Search results for: air contaminants monitoring

Authors: Igor Svishchev, Kashif Choudhry

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Progress in advanced energy technologies is not feasible without understanding how engineering materials perform under extreme environmental conditions. The corrosion behaviour of Fe-Ni-Cr and Zr alloys has been systematically examined under high-temperature and supercritical water flow conditions. The changes in elemental release rate and dissolved gas concentration provide valuable insights into the mechanism of passivation by forming oxide films. A non-intrusive method for monitoring the extent of surface oxidation based on hydrogen release rate has been developed. This approach can be used for the on-line monitoring corrosion behavior of reactor materials without the need to interrupt the flow and remove corrosion coupons. Surface catalysed thermochemical reactions may generate sufficient hydrogen to have an effect on the accumulation of oxidizing species generated by radiolytic processes in the heat transport systems of the supercritical water cooled nuclear reactor.

Keywords: high-temperature corrosion, non-intrusive monitoring, reactor materials, supercritical water

Procedia PDF Downloads 135

Authors: Gyorgy Deak, Marius C. Raischi, Lucian P. Georgescu, Tiberius M. Danalache, Elena Holban, Madalina G. Boboc, Monica Matei, Catalina Iticescu, Marius V. Olteanu, Stefan Zamfir, Gabriel Cornateanu

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At present, on the Lower Danube, different types of pressures have been identified that affect the anadromous sturgeons stocks with an impact that leads to their decline. This paper presents techniques and procedures used by Romanian experts in the tagging and monitoring of anadromous sturgeons, as well as unique results at international level obtained on the basis of an informational volume collected in over 7 years of monitoring on these species behavior (both for adults as well as for ultrasonically tagged juveniles) on the Lower Danube. The local impact of hydrotechnical constructions (bottom sill, maritime navigation channel), the global impact of the poaching phenomenon and the impact of the restocking programs with sturgeon juveniles were assessed. Thus, the bottom sill impact on the Bala branch, the Bastroe Channel (cross-border impact) and the poaching phenomenon at the level of the Lower Danube was analyzed on the basis of a unique informational volume obtained through the use of patented monitoring systems by the Romanian experts (DKTB respectively, DKMR-01T). At the same time, the results from the monitoring of ultrasonically tagged sturgeon juveniles from the 2015 repopulation program are presented. Conclusions resulting from research can ensure favorable premises for finding some conservation solutions for CITES-protected sturgeon species that have survived for millions of years, currently being 1 species on the brink of extinction - Russian sturgeon, 2 species in danger of extinction - Beluga sturgeon and Stellate sturgeon and 2 species already extinct from the Lower Danube, namely common sturgeon and ship sturgeon.

Keywords: Lower Danube, sturgeons monitoring (adults and juveniles), tagging, impact on conservation

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Authors: Xiaohui Wang, Zhichuan Guan, Roman Shor, Chuanbin Xu

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Early monitoring and real-time quantitative description of gas intrusion under the premise of ensuring the integrity of the drilling fluid circulation system will greatly improve the accuracy and effectiveness of deepwater gas-kick monitoring. Therefore, in order to study the propagation characteristics of ultrasonic waves in the gas-liquid two-phase flow within the marine riser, in this paper, a numerical simulation method of ultrasonic propagation in the annulus of the riser was established, and the credibility of the numerical analysis was verified by the experimental results of the established gas intrusion monitoring simulation experimental device. The numerical simulation can solve the sound field in the gas-liquid two-phase flow according to different physical models, and it is easier to realize the single factor control. The influence of each parameter on the received signal can be quantitatively investigated, and the law with practical guiding significance can be obtained.

Keywords: gas-kick detection, ultrasonic, void fraction, coda wave velocity

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Authors: ZerarkaHizia, Akchiche Mustapha, Prunier Florent

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Landslide of Ain El Hammam (AEH) has been an old slip since 1969; it was reactivated after an intense rainfall period in 2008 where it presents a complex shape and affects broad areas. The schist of AEH is more or less altered; the alteration is facilitated by the fracturing of the rock in its upper part, the presence of flowing water as well as physical and chemical mechanisms of desegregation in joint of altered schist. The factors following these instabilities are mostly related to the geological formation, the hydro-climatic conditions and the topography of the region. The city of AEH is located on the top of a steep slope at 50 km from the city of TiziOuzou (Algeria). AEH’s topographic monitoring of unstable slope allows analyzing the structure and the different deformation mechanism and the gradual change in the geometry, the direction of change of slip. It also allows us to delimit the area affected by the movement. This work aims to study the behavior of AEH landslide with topographic monitoring and to validate the results with numerical modeling of the slip site, when the hydraulic factors are identified as the most important factors for the reactivation of this landslide. With the help of the numerical code PLAXIS 2D and PlaxFlow, the precipitations and the steady state flow are modeled. To identify the mechanism of deformation and to predict the spread of the AEH landslide numerically, we used the equivalent deviatory strain, and these results were visualized by MATLAB software.

Keywords: equivalent deviatory strain, landslide, numerical modeling, topographic monitoring

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Authors: Navneet Sharma, Himanshu Ojha, Dharam Pal Pathak, Rakesh Kumar Sharma

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Radio-nuclides decontamination is an important task because any extra second of deposition leads to deleterious health effects. We had developed and characterise nanoemulsion of p-tertbutylcalix[4]arens using phase inversion temperature (PIT) method and evaluate its decontamination efficacy (DE). The solubility of the drug was determined in various oils and surfactants. Nanoemulsion developed with an HLB value of 11 and different ratios of the surfactants 10% (7:3, w/w), oil (20%, w/w), and double distilled water (70%) were selected. Formulation was characterised by multi-photon spectroscopy and parameters like viscosity, droplet size distribution, zeta potential and stability were optimised. In vitro and Ex vivo decontamination efficacy (DE) was evaluated against Technetium-99m, Iodine-131, and Thallium-201 as radio-contaminants applied over skin of Sprague-Dawley rat and human tissue equivalent model. Contaminants were removed using formulation soaked in cotton swabs at different time intervals and whole body imaging and static counts were recorded using SPECT gamma camera before and after decontamination attempt. Data were analysed using one-way analysis of variance (ANOVA) and was found to be significant (p <0.05). DE of the nanoemulsion loaded with p-tertbutylcalix[4]arens was compared with placebo and recorded to be 88±5%, 90±3% and 89±3% for 99mTc, 131I and 201Tl respectively. Ex-vivo complexation study of p-tertbutylcalix[4]arene nanoemulsion with surrogate nuclides of radioactive thallium and Iodine, were performed on rat skin mounted on Franz diffusion cell using high-resolution sector field inductively coupled plasma mass spectroscopy (HR-SF-ICPMS). More than 90% complexation of the formulation with these nuclides was observed. Results demonstrate that the prepared nanoemulsion formulation was found efficacious for the decontamination of radionuclides from a large contaminated population.

Keywords: p-tertbutylcalix[4]arens, skin decontamination, radiological emergencies, nanoemulsion, iodine-131, thallium-201

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: B. Atli-Veltin, A. Vosteen, D. Megan, A. Jedynska, L. K. Cheng

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Composite materials combine the advantages of being lightweight and possessing high strength. This is in particular of interest for the development of large constructions, e.g., aircraft, space applications, wind turbines, etc. One of the shortcomings of using composite materials is the complex nature of the failure mechanisms which makes it difficult to predict the remaining lifetime. Therefore, condition and health monitoring are essential for using composite material for critical parts of a construction. Different types of sensors are used/developed to monitor composite structures. These include ultrasonic, thermography, shearography and fiber optic. The first 3 technologies are complex and mostly used for measurement in laboratory or during maintenance of the construction. Optical fiber sensor can be surface mounted or embedded in the composite construction to provide the unique advantage of in-operation measurement of mechanical strain and other parameters of interest. This is identified to be a promising technology for Structural Health Monitoring (SHM) or Prognostic Health Monitoring (PHM) of composite constructions. Among the different fiber optic sensing technologies, Fiber Bragg Grating (FBG) sensor is the most mature and widely used. FBG sensors can be realized in an array configuration with many FBGs in a single optical fiber. In the current project, different aspects of using embedded FBG for composite wind turbine monitoring are investigated. The activities are divided into two parts. Firstly, FBG embedded carbon composite laminate is subjected to tensile and bending loading to investigate the response of FBG which are placed in different orientations with respect to the fiber. Secondly, the demonstration of using FBG sensor array for temperature and strain sensing and monitoring of a 5 m long scale model of a glass fiber mono-pile is investigated. Two different FBG types are used; special in-house fibers and off-the-shelf ones. The results from the first part of the study are showing that the FBG sensors survive the conditions during the production of the laminate. The test results from the tensile and the bending experiments are indicating that the sensors successfully response to the change of strain. The measurements from the sensors will be correlated with the strain gauges that are placed on the surface of the laminates.

Keywords: Fiber Bragg Gratings, embedded sensors, health monitoring, wind turbine towers

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Authors: Pakhil Singh, Rahul Singh, Mohammad Saad Alam, Yasser Rafat

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The battery management system is an essential package/system which ensures optimum performance and safety of a battery by monitoring the key essential parameters of the battery like the voltage, current, temperature, state of charge, state of health during charging and discharging. This can be accomplished using outputs of various sensors employed to serve the purpose. The increasing demand for electricity generation from renewable energy sources requires proper storage and hence a proper monitoring system as well. A battery management system is required in wide applications ranging from renewable energy storage systems, off-grid solar PV applications to electric vehicles. The aim of this paper is to study the parameters used in monitoring various battery operating conditions and proposes the usage of the internet of things (IoT) to implement a reliable battery management system.

Keywords: electric vehicles, internet of things, sensors, state of charge, state of health

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Authors: Norbert Magyar, Jozsef Kovacs, Peter Tanos, Balazs Trasy, Tamas Garamhegyi, Istvan Gabor Hatvani

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Water is one of the most important common resources, and as a result of urbanization, agriculture, and industry it is becoming more and more exposed to potential pollutants. The prevention of the deterioration of water quality is a crucial role for environmental scientist. To achieve this aim, the operation of monitoring networks is necessary. In general, these networks have to meet many important requirements, such as representativeness and cost efficiency. However, existing monitoring networks often include sampling sites which are unnecessary. With the elimination of these sites the monitoring network can be optimized, and it can operate more economically. The aim of this study is to illustrate the applicability of the CCDA (Combined Cluster and Discriminant Analysis) to the field of water quality monitoring and optimize the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and two surface-subsurface water systems on the watershed of Lake Neusiedl/Lake Fertő and on the Szigetköz area over a period of approximately two decades. CCDA combines two multivariate data analysis methods: hierarchical cluster analysis and linear discriminant analysis. Its goal is to determine homogeneous groups of observations, in our case sampling sites, by comparing the goodness of preconceived classifications obtained from hierarchical cluster analysis with random classifications. The main idea behind CCDA is that if the ratio of correctly classified cases for a grouping is higher than at least 95% of the ratios for the random classifications, then at the level of significance (α=0.05) the given sampling sites don’t form a homogeneous group. Due to the fact that the sampling on the Lake Neusiedl/Lake Fertő was conducted at the same time at all sampling sites, it was possible to visualize the differences between the sampling sites belonging to the same or different groups on scatterplots. Based on the results, the monitoring network of the Danube yields redundant information over certain sections, so that of 12 sampling sites, 3 could be eliminated without loss of information. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, and in the case of Lake Balaton, 5 out of 10 could be discarded. For the groundwater system of the catchment area of Lake Neusiedl/Lake Fertő all 50 monitoring wells are necessary, there is no redundant information in the system. The number of the sampling sites on the Lake Neusiedl/Lake Fertő can decrease to approximately the half of the original number of the sites. Furthermore, neighbouring sampling sites were compared pairwise using CCDA and the results were plotted on diagrams or isoline maps showing the location of the greatest differences. These results can help researchers decide where to place new sampling sites. The application of CCDA proved to be a useful tool in the optimization of the monitoring networks regarding different types of water bodies. Based on the results obtained, the monitoring networks can be operated more economically.

Keywords: combined cluster and discriminant analysis, cost efficiency, monitoring network optimization, water quality

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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The practical efficient approach is suggested for estimation of the seismoacoustic sources energy in C-OTDR monitoring systems. This approach represents the sequential plan for confidence estimation both the seismoacoustic sources energy, as well the absorption coefficient of the soil. The sequential plan delivers the non-asymptotic guaranteed accuracy of obtained estimates in the form of non-asymptotic confidence regions with prescribed sizes. These confidence regions are valid for a finite sample size when the distributions of the observations are unknown. Thus, suggested estimates are non-asymptotic and nonparametric, and also these estimates guarantee the prescribed estimation accuracy in the form of the prior prescribed size of confidence regions, and prescribed confidence coefficient value.

Keywords: nonparametric estimation, sequential confidence estimation, multichannel monitoring systems, C-OTDR-system, non-lineary regression

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Authors: M. Fares, S. Mameri, I. Abdlani, K. Negara

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Neutron detectors in general, proportional counters gas filling based isotope 3He in particular are going to be essential for monitoring and control of certain nuclear facilities, monitoring of experimentation around neutron beams and channels nuclear research reactors, radiation protection instruments and other tools multifaceted exploration and testing of materials, etc. This work consists of a measurement campaign features two Proportional Counters 3He (3He: LND252/USA CP, CP prototype: 3He LND/DDM). This is to make a comparison study of a CP 3He LND252/USA reference one hand, and in the context of routine periodic monitoring of the characteristics of the detectors for controlling the operation especially for laboratory prototypes. In this paper, we have described the different characteristics of the detectors and the experimental protocols used. Tables of measures have been developed and the different curves were plotted. The experimental campaign at stake: 2 PC 3He were thus characterized: Their characteristics (sensitivity, energy pulse height distribution spectra, gas amplification etc.) Were identified: 01 PC 3He 1'' Type: prototype DEDIN/DDM, 01 PC 3He 1'' Type: LND252/USA.

Keywords: PC 3He, sensitivity, pulse height distribution spectra, gas amplification

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Authors: Mohamed Sarrab, Hadj Bourdoucen

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Mobile applications are verified to check the correctness or evaluated to check the performance with respect to specific security properties such as availability, integrity, and confidentiality. Where they are made available to the end users of the mobile application is achievable only to a limited degree using software engineering static verification techniques. The more sensitive the information, such as credit card data, personal medical information or personal emails being processed by mobile application, the more important it is to ensure the confidentiality of this information. Monitoring non-trusted mobile application during execution in an environment where sensitive information is present is difficult and unnerving. The paper addresses the issue of monitoring and controlling the flow of confidential information during non-trusted mobile application execution. The approach concentrates on providing a dynamic and usable information security solution by interacting with the mobile users during the run-time of mobile application in response to information flow events.

Keywords: mobile application, run-time verification, usable security, direct information flow

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Tzong-Ying Hao, Mohammad T. Rahmani

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The Torre Central building is a 9-story shear wall structure located in Santiago, Chile, and has been instrumented since 2009. Events of different intensity (ambient vibrations, weak and strong earthquake motions) have been recorded, and thus the building can serve as a full-scale benchmark to evaluate the structural health monitoring method developed. The first part of this article presents an analysis of inter-story drifts, and of changes in the first system frequencies (estimated from the relative displacement response of the 8th-floor with respect to the basement from recorded data) as baseline indicators of the occurrence of damage. During 2010 Chile earthquake the system frequencies were detected decreasing approximately 24% in the EW and 27% in NS motions. Near the end of shaking, an increase of about 17% in the EW motion was detected. The structural health monitoring (SHM) method based on changes in wave traveling time (wave method) within a layered shear beam model of structure is presented in the second part of this article. If structural damage occurs the velocity of wave propagated through the structure changes. The wave method measures the velocities of shear wave propagation from the impulse responses generated by recorded data at various locations inside the building. Our analysis and results show that the detected changes in wave velocities are consistent with the observed damages. On this basis, the wave method is proven for actual implementation in structural health monitoring systems.

Keywords: Chile earthquake, damage detection, earthquake response, impulse response, layered shear beam, structural health monitoring, Torre Central building, wave method, wave travel time

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Ibrahim Hakeem

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Biosolids are stabilised sewage sludge produced from wastewater treatment processes. Biosolids contain valuable plant nutrient which facilitates their beneficial reuse in agricultural land. However, the increasing levels of legacy and emerging contaminants such as heavy metals (HMs), PFAS, microplastics, pharmaceuticals, microbial pathogens etc., are restraining the direct land application of biosolids. Pyrolysis of biosolids can effectively degrade microbial and organic contaminants; however, HMs remain a persistent problem with biosolids and their pyrolysis-derived biochar. In this work, we demonstrated the integrated processing of biosolids involving the acid pre-treatment for HMs removal and selective reduction of ash-forming elements followed by the bench-scale pyrolysis of the treated biosolids to produce quality biochar and bio-oil enriched with valuable platform chemicals. The pre-treatment of biosolids using 3% v/v H₂SO₄ at room conditions for 30 min reduced the ash content from 30 wt% in raw biosolids to 15 wt% in the treated sample while removing about 80% of limiting HMs without degrading the organic matter. The preservation of nutrients and reduction of HMs concentration and mobility via the developed hydrometallurgical process improved the grade of the treated biosolids for beneficial land reuse. The co-removal of ash-forming elements from biosolids positively enhanced the fluidised bed pyrolysis of the acid-treated biosolids at 700 ℃. Organic matter devolatilisation was improved by 40%, and the produced biochar had higher surface area (107 m²/g), heating value (15 MJ/kg), fixed carbon (35 wt%), organic carbon retention (66% dry-ash free) compared to the raw biosolids biochar with surface area (56 m²/g), heating value (9 MJ/kg), fixed carbon (20 wt%) and organic carbon retention (50%). Pre-treatment also improved microporous structure development of the biochar and substantially decreased the HMs concentration and bioavailability by at least 50% relative to the raw biosolids biochar. The integrated process is a viable approach to enhancing value recovery from biosolids.

Keywords: biosolids, pyrolysis, biochar, heavy metals

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Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

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This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

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Authors: Launcelot McGrath

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A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: biological signals, signal acquisition, anaesthesiology, patient monitoring

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Authors: Tomas Makaras, Gintaras Svecevičius

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Landfill waste is a common problem as it has an economic and environmental impact even if it is closed. Landfill waste contains a high density of various persistent compounds such as heavy metals, organic and inorganic materials. As persistent compounds are slowly-degradable or even non-degradable in the environment, they often produce sublethal or even lethal effects on aquatic organisms. The aims of the present study were to estimate sublethal effects of the Kairiai landfill (WGS: 55°55‘46.74“, 23°23‘28.4“) leachate on the locomotor activity of rainbow trout Oncorhynchus mykiss juveniles using the original system package developed in our laboratory for automated monitoring, recording and analysis of aquatic organisms’ activity, and to determine patterns of fish behavioral response to sublethal effects of leachate. Four different concentrations of leachate were chosen: 0.125; 0.25; 0.5 and 1.0 mL/L (0.0025; 0.005; 0.01 and 0.002 as part of 96-hour LC50, respectively). Locomotor activity was measured after 5, 10 and 30 minutes of exposure during 1-minute test-periods of each fish (7 fish per treatment). The threshold-effect-concentration amounted to 0.18 mL/L (0.0036 parts of 96-hour LC50). This concentration was found to be even 2.8-fold lower than the concentration generally assumed to be “safe” for fish. At higher concentrations, the landfill leachate solution elicited behavioral response of test fish to sublethal levels of pollutants. The ability of the rainbow trout to detect and avoid contaminants occurred after 5 minutes of exposure. The intensity of locomotor activity reached a peak within 10 minutes, evidently decreasing after 30 minutes. This could be explained by the physiological and biochemical adaptation of fish to altered environmental conditions. It has been established that the locomotor activity of juvenile trout depends on leachate concentration and exposure duration. Modeling of these parameters showed that the activity of juveniles increased at higher leachate concentrations, but slightly decreased with the increasing exposure duration. Experiment results confirm that the behavior of rainbow trout juveniles is a sensitive and rapid biomarker that can be used in combination with the system for fish behavior monitoring, registration and analysis to determine sublethal concentrations of pollutants in ambient water. Further research should be focused on software improvement aimed to include more parameters of aquatic organisms’ behavior and to investigate the most rapid and appropriate behavioral responses in different species. In practice, this study could be the basis for the development and creation of biological early-warning systems (BEWS).

Keywords: fish behavior biomarker, landfill leachate, locomotor activity, rainbow trout juveniles, sublethal effects

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Authors: Bilal Arshad, Johan Barthelemy, Elliott Pilton, Pascal Perez

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Getting reliable, informative, and up-to-date information about the location, mobility, and behavioural patterns of animals will enhance our ability to research and preserve biodiversity. The fusion of infra-red sensors and camera traps offers an inexpensive way to collect wildlife data in the form of images. However, extracting useful data from these images, such as the identification and counting of animals remains a manual, time-consuming, and costly process. In this paper, we demonstrate that such information can be automatically retrieved by using state-of-the-art deep learning methods. Another major challenge that ecologists are facing is the recounting of one single animal multiple times due to that animal reappearing in other images taken by the same or other camera traps. Nonetheless, such information can be extremely useful for tracking wildlife and understanding its behaviour. To tackle the multiple count problem, we have designed a meshed network of camera traps, so they can share the captured images along with timestamps, cumulative counts, and dimensions of the animal. The proposed method takes leverage of edge computing to support real-time tracking and monitoring of wildlife. This method has been validated in the field and can be easily extended to other applications focusing on wildlife monitoring and management, where the traditional way of monitoring is expensive and time-consuming.

Keywords: computer vision, ecology, internet of things, invasive species management, wildlife management

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Authors: Patrícia Branco, Catarina Prista, Helena Albergaria

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Industrial fuel-ethanol fermentations are carried out under non-sterile conditions, which favors the development of microbial contaminants, leading to huge economic losses. Wild yeasts such as Brettanomyces bruxellensis and lactic acid bacteria are the main contaminants of industrial bioethanol fermentation, affecting Saccharomyces cerevisiae performance and decreasing ethanol yields and productivity. In order to control microbial contaminations, the fuel-ethanol industry uses different treatments, including acid washing and antibiotics. However, these control measures carry environmental risks such as acid toxicity and the rise of antibiotic-resistant bacteria. Therefore, it is crucial to develop and apply less toxic and more environmentally friendly biocontrol methods. In the present study, an industrial fuel-ethanol starter, S. cerevisiae Ethanol-Red, was genetically modified to over-express AMPs with activity against fuel-ethanol microbial contaminants and evaluated regarding its biocontrol effect during mixed-culture alcoholic fermentations artificially contaminated with B. bruxellensis. To achieve this goal, S. cerevisiae Ethanol-Red strain was transformed with a plasmid containing the AMPs-codifying genes, i.e., partial sequences of TDH1 (925-963 bp) and TDH2/3 (925-963 bp) and a geneticin resistance marker. The biocontrol effect of those genetically modified strains was evaluated against B. bruxellensis and compared with the antagonistic effect exerted by the modified strain with an empty plasmid (without the AMPs-codifying genes) and the non-modified strain S. cerevisiae Ethanol-Red. For that purpose, mixed-culture alcoholic fermentations were performed in a synthetic must use the modified S. cerevisiae Ethanol-Red strains together with B. bruxellensis. Single-culture fermentations of B. bruxellensis strains were also performed as a negative control of the antagonistic effect exerted by S. cerevisiae strains. Results clearly showed an improved biocontrol effect of the genetically-modified strains against B. bruxellensis when compared with the modified Ethanol-Red strain with the empty plasmid (without the AMPs-codifying genes) and with the non-modified Ethanol-Red strain. In mixed-culture fermentation with the modified S. cerevisiae strain, B. bruxellensis culturability decreased from 5×104 CFU/mL on day-0 to less than 1 CFU/mL on day-10, while in single-culture B. bruxellensis increased its culturability from 6×104 to 1×106 CFU/mL in the first 6 days and kept this value until day-10. Besides, the modified Ethanol-Red strain exhibited an enhanced antagonistic effect against B. bruxellensis when compared with that induced by the non-modified Ethanol-Red strain. Indeed, culturability loss of B. bruxellensis after 10 days of fermentation with the modified Ethanol-Red strain was 98.7 and 100% higher than that occurred in fermentations performed with the non-modified Ethanol-Red and the empty-plasmid modified strain, respectively. Therefore, one can conclude that the S. cerevisiae genetically modified strain obtained in the present work may be a valuable solution for the mitigation of microbial contamination in fuel-ethanol fermentations, representing a much safer and environmentally friendly preservation strategy than the antimicrobial treatments (acid washing and antibiotics) currently applied in fuel-ethanol industry.

Keywords: antimicrobial peptides, fuel-ethanol microbial contaminations, fuel-ethanol fermentation, biocontrol agents, genetically-modified yeasts

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Authors: Ardalan Tofighi Soleimandarabi

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Artificial intelligence has revolutionized the concrete construction industry and improved processes by increasing efficiency, accuracy, and sustainability. This article examines the applications of artificial intelligence in predicting the compressive strength of concrete, optimizing mixing plans, and improving structural health monitoring systems. Artificial intelligence-based models, such as artificial neural networks (ANN) and combined machine learning techniques, have shown better performance than traditional methods in predicting concrete properties. In addition, artificial intelligence systems have made it possible to improve quality control and real-time monitoring of structures, which helps in preventive maintenance and increases the life of infrastructure. Also, the use of artificial intelligence plays an effective role in sustainable construction by optimizing material consumption and reducing waste. Although the implementation of artificial intelligence is associated with challenges such as high initial costs and the need for specialized training, it will create a smarter, more sustainable, and more affordable future for concrete structures.

Keywords: artificial intelligence, concrete construction, compressive strength prediction, structural health monitoring, stability

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Authors: Hyun-Woo Cho

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It is quite important to utilize right time and intelligent production monitoring and diagnosis of industrial processes in terms of quality and safety issues. When compared with monitoring task, fault diagnosis represents the task of finding process variables responsible causing a specific fault in the process. It can be helpful to process operators who should investigate and eliminate root causes more effectively and efficiently. This work focused on the active use of combining a nonlinear statistical technique with a preprocessing method in order to implement practical real-time fault identification schemes for data-rich cases. To compare its performance to existing identification schemes, a case study on a benchmark process was performed in several scenarios. The results showed that the proposed fault identification scheme produced more reliable diagnosis results than linear methods. In addition, the use of the filtering step improved the identification results for the complicated processes with massive data sets.

Keywords: diagnosis, filtering, nonlinear statistical techniques, process monitoring

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Authors: Andrey V. Timofeev

Abstract:

The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Vathsala Kaluarachchi, Oshani Wimalarathna, Charith Vandebona, Gayani Chandrarathna, Lakmal Rupasinghe, Windhya Rankothge

Abstract:

It is the fundamental function of a monitoring system to allow users to collect and process data. A worldwide threat, the corona outbreak has wreaked havoc in Sri Lanka, and the situation has gotten out of hand. Since the epidemic, the Sri Lankan government has been unable to establish a systematic system for monitoring corona patients and providing emergency care in the event of an outbreak. Most patients have been held at home because of the high number of patients reported in the nation, but they do not yet have access to a functioning medical system. It has resulted in an increase in the number of patients who have been left untreated because of a lack of medical care. The absence of competent medical monitoring is the biggest cause of mortality for many people nowadays, according to our survey. As a result, a smartphone app for analyzing the patient's state and determining whether they should be hospitalized will be developed. Using the data supplied, we are aiming to send an alarm letter or SMS to the hospital once the system recognizes them. Since we know what those patients need and when they need it, we will put up a desktop program at the hospital to monitor their progress. Deep learning, image processing and application development, natural language processing, and blockchain management are some of the components of the research solution. The purpose of this research paper is to introduce a mechanism to connect hospitals and patients even when they are physically apart. Further data security and user-friendliness are enhanced through blockchain and NLP.

Keywords: blockchain, deep learning, NLP, monitoring system

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Authors: Frank Hespeler, Giuseppe Loiacono

Abstract:

We propose measures for systemic risk generated through intra-sectorial interdependencies in the hedge fund sector. These measures are based on variations in the average cross-effects of funds showing significant interdependency between their individual returns and the moments of the sector’s return distribution. The proposed measures display a high ability to identify periods of financial distress, are robust to modifications in the underlying econometric model and are consistent with intuitive interpretation of the results.

Keywords: hedge funds, systemic risk, vector autoregressive model, risk monitoring

Procedia PDF Downloads 325

Authors: Chen-Fang Tsai, Shin-Li Lu

Abstract:

Distribution-free control chart is an oncoming area from the statistical process control charts in recent years. Some researchers have developed various nonparametric control charts and investigated the detection capability of these charts. The major advantage of nonparametric control charts is that the underlying process is not specifically considered the assumption of normality or any parametric distribution. In this paper, two nonparametric exponentially weighted moving average (EWMA) control charts based on nonparametric tests, namely NE-S and NE-M control charts, are proposed for monitoring process variability. Generally, weighted moving average (GWMA) control charts are extended by utilizing design and adjustment parameters for monitoring the changes in the process variability, namely NG-S and NG-M control charts. Statistical performance is also investigated on NG-S and NG-M control charts with run rules. Moreover, sensitivity analysis is performed to show the effects of design parameters under the nonparametric NG-S and NG-M control charts.

Keywords: Distribution-free control chart, EWMA control charts, GWMA control charts

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Authors: Bryce Benson, Sooin Lee, Ashwin Belle

Abstract:

Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring

Procedia PDF Downloads 187

Authors: F. Sahnoune, M. Belhamel, M. Zelmat

Abstract:

Solar thermal systems available today provide reliability, efficiency and significant environmental benefits. In housing, they can satisfy the hot water demand and reduce energy bills by 60 % or more. Additionally, collective systems or large scale solar thermal systems are increasingly used in different conditions for hot water applications and space heating in hotels and multi-family homes, hospitals, nursing homes and sport halls as well as in commercial and industrial building. However, in situ real performance data for collective solar water heating systems has not been extensively outlined. This paper focuses on the study of real energy performances of a collective solar water heating system using the remote monitoring technique in Algerian climatic conditions. This is to ensure proper operation of the system at any time, determine the system performance and to check to what extent solar performance guarantee can be achieved. The measurements are performed on an active indirect heating system of 12 m2 flat plate collector’s surface installed in Algiers and equipped with a various sensors. The sensors transmit measurements to a local station which controls the pumps, valves, electrical auxiliaries, etc. The simulation of the installation was developed using the software SOLO 2000. The system provides a yearly solar yield of 6277.5 KWh for an estimated annual need of 7896 kWh; the yearly average solar cover rate amounted to 79.5%. The productivity is in the order of 523.13 kWh / m²/year. Simulation results are compared to measured results and to guaranteed solar performances. The remote monitoring shows that 90% of the expected solar results can be easy guaranteed on a long period. Furthermore, the installed remote monitoring unit was able to detect some dysfunctions. It follows that remote monitoring is an important tool in energy management of some building equipment.

Keywords: large-scale solar water heater, real energy performance, remote monitoring, solar performance guarantee, tool to promote solar water heater

Procedia PDF Downloads 243