modern underground mine operations monitor and evaluate atmospheric parameters such as barometric pressure, temperature, gas concentrations, and ventilation parameters (e.g., fan performance and airflow) by means of Automated Atmospheric Monitoring Systems, which use sensors that collect a massive amount of data implemented by mine operators to make decisions concerning mine safety and operate ventilation systems more effectively. In addition, however, some of these data can be statistically studied to develop forecast models to help improve the safety and health parameters of underground coal mining operations. The research presented in this dissertation investigates potential correlations between methane gas concentrations and independent variables such as barometric pressure and coal production rate to build reliable forecasting models capable of predicting future concentrations of methane gas, mainly based on time series data collected by the Atmospheric Monitoring System of three active underground coal mining operations in the eastern US and weather data retrieved from public weather stations in the proximity of the case studies. The mine and weather data were stored and pre-processed using an Atmospheric Monitoring Analysis and Database Management system explicitly designed to manage Atmospheric Monitoring Systems data. Furthermore, various statistical techniques were implemented to assess the potential association (e.g., autocorrelation and cross-correlation) between methane gas concentration time series and the independent variables. Such associations were employed to develop univariate and multivariate forecasting models for methane gas emissions in underground coal mines. Finally, the optimal model is selected using the Akaike Information Criterion, and the results obtained from the different forecast approaches (univariate and multivariate) are compared using cross-validation metrics to determine the best model. It was concluded that the ARIMA, VAR, and ARIMAX methane gas forecasting methodologies proposed in this research can accurately predict methane gas concentrations in underground coal mines operations. The methane gas forecasted from the models matched the validation data consistently, and their linear correlation was positive and strong in most cases. In addition, the 95% confidence interval consistently captured the forecast and validation data

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES

Methane gas management continues to be a challenge concerning underground coal mine safety and productivity worldwide despite the extraordinary effort of the mining industry, governmental agencies, and academia to develop new technologies to monitor and control methane gas emissions more efficiently. The risk of hazardous methane gas concentrations in underground environments cannot be underestimated. Statistical data for the last 100 years indicate that around 80% of the accidents and 90% of the fatalities in the underground coal mining industry in the US were related to methane gas explosions. Modern underground mine operations monitor and evaluate atmospheric parameters such as barometric pressure, temperature, gas concentrations, and ventilation parameters (e.g., fan performance and airflow) by means of Automated Atmospheric Monitoring Systems, which use sensors that collect a massive amount of data implemented by mine operators to make decisions concerning mine safety and operate ventilation systems more effectively. In addition, however, some of these data can be statistically studied to develop forecast models to help improve the safety and health parameters of underground coal mining operations. The research presented in this dissertation investigates potential correlations between methane gas concentrations and independent variables such as barometric pressure and coal production rate to build reliable forecasting models capable of predicting future concentrations of methane gas, mainly based on time series data collected by the Atmospheric Monitoring System of three active underground coal mining operations in the eastern US and weather data retrieved from public weather stations in the proximity of the case studies. The mine and weather data were stored and pre-processed using an Atmospheric Monitoring Analysis and Database Management system explicitly designed to manage Atmospheric Monitoring Systems data. Furthermore, various statistical techniques were implemented to assess the potential association (e.g., autocorrelation and cross-correlation) between methane gas concentration time series and the independent variables. Such associations were employed to develop univariate and multivariate forecasting models for methane gas emissions in underground coal mines. Finally, the optimal model is selected using the Akaike Information Criterion, and the results obtained from the different forecast approaches (univariate and multivariate) are compared using cross-validation metrics to determine the best model. It was concluded that the ARIMA, VAR, and ARIMAX methane gas forecasting methodologies proposed in this research can accurately predict methane gas concentrations in underground coal mines operations. The methane gas forecasted from the models matched the validation data consistently, and their linear correlation was positive and strong in most cases. In addition, the 95% confidence interval consistently captured the forecast and validation data

Mobile sensing within smart buildings: A survey

In recent times, there has been an increase in interest in mobile sensing systems. These systems integrate sensors with mobile devices like smartphones and robots, enabling data collection across varying locations. Such systems facilitate automated data acquisition by integrating specific sensors with mobile devices adapted to their intended objectives. Mobile sensing systems have the potential to replace static sensing systems to reduce the complexity of retrofitting buildings and minimise the overall number of sensors. This survey discusses the domain of the built environment and focuses on the building. It categorises the objectives of smart buildings and comprehends human requirements within the range of smart buildings through existing static sensing systems. Additionally, the survey categorises different mobile sensing by carriers and summarises the most suitable for different building objectives. By exploring mobile sensing systems across diverse environments, this study also evaluates the most fitting mobile sensing systems for various sensing scales and compares static sensing with mobile sensing systems

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development

Izaña Atmospheric Research Center. Activity Report 2019-2020

Editors: Emilio Cuevas, Celia Milford and Oksana Tarasova.[EN]The Izaña Atmospheric Research Center (IARC), which is part of the State Meteorological Agency of Spain (AEMET), is a site of excellence in atmospheric science. It manages four observatories in Tenerife including the high altitude Izaña Atmospheric Observatory. The Izaña Atmospheric Observatory was inaugurated in 1916 and since that date has carried out uninterrupted meteorological and climatological observations, contributing towards a unique 100-year record in 2016. This reports are a summary of the many activities at the Izaña Atmospheric Research Center to the broader community. The combination of operational activities, research and development in state-of-the-art measurement techniques, calibration and validation and international cooperation encompass the vision of WMO to provide world leadership in expertise and international cooperation in weather, climate, hydrology and related environmental issues.[ES]El Centro de Investigación Atmosférica de Izaña (CIAI), que forma parte de la Agencia Estatal de Meteorología de España (AEMET), representa un centro de excelencia en ciencias atmosféricas. Gestiona cuatro observatorios en Tenerife, incluido el Observatorio de Izaña de gran altitud, inaugurado en 1916 y que desde entonces ha realizado observaciones meteorológicas y climatológicas ininterrumpidas y se ha convertido en una estación centenaria de la OMM. Estos informes resumen las múltiples actividades llevadas a cabo por el Centro de Investigación Atmosférica de Izaña. El liderazgo del Centro en materia de investigación y desarrollo con respecto a las técnicas de medición, calibración y validación de última generación, así como la cooperación internacional, le han otorgado una reputación sobresaliente en lo que se refiere al tiempo, el clima, la hidrología y otros temas ambientales afines