Search results for: early fire detection

Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Ana C. V. Freitas, Luciana B. M. Pires, Joao P. Martins

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In recent years, fire dynamics in deforestation areas of tropical forests have received considerable attention because of their relationship to climate change. Climate models project great increases in the frequency and area of drought in the Amazon region, which may increase the occurrence of fires. This study analyzes the historical record number of fire outbreaks in 2017 using satellite-derived data sets of active fire detections, burned area, precipitation, and data of the Fire Program from the Center for Weather Forecasting and Climate Studies (CPTEC/INPE). A downward trend in the number of fire outbreaks occurred in the first half of 2017, in relation to the previous year. This decrease can be related to the fact that 2017 was not an El Niño year and, therefore, the observed rainfall and temperature in the Amazon region was close to normal conditions. Meanwhile, the worst period in history for fire outbreaks began with the subsequent arrival of the dry season. September of 2017 exceeded all monthly records for number of fire outbreaks per month in the entire series. This increase was mainly concentrated in Bolivia and in the states of Amazonas, northeastern Pará, northern Rondônia and Acre, regions with high densities of rural settlements, which strongly suggests that human action is the predominant factor, aggravated by the lack of precipitation during the dry season allowing the fires to spread and reach larger areas. Thus, deforestation in the Amazon is primarily a human-driven process: climate trends may be providing additional influences.

Keywords: Amazon forest, climate change, deforestation, human-driven process, fire outbreaks

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Authors: Sitrapee Changmuenwai, Sudaratana Wongweragiat

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It is important that a dry chemical fire extinguisher must be inspected for its readiness. For each inspection, the inspectors need to turn the fire extinguisher tank upside down to let the chemical inside the tank move and prevent solidification, which would make the tank not ready for usage when needed. Each tank weighs approximately 16 kg. The inspectors have to turn each tank upside down twice (2 minutes/round). They need to put the tanks over their shoulder close to their ear in order to hear the chemical flow inside the tank or use their hands to feel it. The survey and questionnaire 'The Questionnaire Know Body', which includes neck, left shoulder, upper and lower right arms suggest that all 12 security staffs have the same fatigues. The current dry chemical fire extinguisher inspection affects various ergonomic health problems. Rapid Entire Body Assessment (REBA) is used for evaluation of posture risks so that the working postures may be redesigned or corrected. The dry chemical fire extinguisher inspection equipment has been developed to reduce ergonomic health risks for the inspectors. A REBA analysis has been performed again, and the risk score has been decreased from 13 to 3. In addition, feedbacks from the first trial of the developed equipment show that there are demands to increase the installation in order to reduce the ergonomic health risks.

Keywords: dry chemical fire extinguisher inspection equipment, ergonomic, REBA, rapid entire body assessment

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Authors: Hammad Aziz

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Intumescent fire retardant coating (IFRC) is applied on the surface of material requiring fire protection. In this research work, IFRC’s were developed using ammonium polyphosphate, expandable graphite, melamine, boric acid, zinc borate, mica, magnesium oxide, and bisphenol A BE-188 with polyamide polyamine H-4014 as curing agent. Formulations were prepared using nano size MgO and compared with control formulation i.e. without nano size MgO. Small scale hydrocarbon fire test was conducted to scrutinize the thermal performance of the coating. Char and coating were further characterized by using FESEM, FTIR, EDS, TGA and DTGA. Thus, Intumescent coatings reinforced with 2 wt. % of nano-MgO (rod shaped particles) provide superior thermal performance and uniform microstructure of char due to well dispersion of nano particles.

Keywords: intumescent coating, char, SEM, TGA

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Authors: Muhammad Hassan Khalil, Xu Jiadong

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Detection of malignant tumor inside the breast of women is a challenging field for the researchers. MWI (Microwave imaging) for breast cancer diagnosis has been of interest for last two decades, newly it suggested for finding cancerous tissues of women breast. A simple and basic idea of the mathematical modeling is used throughout this paper for imaging of malignant tumor. In this paper, the authors explained inverse scattering method in the microwave imaging and also present some simulation results.

Keywords: breast cancer detection, microwave imaging, tomography, tumor

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Authors: Aileen F. Wang

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Mammography has been one of the most reliable methods for early detection and diagnosis of breast cancer. However, mammography misses about 17% and up to 30% of breast cancers due to the subtle and unstable appearances of breast cancer in their early stages. Recent computer-aided diagnosis (CADx) technology using Zernike moments has improved detection accuracy. However, it has several drawbacks: it uses manual segmentation, Zernike moments are not robust, and it still has a relatively high false negative rate (FNR)–17.6%. This project will focus on the development of a novel breast cancer detection algorithm to automatically segment the breast mass and further reduce FNR. The algorithm consists of automatic segmentation of a single breast mass using Point Region Growing Segmentation, reconstruction of the segmented breast mass using Pseudo-Zernike moments, and classification of the breast mass using the root mean square (RMS). A comparative study among the various algorithms on the segmentation and reconstruction of breast masses was performed on randomly selected mammographic images. The results demonstrated that the newly developed algorithm is the best in terms of accuracy and cost effectiveness. More importantly, the new classifier RMS has the lowest FNR–6%.

Keywords: computer aided diagnosis, mammography, point region growing segmentation, pseudo-zernike moments, root mean square

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Xuelin Zhang, Shouxiang Lu, Changhai Li

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The commercial retardant flame polycarbonate samples as the main high speed train interior carriage material with different thicknesses were investigated in Fire Propagation Apparatus with different external heat fluxes under different oxygen concentration from 12% to 40% to study the fire characteristics and quantitatively analyze the ignition time, mass loss rate and heat blockage. The additives of commercial retardant flame polycarbonate were intumescent and maintained a steady height before ignition when heated. The results showed the transformed ignition time (1/t_ig)ⁿ increased linearly with external flux under different oxygen concentration after deducting the heat blockage due to pyrolysis products, the mass loss rate was taken on linearly with external heat fluxes and the slop of the fitting line for mass loss rate and external heat fluxes decreased with the enhanced oxygen concentration and the heat blockage independent on external heat fluxes rose with oxygen concentration increasing. The inquired data as the input of the fire simulation model was the most important to be used to evaluate the fire risk of commercial retardant flame polycarbonate.

Keywords: ignition time, mass loss rate, heat blockage, fire characteristic

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Authors: Ji Hun Choi, Seung Un Chae, Kyeong Suk Cho

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Synthetic resins are widely used in various fields including electricity, engineering, construction and agriculture. Many of interior and exterior finishing materials for buildings are synthetic resin products. In this study, full-scale fire tests were conducted on polyvinyl chloride, polypropylene and urethane in accordance with the “ISO 9705: Fire test - Full-scale room test for surface products” to measure heat release rate, toxic gas emission and smoke production rate. Based on the tests, fire growth pattern and fire risk were analyzed. Findings from the tests conducted on polyvinyl chloride and urethane are as follows. The total heat release rate and total smoke production rate of polyvinyl chloride were 98.89MW and 5284.41m2, respectively and its highest CO2 concentration was 0.149%. The values obtained from the test with urethane were 469.94 MW, 3396.28 m2 and 1.549%. While heat release rate and CO2 concentration were higher in urethane implying its high combustibility, smoke production rate was 1.5 times higher in polyvinyl chloride. Follow-up tests are planned to be conducted to accumulate data for the evaluation of heat emission and fire risk associated with synthetic resins.

Keywords: synthetic resins, fire test, full-scale test, heat release rate, smoke production rate, polyvinyl chloride, polypropylene, urethane

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Authors: Zeynep R. Ege, Aydin Akan, Faik N. Oktar, Betul Karademir, Oguzhan Gunduz

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Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.

Keywords: chitosan, coaxial electrospinning, controlled releasing, drug delivery, indocyanine green, polyethylene oxide

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Authors: Suzanne Zazycki, Bamidele Osamika, Heather Craska, Kaelyn Conaway, Reena Murphy, Stephanie Spence

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Invasive Plant Species (IPS) are an important component of effective preservation and conservation of natural lands management. IPS are non-native plants which can aggressively encroach upon native species and pose a significant threat to the ecology, public health, and social welfare of a community. The presence of IPS in U.S. nature preserves has caused economic costs, which has estimated to exceed $26 billion a year. While different methods have been identified to control IPS, few methods have been recognized for early detection of IPS. This study examined identified methods for early detection of IPS in Hueston Woods State Nature Preserve. Mixed methods research design was adopted in this four-phased study. The first phase entailed data gathering, the phase described the characteristics and qualities of IPS and the importance of early detection (ED). The second phase explored ED methods, Geographic Information Systems (GIS) and Citizen Science were discovered as ED methods for IPS. The third phase of the study involved the creation of hotspot maps to identify likely areas for IPS growth. While the fourth phase involved testing and evaluating mobile applications that can support the efforts of citizen scientists in IPS detection. Literature reviews were conducted on IPS and ED methods, and four regional experts from ODNR and Miami University were interviewed. A questionnaire was used to gather information about ED methods used across the state. The findings revealed that geospatial methods, including Unmanned Aerial Vehicles (UAVs), Multispectral Satellites (MSS), and Normalized Difference Vegetation Index (NDVI), are not feasible for early detection of IPS, as they require GIS expertise, are still an emerging technology, and are not suitable for every habitat for the ED of IPS. Therefore, Other ED methods options were explored, which include predicting areas where IPS will grow, which can be done through monitoring areas that are like the species’ native habitat. Through literature review and interviews, IPS are known to grow in frequently disturbed areas such as along trails, shorelines, and streambanks. The research team called these areas “hotspots” and created maps of these hotspots specifically for HW NP to support and narrow the efforts of citizen scientists and staff in the ED of IPS. The results further showed that utilizing citizen scientists in the ED of IPS is feasible, especially through single day events or passive monitoring challenges. The study concluded that the creation of hotspot maps to direct the efforts of citizen scientists are effective for the early detection of IPS. Several recommendations were made, among which is the creation of hotspot maps to narrow the ED efforts as citizen scientists continues to work in the preserves and utilize citizen science volunteers to identify and record emerging IPS.

Keywords: early detection, hueston woods state nature preserve, invasive plant species, hotspots

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

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Authors: Daniel Martin Fellows, Sean P. Walton, Jennifer Thompson, Oubay Hassan, Ella Quigley, Kevin Tinkham

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Fire performance is a crucial aspect to consider when designing cladding products, and testing this performance is extremely expensive. Appropriate use of numerical simulation of fire performance has the potential to reduce the total number of fire tests required when designing a product by eliminating poor-performing design ideas early in the design phase. Due to the complexity of fire and the large spectrum of failures it can cause, multi-disciplinary models are needed to capture the complex fire behavior and its structural effects on its surroundings. Working alongside Tata Steel U.K., the authors have focused on completing a coupled CFD-FEA simulation model suited to test Polyisocyanurate (PIR) based sandwich panel products to gain confidence before costly experimental standards testing. The sandwich panels are part of a thermally insulating façade system primarily for large non-domestic buildings. The work presented in this paper compares two coupling methodologies of a replicated physical experimental standards test LPS 1181-1, carried out by Tata Steel U.K. The two coupling methodologies that are considered within this research are; one-way and two-way. A one-way coupled analysis consists of importing thermal data from the CFD solver into the FEA solver. A two-way coupling analysis consists of continuously importing the updated changes in thermal data, due to the fire's behavior, to the FEA solver throughout the simulation. Likewise, the mechanical changes will also be updated back to the CFD solver to include geometric changes within the solution. For CFD calculations, a solver called Fire Dynamic Simulator (FDS) has been chosen due to its adapted numerical scheme to focus solely on fire problems. Validation of FDS applicability has been achieved in past benchmark cases. In addition, an FEA solver called ABAQUS has been chosen to model the structural response to the fire due to its crushable foam plasticity model, which can accurately model the compressibility of PIR foam. An open-source code called FDS-2-ABAQUS is used to couple the two solvers together, using several python modules to complete the process, including failure checks. The coupling methodologies and experimental data acquired from Tata Steel U.K are compared using several variables. The comparison data includes; gas temperatures, surface temperatures, and mechanical deformation of the panels. Conclusions are drawn, noting improvements to be made on the current coupling open-source code FDS-2-ABAQUS to make it more applicable to Tata Steel U.K sandwich panel products. Future directions for reducing the computational cost of the simulation are also considered.

Keywords: fire engineering, numerical coupling, sandwich panels, thermo fluids

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Authors: Shah Abbas

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Rheumatoid arthritis is a systemic, inflammatory autoimmune disease, affecting an overall 1% of the global population. Despite being tremendous efforts by scientists, early diagnosis of RA still has not been achieved. In the current study, a Graphene oxide (GO) based electrochemical sensor has been developed for early diagnosis of RA through Cyclic voltammetry. Chitosan (CHI), a CPnatural polymer has also been incorporated along with GO in order to enhance the biocompatibility and functionalization potential of the biosensor. CCPs are known antigens for Anti Citrullinated Peptide Antibodies (ACPAs) which can be detected in serum even 14 years before the appearance of symptoms, thus they are believed to be an ideal target for the early diagnosis of RA. This study has yielded some promising results regarding the binding and detection of ACPAs through changes in the electrochemical properties of biosensing material. The cyclic voltammogram of this biosensor reflects the binding of ACPAs to the biosensor surface, due to its shifts observed in the current flow (cathodic current) as compared to the when no ACPAs bind as it is absent in RA negative patients.

Keywords: rheumatoid arthritis, peptide sensor, graphene oxide, anti citrullinated peptide antibodies, cyclic voltammetry

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

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Authors: Coline Bretz, Andrea Vaccaro, Dario Leumann

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The food, personal care, and cosmetic industries are seeing increased consumer demand for more sustainable and innovative ingredients. When developing new formulations incorporating such ingredients, stability is one of the first criteria that must be assessed, and it is thus of great importance to have a method that can detect instabilities early and quickly. Diffusing Wave Spectroscopy (DWS) is a light scattering technique that probes the motion,i.e., the mean square displacement (MSD), of colloids, such as nanoparticles in a suspension or droplets in emulsions. From the MSD, the rheological properties of the surrounding medium can be determined via the so-called microrheology approach. In the case of purely viscous media, it is also possible to obtain information about particle size. DWS can thus be used to monitor the size evolution of particles, droplets, or bubbles in aging dispersions, emulsions, or foams. In the context of early instability detection in emulsions, DWS offers considerable advantages, as the samples are measured in a contact-free manner, using only small quantities of samples loaded in a sealable cuvette. The sensitivity and rapidity of the technique are key to detecting and following the ageing of emulsions reliably. We present applications of DWS focused on the characterization of emulsions. In particular, we demonstrate the ability to record very subtle changes in the structural properties early on. We also discuss the various mechanisms at play in the destabilization of emulsions, such as coalescence or Ostwald ripening, and how to identify them through this technique.

Keywords: instrumentation, emulsions, stability, DWS

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Authors: Yeo Kyeong Lee, Ji Yeon Kang, Eun Mi Ryu, Hee Sun Kim, Yeong Soo Shin

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The objective of this paper is an investigation of the effects of the thickness and axial loading during a fire test on the load-bearing capacity of a fire-damaged normal-strength concrete wall. Two factors are attributed to the temperature distributions in the concrete members and are mainly obtained through numerous experiments. Toward this goal, three wall specimens of different thicknesses are heated for 2 h according to the ISO-standard heating curve, and the temperature distributions through the thicknesses are measured using thermocouples. In addition, two wall specimens are heated for 2 h while simultaneously being subjected to a constant axial loading at their top sections. The test results show that the temperature distribution during the fire test depends on wall thickness and axial load during the fire test. After the fire tests, the specimens are cured for one month, followed by the loading testing. The heated specimens are compared with three unheated specimens to investigate the residual load-bearing capacities. The fire-damaged walls show a minor difference of the load-bearing capacity regarding the axial loading, whereas a significant difference became evident regarding the wall thickness. To validate the experiment results, finite element models are generated for which the material properties that are obtained for the experiment are subject to elevated temperatures, and the analytical results show sound agreements with the experiment results. The analytical method based on validated thought experimental results is applied to generate the fire-damaged walls with 2,800 mm high considering the buckling effect: typical story height of residual buildings in Korea. The models for structural analyses generated to deformation shape after thermal analysis. The load-bearing capacity of the fire-damaged walls with pin supports at both ends does not significantly depend on the wall thickness, the reason for it is restraint of pinned ends. The difference of the load-bearing capacity of fire-damaged walls as axial load during the fire is within approximately 5 %.

Keywords: normal-strength concrete wall, wall thickness, axial-load ratio, slenderness ratio, fire test, residual strength, finite element analysis

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Muhammad Ali Syed, Arshad Saleem Bhatti, Chen-zhong Li, Habib Ali Bokhari

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Surface plasmon resonance (SPR) biosensors have emerged as a promising technique for bioanalysis as well as microbial detection and identification. Real time, sensitive, cost effective, and label free detection of biomolecules from complex samples is required for early and accurate diagnosis of infectious diseases. Like many other types of optical techniques, SPR biosensors may also be successfully utilized for microbial detection for accurate, point of care, and rapid results. In the present study, we have utilized a commercially available automated SPR biosensor of BI company to study the microbial detection form water samples spiked with different concentration of Staphylococcus aureus bacterial cells. The gold thin film sensor surface was functionalized to react with proteins such as protein G, which was used for directed immobilization of monoclonal antibodies against Staphylococcus aureus. The results of our work reveal that this immunosensor can be used to detect very small number of bacterial cells with higher sensitivity and specificity. In our case 10^3 cells/ml of water have been successfully detected. Therefore, it may be concluded that this technique has a strong potential to be used in microbial detection and identification.

Keywords: surface plasmon resonance (SPR), Staphylococcus aureus, biosensors, microbial detection

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Dinesh Gundavaram

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This paper presents an investigation on bridge damage detection based on the dynamic responses estimated from a passing vehicle. A numerical simulation of steel truss bridge for railway was used in this investigation. The bridge response at different locations is measured using CSI-Bridge software. Several damage scenarios are considered including different locations and severities. The possibilities of dynamic properties of global modes in the identification of structural changes in truss bridges were discussed based on the results of measurement.

Keywords: bridge, damage, dynamic responses, detection

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Authors: A. T. Kassem

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Fires areconsidered a common hazardous action that any building may face. Most buildings’ structural elements are designed, taking into consideration precautions for fire safety, using deterministic design approaches. Public and highly important buildings are commonly designed considering standard fire rating and, in many cases, contain large compartments with central domes. Real fire scenarios are not commonly brought into action in structural design of buildings because of complexities in both scenarios and analysis tools. This paper presents a modern approach towards analysis of spherical domes in real fire condition via implementation of building information modelling, and adopting a probabilistic approach. BIMhas been implemented to bridge the gap between various software packages enabling them to function interactively to model both real fire and corresponding structural response. Ventilation controlled fires scenarios have been modeled using both “Revit” and “Pyrosim”. Monte Carlo simulation has been adopted to engage the probabilistic analysis approach in dealing with various parameters. Conclusions regarding failure probability and fire endurance, in addition to the effects of various parameters, have been extracted.

Keywords: concrete, spherical domes, ventilation controlled fires, BIM, monte carlo simulation, pyrosim, revit

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Authors: Mira Ezora Zainal Abidin, Siti Fauzuna Othman, Zalina Harun, M. Hafiz M. Pikri

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In a fire incident involving a Nitrogen storage tank that over-pressured and exploded, resulting in a fire in one of the units in a refinery, lack of data and evidence hampered the investigation to determine the root cause. Instrumentation and fittings were destroyed in the fire. To make it worst, this incident occurred during the COVID-19 pandemic, making collecting and testing evidence delayed. In addition to that, the storage tank belonged to a third-party company which requires legal agreement prior to the refinery getting approval to test the remains. Despite all that, the investigation had to be carried out with stakeholders demanding answers. The investigation team had to devise alternative means to support whatever little evidence came out as the most probable root cause. International standards, practices, and previous incidents on similar tanks were referred. To narrow down to just one root cause from 8 possible causes, transient simulations were conducted to simulate the overpressure scenarios to prove and eliminate the other causes, leaving one root cause. This paper shares the methodology used and details how transient simulations were applied to help solve this. The experience and lessons learned gained from the event investigation and from numerous case studies via transient analysis in finding the root cause of the accident leads to the formulation of future mitigations and design modifications aiming at preventing such incidents or at least minimize the consequences from the fire incident.

Keywords: fire, transient, simulation, relief

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

Abstract:

Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Shahram Jamali, Samira Hamed

Abstract:

One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Ben Thompson

Abstract:

Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. Currently, many children with strabismus remain undiagnosed until school entry because current automated screening methods have limited success in the preschool age range. A method for strabismus detection using eye alignment stability (EAS) is proposed. This method starts with face detection, followed by facial landmark detection, eye region segmentation, eye gaze extraction, and eye alignment stability estimation. Binarization and morphological operations are performed for segmenting the pupil region from the eye. After finding the EAS, its absolute value is used to differentiate the strabismic eye from the non-strabismic eye. If the value of the eye alignment stability is greater than a particular threshold, then the eyes are misaligned, and if its value is less than the threshold, the eyes are aligned. The method was tested on 175 strabismic and non-strabismic images obtained from Kaggle and Google Photos. The strabismic eye is taken as a positive class, and the non-strabismic eye is taken as a negative class. The test produced a true positive rate of 100% and a false positive rate of 7.69%.

Keywords: strabismus, face detection, facial landmarks, eye segmentation, eye gaze, binarization

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Authors: Jeremy Johnson, Craig Nitschke, Luke Kelly

Abstract:

Fire regimes, climate and topographic gradients interact to influence ecosystem structure and function across fire-prone, montane landscapes worldwide. Biota have developed a range of adaptations to historic fire regime thresholds, which allow them to persist in these environments. In south-eastern Australia, a signal of fire regime changes is emerging across these landscapes, and anthropogenic climate change is likely to be one of the main drivers of an increase in burnt area and more frequent wildfire over the last 25 years. This shift has the potential to modify vegetation structure and composition at broad scales, which may lead to landscape patterns to which biota are not adapted, increasing the likelihood of local extirpation of some mammal species. This study aimed to address concerns related to the influence of abrupt changes in fire regimes on mammal populations in montane landscapes. It first examined the impact of climate, topography, and vegetation on fire patterns and then explored the consequences of these changes on mammal populations and their habitats. Field studies were undertaken across diverse vegetation, fire severity and fire frequency gradients, utilising camera trapping and passive acoustic monitoring methodologies and the collection of fine-scale vegetation data. Results show that drought is a primary contributor to fire regime shifts at the landscape scale, while topographic factors have a variable influence on wildfire occurrence at finer scales. Frequent, high severity wildfire influenced forest structure and composition at broad spatial scales, and at fine scales, it reduced occurrence of hollow-bearing trees and promoted coarse woody debris. Mammals responded differently to shifts in forest structure and composition depending on their habitat requirements. This study highlights the complex interplay between fire regimes, environmental gradients, and biotic adaptations across temporal and spatial scales. It emphasizes the importance of understanding complex interactions to effectively manage fire-prone ecosystems in the face of climate change.

Keywords: fire, ecology, biodiversity, landscape ecology

Procedia PDF Downloads 43