Search results for: forest fire detection

Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

Procedia PDF Downloads 217

Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

Procedia PDF Downloads 296

Authors: E. A. Asabina, V. I. Pet'kov, P. A. Mayorov, A. V. Markin, N. N. Smirnova, A. M. Kovalskii, A. A. Usenko

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The purpose of this work was to synthesize and investigate phase formation, structure and thermophysical properties of the phosphates M_0.5+xM'_xZr_2–x(PO₄)₃ (M – Cd, Sr, Pb; M' – Mg, Co, Mn). The compounds were synthesized by sol-gel method. The results showed formation of limited solid solutions of NZP/NASICON type. The crystal structures of triple phosphates of the compositions MMg_0.5Zr_1.5(PO₄)₃ were refined by the Rietveld method using XRD data. Heat capacity (8–660 K) of the phosphates Pb_0.5+xMg_xZr_2-x(PO₄)₃ (x = 0, 0.5) was measured, and reversible polymorphic transitions were found at temperatures, close to the room temperature. The results of Rietveld structure refinement showed the polymorphism caused by disordering of lead cations in the cavities of NZP/NASICON structure. Thermal expansion (298−1073 K) of the phosphates MMg_0.5Zr_1.5(PO₄)₃ was studied by XRD method, and the compounds were found to belong to middle and low-expanding materials. Thermal diffusivity (298–573 K) of the ceramic samples of phosphates slightly decreased with temperature increasing. As was demonstrated, the studied phosphates are characterized by the better thermophysical characteristics than widespread fire-resistant materials, such as zirconia and etc.

Keywords: NASICON, NZP, phosphate, structure, synthesis, thermophysical properties

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Anita, Sari Septiani Tangke, Rusdina Bte Ladju, Nasrum Massi

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New diagnostic tools are urgently needed to interrupt the transmission of tuberculosis and multidrug-resistant tuberculosis. The microscopic-observation drug-susceptibility (MODS) assay is a rapid, accurate and simple liquid culture method to detect multidrug-resistant tuberculosis (MDR-TB). MODS were evaluated to determine a lower and same concentration of isoniazid and rifampin for detection of MDR-TB. Direct drug-susceptibility testing was performed with the use of the MODS assay. Drug-sensitive control strains were tested daily. The drug concentrations that used for both isoniazid and rifampin were at the same concentration: 0.16, 0.08 and 0.04μg per milliliter. We tested 56 M. tuberculosis clinical isolates and the control strains M. tuberculosis H37RV. All concentration showed same result. Of 53 M. tuberculosis clinical isolates, 14 were MDR-TB, 38 were susceptible with isoniazid and rifampin, 1 was resistant with isoniazid only. Drug-susceptibility testing was performed with the use of the proportion method using Mycobacteria Growth Indicator Tube (MGIT) system as reference. The result of MODS assay using lower concentration was significance (P<0.001) compare with the reference methods. A lower and same concentration of isoniazid and rifampin can be used to detect MDR-TB. Operational cost and application can be more efficient and easier in resource-limited environments. However, additional studies evaluating the MODS using lower and same concentration of isoniazid and rifampin must be conducted with a larger number of clinical isolates.

Keywords: isoniazid, MODS assay, MDR-TB, rifampin

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Authors: Ghada A. Gamea, Nabila A. Yaseen, Ahmed A. Othman, Ahmed S. Tawfik

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Infection with Toxoplasma gondii can cause serious complications in pregnant women, leading to abortion, stillbirth, and congenital anomalies in the fetus. Definitive diagnosis of T. gondii acute infection is therefore critical for the clinical management of a mother and her fetus. This study was conducted on 250 pregnant females in the first trimester who were inpatients or outpatients at Obstetrics and Gynaecology Department at Tanta University Hospital. Screening of the selected females was done for the detection of immunoglobulin (IgG and IgM), and all subjects were submitted to history taking through a questionnaire including personal data, risk factors for Toxoplasma, complaint and history of the present illness. Thirty-eight samples, including 18 IgM +ve and 20 IgM-ve cases were further investigated by the avidity and IgE ELISA tests. The seroprevalence of toxoplasmosis in pregnant women was (42.8%) based on the presence of IgG antibodies in their sera. Contact with cats and consumption of raw or undercooked meat are important risk factors that were associated with toxoplasmosis in pregnant women. By serology, it could be observed that in the IgM +ve group, only one case (5.6%) showed an acute pattern by using the avidity test, though 10 (55.6%) cases were found to be acute by the IgE assay. On the other hand, in the IgM –ve group, 3 (15%) showed low avidity, but none of them was positive by using the IgE assay. In conclusion, there is no single serological test that can be used to confirm whether T. gondii infection is recent or was acquired in the distant past. A panel of tests for detection of toxoplasmosis will certainly have higher discriminatory power than any test alone.

Keywords: diagnosis, serology, seroprevalence, toxoplasmosis

Procedia PDF Downloads 155

Authors: Judy M. Obliosca, Olivia Vest, Sandra Poulos, Kelsi Smith, Tammy Ferguson, Abigail Powers Lott, Alicia K. Smith, Yang Xu, Christopher K. Tison

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Post-Traumatic Stress Disorder (PTSD) is a mental health problem that people may develop after experiencing traumatic events such as combat, natural disasters, and major emotional challenges. Tragically, the number of military personnel with PTSD correlates directly with the number of veterans who attempt suicide, with the highest rate in the Army. Research has shown epigenetic risks in those who are prone to several psychiatric dysfunctions, particularly PTSD. Once initiated in response to trauma, epigenetic alterations in particular, the DNA methylation in the form of 5-methylcytosine (5mC) alters chromatin structure and represses gene expression. Current methods to detect DNA methylation, such as bisulfite-based genomic sequencing techniques, are laborious and have massive analysis workflow while still having high error rates. A faster and simpler detection method of high sensitivity and precision would be useful in a clinical setting to confirm potential PTSD etiologies, prevent other psychiatric disorders, and improve military health. A nano-enhanced Surface Plasmon Resonance imaging (SPRi)-based assay that simultaneously detects site-specific 5mC base (termed as PTSD base) in methylated genes related to PTSD is being developed. The arrays on a sensing chip were first constructed for parallel detection of PTSD bases using synthetic and genomic DNA (gDNA) samples. For the gDNA sample extracted from the whole blood of a PTSD patient, the sample was first digested using specific restriction enzymes, and fragments were denatured to obtain single-stranded methylated target genes (ssDNA). The resulting mixture of ssDNA was then injected into the assay platform, where targets were captured by specific DNA aptamer probes previously immobilized on the surface of a sensing chip. The PTSD bases in targets were detected by anti-5-methylcytosine antibody (anti-5mC), and the resulting signals were then enhanced by the universal nanoenhancer. Preliminary results showed successful detection of a PTSD base in a gDNA sample. Brighter spot images and higher delta values (control-subtracted reflectivity signal) relative to those of the control were observed. We also implemented the in-house surface activation system for detection and developed SPRi disposable chips. Multiplexed PTSD base detection of target methylated genes in blood DNA from PTSD patients of severity conditions (asymptomatic and severe) was conducted. This diagnostic capability being developed is a platform technology, and upon successful implementation for PTSD, it could be reconfigured for the study of a wide variety of neurological disorders such as traumatic brain injury, Alzheimer’s disease, schizophrenia, and Huntington's disease and can be extended to the analyses of other sample matrices such as urine and saliva.

Keywords: epigenetic assay, DNA methylation, PTSD, whole blood, multiplexing

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Authors: Waraporn Wimuktalop

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This research will select regions which are vulnerable to flooding in different level. Mathematical principles will be systematically and rationally utilized as a tool to solve problems of selection the regions. Therefore the method called Multiple Criteria Decision Making (MCDM) has been chosen by having two analysis standards, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and AHP (Analytic Hierarchy Process). There are three criterions that have been considered in this research. The first criterion is climate which is the rainfall. The second criterion is geography which is the height above mean sea level. The last criterion is the land utilization which both forest and agriculture use. The study found that the South has the highest risk of flooding, then the East, the Centre, the North-East, the West and the North, respectively.

Keywords: multiple criteria decision making, TOPSIS, analytic hierarchy process, flooding

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Authors: Lopita Pal, Suresh V. Madha

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Wetlands are areas of marsh, fen, peat land or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres. The rapidly expanding human population, large scale changes in land use/land cover, burgeoning development projects and improper use of watersheds all has caused a substantial decline of wetland resources in the world. Major degradations have been impacted from agricultural, industrial and urban developments leading to various types of pollutions and hydrological perturbations. Regular fishing activities and unsustainable grazing of animals are degrading the wetlands in a slow pace. The paper focuses on the spatio-temporal change detection of the area of the water body and the main cause of this depletion. The total area under study (22°19’87’’ N, 88°20’23’’ E) is a wetland region in West Bengal of 213 sq.km. The procedure used is the Normalized Difference Water Index (NDWI) from multi-spectral imagery and Landsat to detect the presence of surface water, and the datasets have been compared of the years 2016, 2006 and 1996. The result shows a sharp decline in the area of water body due to a rapid increase in the agricultural practices and the growing urbanization.

Keywords: spatio-temporal change, NDWI, urbanization, wetland

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Authors: Mostafa Firoozabadi, Alireza Foroughi Nematollahi

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Wet mills are one of the most important equipment in the mining industries and any defect occurrence in them can stop the production line and it can make some irrecoverable damages to the system. Electromotors are the significant parts of a mill and their monitoring is a necessary process to prevent unwanted defects. The purpose of this study is to investigate the Electromotor bearing defects, theoretically and practically, using the vibration analysis method. When a defect happens in a bearing, it can be transferred to the other parts of the equipment like inner ring, outer ring, balls, and the bearing cage. The electromotor defects source can be electrical or mechanical. Sometimes, the electrical and mechanical defect frequencies are modulated and the bearing defect detection becomes difficult. In this paper, to detect the electromotor bearing defects, the electrical and mechanical defect frequencies are extracted firstly. Then, by calculating the bearing defect frequencies, and the spectrum and time signal analysis, the bearing defects are detected. In addition, the obtained frequency determines that the bearing level in which the defect has happened and by comparing this level to the standards it determines the bearing remaining lifetime. Finally, the defect length is calculated by theoretical equations to demonstrate that there is no need to replace the bearing. The results of the proposed method, which has been implemented on the wet mills in the Golgohar mining and industrial company in Iran, show that this method is capable of detecting the electromotor bearing defects accurately and on time.

Keywords: bearing defect length, defect frequency, electromotor defects, vibration analysis

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Authors: Aniket S. Joshi, Aditya R. Mane, Arjun Tukaram

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In the 21st century, in the era of e-world, people are continuously getting updated by daily information such as weather conditions, news, stock exchange market updates, new projects, cricket updates, sports and other such applications. In the busy situation, they want this information on the little use of keyboard, time. Today in order to get such information user have to repeat same mouse and keyboard actions which includes time and inconvenience. In India due to rural background many people are not much familiar about the use of computer and internet also. Also in small clinics, small offices, and hotels and in the airport there should be a system which retrieves daily information with the minimum use of keyboard and mouse actions. We plan to design application based project that can easily retrieve information with minimum use of keyboard and mouse actions and make our task more convenient and easier. This can be possible with an image processing application which takes real time hand gestures which will get matched by system and retrieve information. Once selected the functions with hand gestures, the system will report action information to user. In this project we use real time hand gesture movements to select required option which is stored on the screen in the form of RSS Feeds. Gesture will select the required option and the information will be popped and we got the information. A real time hand gesture makes the application handier and easier to use.

Keywords: hand detection, hand tracking, hand gesture recognition, HSV color model, Blob detection

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Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

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Authors: Chris Procter

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There is a significant debate on the role of University education in developing or teaching employability skills. Should higher education attempt to do this? Is it the best place? Is it able to do so? Different views abound, but the question is wrongly posed – one of the reasons that previous employability initiatives foundered (e.g., in the UK). Our role is less to teach than to guide, less to develop and more to help articulate: “the mind is not a vessel to be filled, but a fire to be lit” (Plutarch). This paper then addresses how this can be achieved taking into account criticism of employability initiatives as well as relevant learning theory. It discusses the experience of a large module which involved students being assessed on all stages of application for a live job description together with reflection on their professional development. The assessment itself adopted a Patchwork Text approach as a vehicle for learning. Students were guided to evaluate their strengths and areas to be developed, articulate their competencies, and reflect upon their development, moving on to new Thresholds of Employability. The paper uses the student voices to express the progress they made. It concludes that employability can and should be an effective part of the higher education curriculum when designed to encourage students to confidently articulate their competencies and take charge of their own professional development.

Keywords: competencies, employability, patchwork assessment, threshold concepts

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Authors: John E. Foley, Martin Miele, Raul Fonda, Jon Jacobson

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This paper presents development and implementation of new and innovative data collection and analysis methodologies based on deployment of total field magnetometer arrays. Our research has focused on the development of a vertically-integrated suite of platforms all utilizing common data acquisition, data processing and analysis tools. These survey platforms include low-altitude helicopters and ground-based vehicles, including robots, for terrestrial mapping applications. For marine settings the sensor arrays are deployed from either a hydrodynamic bottom-following wing towed from a surface vessel or from a towed floating platform for shallow-water settings. Additionally, sensor arrays are deployed from tethered remotely operated vehicles (ROVs) for underwater settings where high maneuverability is required. While the primary application of these systems is the detection and mapping of unexploded ordnance (UXO), these system are also used for various infrastructure mapping and geologic investigations. For each application, success is driven by the integration of magnetometer arrays, accurate geo-positioning, system noise mitigation, and stable deployment of the system in appropriate proximity of expected targets or features. Each of the systems collects geo-registered data compatible with a web-enabled data management system providing immediate access of data and meta-data for remote processing, analysis and delivery of results. This approach allows highly sophisticated magnetic processing methods, including classification based on dipole modeling and remanent magnetization, to be efficiently applied to many projects. This paper also briefly describes the initial development of magnetometer-based detection systems deployed from low-altitude helicopter platforms and the subsequent successful transition of this technology to the marine environment. Additionally, we present examples from a range of terrestrial and marine settings as well as ongoing research efforts related to sensor miniaturization for unmanned aerial vehicle (UAV) magnetic field mapping applications.

Keywords: dipole modeling, magnetometer mapping systems, sub-surface infrastructure mapping, unexploded ordnance detection

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Authors: Sebastian Töpfer, Jorge Fuenzalida, Marta Gilaberte Basset, Juan P. Torres, Markus Gräfe

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This work presents an experimental and theoretical study about the noise resilience of quantum holography with undetected photons. Quantum imaging has become an important research topic in the recent years after its first publication in 2014. Following this research, advances towards different spectral ranges in detection and different optical geometries have been made. Especially an interest in the field of near infrared to mid infrared measurements has developed, because of the unique characteristic, that allows to sample a probe with photons in a different wavelength than the photons arriving at the detector. This promising effect can be used for medical applications, to measure in the so-called molecule fingerprint region, while using broadly available detectors for the visible spectral range. Further advance the development of quantum imaging methods have been made by new measurement and detection schemes. One of which is quantum holography with undetected light. It combines digital phase shifting holography with quantum imaging to extent the obtainable sample information, by measuring not only the object transmission, but also its influence on the phase shift experienced by the transmitted light. This work will present extended research for the quantum holography with undetected light scheme regarding the influence of external noise. It is shown experimentally and theoretically that the samples information can still be at noise levels of 250 times higher than the signal level, because of its information being transmitted by the interferometric pattern. A detailed theoretic explanation is also provided.

Keywords: distillation, quantum holography, quantum imaging, quantum metrology

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Authors: Rudzani Manafe, Ezekiel Green

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Brucella, has many different virulence factors that act as a causative agent of brucellosis, depending on the environment and other factors, some factors may play a role more than others during infection and as a result, play a role in becoming a causative agent for pathogenesis. Brucella melitensis and Brucella abortus are considered to be pathogenic to humans. The genetic regularity of nine potential causes of virulence of two Brucella species in Eastern Cape livestock have been examined. A hundred and twenty isolates obtained from Molecular Pathogenesis and Molecular Epidemiology Research Group (MPMERG) were used for this study. All isolates were grown on Brucella agar medium. Nine primer pairs were used for the detection of virB2, virB5, vceC, btpA, btpB, prpA, betB, bpe275, and bspB virulence factors using Polymerase chain reaction (PCR). Approximately 100% was observed for genes BecC and BetB from B. arbotus. While the lowest gene observed was PrpA at 4.6% from B. arbotus. BetB was detected in 34.7%, while virB2 and prpA (0%) were not detected in B. melitensis. The results from this research suggest that most isolates of Brucella have virulence-related genes associated with disease pathogenesis. Finally, our findings showed that Brucella strains in the Eastern Cape Province are extremely virulent as virulence characteristics exist in most strains investigated.

Keywords: putative virulence genes, brucella, polymerase chain reaction, milk

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Crysovaladou Kefaloudi, Kassiani Mellou, Eirini Saranti-Papasaranti, Athanasios Koustenis, Chrysoula Botsi, Agapios Terzidis

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Recent migration crisis led to a vast migrant – refugees movement to Greece which created an urgent need for hosting settlements. Taken into account the protection of public health from possible pathogens related to water and food supply as well as waste and sewage accumulation, a 'Living Conditions Recording Form' was created in the context of 'PHILOS' European Program funded by the Asylum Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs, in order to assess a number of environmental sanitation parameters, in refugees – migrants camps in mainland. The assessment will be completed until the end of July. From March to June 2017, mobile unit teams comprised of health inspectors of sub-action 2 of “PHILOS” proceeded with the assessment of living conditions in twenty-two out of thirty-one camps and 'Stata' was used for the statistical analysis of obtained information. Variables were grouped into the following categories: 1) Camp administration, 2) hosted population number, 3) accommodation, 4) heating installations, 5) personal hygiene, 6) sewage collection and disposal, 7) water supply, 8) waste collection and management, 9) pest control, 10) fire safety, 11) food handling and safety. Preliminary analysis of the results showed that camp administration was performed in 90% of the camps by a public authority with the coordination of various NGOs. The median number of hosted population was 222 ranging from 62 to 3200, and the median value of hosted population per accommodation type was 4 in 19 camps. Heating facilities were provided in 86.1% of camps. In 18.2 % of the camps, one personal hygiene facility was available per 6 people ranging in the rest of the camps from 1 per 3 to 1 per 20 hosted refugees-migrants. Waste and sewage collection was performed depending on populations demand in an adequate way in all recorded camps. In 90% of camps, water was supplied through the central water supply system. In 85% of camps quantity and quality of water supply inside camps was regularly monitored for microbial and chemical indices. Pest control was implemented in 86.4% of the camps as well as fire safety measures. Food was supplied by catering companies in 50% of the camps, and the quality and quantity food was monitored at a regular basis. In 77% of camps, food was prepared by the hosted population with the availability of proper storage conditions. Furthermore, in all camps, hosted population was provided with personal hygiene items and health sanitary educational programs were implemented in 77.3% of camps. In conclusion, in the majority of the camps, environmental sanitation parameters were satisfactory. However, waste and sewage accumulation, as well as inadequate pest control measures were recorded in some camps. The obtained data have led to a number of recommendations for the improvement of sanitary conditions, disseminated to all relevant stakeholders. Special emphasis was given to hygiene measures implementation during food handling by migrants – refugees, as well as to waste and sewage accumulation taking in to account the population’s cultural background.

Keywords: environmental sanitation parameters, food borne diseases risk assessment, refugee – migrants camps, water borne diseases risk assessment

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Authors: Francesca Crivellari, Nicholas Mavrogiannis, Zachary Gagnon

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Portable diagnostic methods have become increasingly important for a number of different purposes: point-of-care screening in developing nations, environmental contamination studies, bio/chemical warfare agent detection, and end-user use for commercial health monitoring. The cheapest and most portable methods currently available are paper-based – lateral flow and dipstick methods are widely available in drug stores for use in pregnancy detection and blood glucose monitoring. These tests are successful because they are cheap to produce, easy to use, and require minimally invasive sampling. While adequate for their intended uses, in the realm of blood-borne pathogens and numerous cancers, these paper-based methods become unreliable, as they lack the nM/pM sensitivity currently achieved by clinical diagnostic methods. Clinical diagnostics, however, utilize techniques involving surface plasmon resonance (SPR) and enzyme-linked immunosorbent assays (ELISAs), which are expensive and unfeasible in terms of portability. To develop a better, competitive biosensor, we must reduce the cost of one, or increase the sensitivity of the other. Electric fields are commonly utilized in microfluidic devices to manipulate particles, biomolecules, and cells. Applications in this area, however, are primarily limited to interfaces formed between immiscible interfaces. Miscible, liquid-liquid interfaces are common in microfluidic devices, and are easily reproduced with simple geometries. Here, we demonstrate the use of electrical fields at liquid-liquid electrical interfaces, known as fluidic dielectrophoresis, (fDEP) for biodetection in a microfluidic device. In this work, we apply an AC electric field across concurrent laminar streams with differing conductivities and permittivities to polarize the interface and induce a discernible, near-immediate, frequency-dependent interfacial tilt. We design this aqueous electrical interface, which becomes the biosensing “substrate,” to be intelligent – it “moves” only when a target of interest is present. This motion requires neither labels nor expensive electrical equipment, so the biosensor is inexpensive and portable, yet still capable of sensitive detection. Nanoparticles, due to their high surface-area-to-volume ratio, are often incorporated to enhance detection capabilities of schemes like SPR and fluorimetric assays. Most studies currently investigate binding at an immobilized solid-liquid or solid-gas interface, where particles are adsorbed onto a planar surface, functionalized with a receptor to create a reactive substrate, and subsequently flushed with a fluid or gas with the relevant analyte. These typically involve many preparation and rinsing steps, and are susceptible to surface fouling. Our microfluidic device is continuously flowing and renewing the “substrate,” and is thus not subject to fouling. In this work, we demonstrate the ability to electrokinetically detect biomolecules binding to functionalized nanoparticles at liquid-liquid interfaces using fDEP. In biotin-streptavidin experiments, we report binding detection limits on the order of 1-10 pM, without amplifying signals or concentrating samples. We also demonstrate the ability to detect this interfacial motion, and thus the presence of binding, using impedance spectroscopy, allowing this scheme to become non-optical, in addition to being label-free.

Keywords: biodetection, dielectrophoresis, microfluidics, nanoparticles

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Ibrahim Ilker Ozyigit, Fatih Tabanli, Sezin Adinir

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In this study, Ascochyta blight resistance was investigated in 34 registered chickpea varieties, which are widely planting in different regions of Turkey. For this aim, molecular marker techniques, such as STMS, RAPD and ISSR were used. Ta2, Ta146 and Ts54 primers were used for STMS, while UBC733 and UBC681 primers for RAPD, and UBC836 and UBC858 primers for ISSR. Ta2, Ts54 and Ta146 (STMS), and UBC733 (RAPD) primers demonstrated the distinctive feature for Ascochyta blight resistance. Ta2, Ts54 and Ta146 primers yielded the quite effective results in detection of resistant and sensitive varieties. Besides, UBC 733 primer distinguished all kinds of standard did not give any reliable results for other varieties since it demonstrated all as resistant. In addition, monomorphic bands were obtained from UBC681 (RAPD), and UBC836 and UBC858 (ISSR) primers, not demonstrating reliable results in detection of resistance against Ascochyta blight disease. Obtained results informed us about both disease resistance and genetic diversity in registered Turkish chickpea varieties. This project was funded through the Scientific Research Projects of Marmara University under Grant Number FEN-C-YLP-070617-0365 and The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 113O070.

Keywords: plant genetics, ISSR, RAPD, STMS

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Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

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Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

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Authors: Ali Ben Abbes, ImedRiadh Farah, Vincent Barra

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Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I₁, I₂, … I_n at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

Keywords: multi-temporal satellite image, urban growth, non-stationary, stochastic model

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Authors: Vishal Kumar, Soumitra Satapathi

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Föster Resonance Energy Transfer (FRET) is a powerful technique used to probe close-range molecular interactions. Physically, the FRET phenomenon manifests as a dipole–dipole interaction between closely juxtaposed fluorescent molecules (10–100 Å). Our effort is to employ this FRET technique to make a prototype device for highly sensitive detection of environment pollutant. Among the most common environmental pollutants, nitroaromatic compounds (NACs) are of particular interest because of their durability and toxicity. That’s why, sensitive and selective detection of small amounts of nitroaromatic explosives, in particular, 2,4,6-trinitrophenol (TNP), 2,4-dinitrotoluene (DNT) and 2,4,6-trinitrotoluene (TNT) has been a critical challenge due to the increasing threat of explosive-based terrorism and the need of environmental monitoring of drinking and waste water. In addition, the excessive utilization of TNP in several other areas such as burn ointment, pesticides, glass and the leather industry resulted in environmental accumulation, and is eventually contaminating the soil and aquatic systems. To the date, high number of elegant methods, including fluorimetry, gas chromatography, mass, ion-mobility and Raman spectrometry have been successfully applied for explosive detection. Among these efforts, fluorescence-quenching methods based on the mechanism of FRET show good assembly flexibility, high selectivity and sensitivity. Here, we report a FRET-based sensor system for the highly selective detection of NACs, such as TNP, DNT and TNT. The sensor system is composed of a copolymer Poly [(N,N-dimethylacrylamide)-co-(Boc-Trp-EMA)] (RP) bearing tryptophan derivative in the side chain as donor and dansyl tagged copolymer P(MMA-co-Dansyl-Ala-HEMA) (DCP) as an acceptor. Initially, the inherent fluorescence of RP copolymer is quenched by non-radiative energy transfer to DCP which only happens once the two molecules are within Förster critical distance (R0). The excellent spectral overlap (Jλ= 6.08×10¹⁴ nm⁴M⁻¹cm⁻¹) between donors’ (RP) emission profile and acceptors’ (DCP) absorption profile makes them an exciting and efficient FRET pair i.e. further confirmed by the high rate of energy transfer from RP to DCP i.e. 0.87 ns⁻¹ and lifetime measurement by time correlated single photon counting (TCSPC) to validate the 64% FRET efficiency. This FRET pair exhibited a specific fluorescence response to NACs such as DNT, TNT and TNP with 5.4, 2.3 and 0.4 µM LODs, respectively. The detection of NACs occurs with high sensitivity by photoluminescence quenching of FRET signal induced by photo-induced electron transfer (PET) from electron-rich FRET pair to electron-deficient NAC molecules. The estimated stern-volmer constant (KSV) values for DNT, TNT and TNP are 6.9 × 10³, 7.0 × 10³ and 1.6 × 104 M⁻¹, respectively. The mechanistic details of molecular interactions are established by time-resolved fluorescence, steady-state fluorescence and absorption spectroscopy confirmed that the sensing process is of mixed type, i.e. both dynamic and static quenching as lifetime of FRET system (0.73 ns) is reduced to 0.55, 0.57 and 0.61 ns DNT, TNT and TNP, respectively. In summary, the simplicity and sensitivity of this novel FRET sensor opens up the possibility of designing optical sensor of various NACs in one single platform for developing multimodal sensor for environmental monitoring and future field based study.

Keywords: FRET, nitroaromatic, stern-Volmer constant, tryptophan and dansyl tagged copolymer

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Authors: Alexander Rodríguez-Hernández, Arnulfo Rojas-Perez, Liz Diaz-Vazquez

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The rise in consumerism over the past century has resulted in the creation of higher amounts of plasticizers, personal care products and other chemical substances, which enter and accumulate in water systems. Other sources of pollutants in Neotropical regions experience large inputs of nutrients with these pollutants resulting in eutrophication of water which consume large quantities of oxygen, resulting in high fish mortality. This dilemma has created a need for the development of targeted detection in complex matrices and remediation of emerging contaminants. We have synthesized carbon nanoparticles from macro algae (Ulva fasciata) by oxidizing the graphitic carbon network under extreme acidic conditions. The resulting material was characterized by STEM, yielding a spherical 12 nm average diameter nanoparticles, which can be fixed into a polysaccharide aerogel synthesized from the same macro algae. Spectrophotometer analyses show a pH dependent fluorescent behavior varying from 450-620 nm in aqueous media. Heavily oxidized edges provide for easy functionalization with enzymes for a more targeted analysis and remediation technique. Given the optical properties of the carbon base nanoparticles and the numerous possibilities of functionalization, we have developed a selective and robust targeted bio-detection and bioremediation technique for the treatment of emerging contaminants in complex matrices like estuarine embayment.

Keywords: aerogels, carbon nanoparticles, fluorescent, targeted analysis

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Authors: Kaouther Bergaui, Nafaa Reguigui, Charles Gary

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Characterization and the applications of deuterium-deuterium (DD) neutron generator developed by Adelphie technology and acquired by the National Centre of Nuclear Science and Technology (NCNST) were presented in this work. We study the performance of the neutron generator in terms of neutron yield, production efficiency, and the ionic current as a function of the acceleration voltage at various RF powers. We provide the design and optimization of the PGNAA chamber and thus give insight into the capabilities of the planned PGNAA facility. Additional non-destructive techniques were studied employing the DD neutron generator, such as PGNAA and neutron radiography: The PGNAA is used for determining the concentration of 10B in Si and SiO2 matrices by using a germanium detector HPGe and the results obtained are compared with PGNAA system using a Sodium Iodide detector (NaI (Tl)); Neutron radiography facility was tested and simulated, using a camera device CCD and simulated by the Monte Carlo code; and the explosive detection system (EDS) also simulated using the Monte Carlo code. The study allows us to show that the new models of DD neutron generators are feasible and that superior-quality neutron beams could be produced and used for various applications. The feasibility of Boron neutron capture therapy (BNCT) for cancer treatment using a neutron generator was assessed by optimizing Beam Shaping Assembly (BSA) on a phantom using Monte-Carlo (MCNP6) simulations.

Keywords: neutron generator deuterium-deuterium, Monte Carlo method, radiation, neutron flux, neutron activation analysis, born, neutron radiography, explosive detection, BNCT

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Authors: Anna Daskalaki, Andreas Ashikalis

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This is a conceptual project that suggests an alternative type of summer camp in the forest of Rouvas in the island of Crete. Taking into account some feasts that are organised by the locals or mountaineering clubs near the church of St. John, we created a network of lightweight timber structures that serve the needs of the visitor. These structures are transformable and satisfy the need for rest, food, and sleep – this means a seat, a table and a tent are embodied in each structure. These structures blend in with the environment as they are being installed according to the following parameters: (a) the local relief, (b) the clusters of trees, and (c) the existing paths. Each timber structure could be considered as a module that could be totally independent or part of a bigger construction. The design showcases the advantages of a timber structure as it can be quite adaptive to the needs of the project, but also it is a sustainable and environmentally friendly material that can be recycled. Finally, it is important to note that the basic goal of this project is the minimum alteration of the natural environment.

Keywords: lightweight structures, timber, transformable, tent

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Authors: Dirk V. H. K. Franco

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For many of us a lot of phenomena are considered a risk. Examples are: climate change, decrease of biodiversity, amount of available, clean water and the decreasing variety of living organism in the oceans. On the other hand a lot of people perceive the following trends as catastrophic: the sea level, the melting of the pole ice, the numbers of tornado’s, floods and forest fires, the national security and the potential of 192 million climate migrants in 2060. The interest for climate, health and the possible solutions is large and common. The 5th IPCC states that the last decades especially human activities (and in second order natural emissions) have caused large, mainly negative impacts on our ecological environments. Chris Stringer stated that we represent, nowadays after evolution, the only one version of the possible humanity. At this very moment we are faced with an (over) crowded planet together with global climate changes and a strong demand for energy and material resources. Let us hope that we can counter these difficulties either with better application of existing technologies or by inventing new (applications of) clean technologies together with new business models.

Keywords: clean technologies, catastrophic, climate, possible solutions

Procedia PDF Downloads 500

Authors: Mace Bentley, Zhuojun Duan, Tobias Gerken, Dudley Bonsal, Henry Way, Endre Szakal, Mia Pham, Hunter Donaldson, Chelsea Lang, Hayden Abbott, Leah Wilcynzski

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Air pollution has the potential to modify the lifespan and intensity of thunderstorms and the properties of lightning. Using data mining and geovisualization, we investigate how background climate and weather conditions shape variability in urban air pollution and how this, in turn, shapes thunderstorms as measured by the intensity, distribution, and frequency of cloud-to-ground lightning. A spatiotemporal analysis was conducted in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million lightning flashes were used to identify more than 196,000 thunderstorms that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. Each lightning flash in the dataset was grouped into thunderstorm events by means of a temporal and spatial clustering algorithm. Once the thunderstorm event database was constructed, hourly wind direction, wind speed, and atmospheric thermodynamic data were added to the initiation and dissipation times and locations for the 196,000 identified thunderstorms. Hourly aerosol and air quality data for the thunderstorm initiation times and locations were also incorporated into the dataset. Developing thunderstorm climatologies using a lightning tracking algorithm and lightning detection network data was found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region.

Keywords: lightning, urbanization, thunderstorms, climatology

Procedia PDF Downloads 77