Search results for: model-free damage detection

Authors: Can Mungan, Gokhan Kilic

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Structural engineers and tunnel owners have good reason to attach importance to the assessment and inspection of tunnels. Regular inspection is necessary to maintain and monitor the health of the structure not only at the present time but throughout its life cycle. Detection of flaws within the structure, such as corrosion and the formation of cracks within the internal elements of the structure, can go a long way to ensuring that the structure maintains its integrity over the course of its life. Other issues that may be detected earlier through regular assessment include tunnel surface delamination and the corrosion of the rebar. One advantage of new technology such as the ground penetrating radar (GPR) is the early detection of imperfections. This study will aim to discuss and present the effectiveness of GPR as a tool for assessing the structural integrity of the heavily used tunnel. GPR is used with various antennae in frequency and application method (2 GHz and 500 MHz GPR antennae). The paper will attempt to produce a greater understanding of structural defects and identify the correct tool for such purposes. Conquest View with 3D scanning capabilities was involved throughout the analysis, reporting, and interpretation of the results. This study will illustrate GPR mapping and its effectiveness in providing information of value when it comes to rebar position (lower and upper reinforcement). It will also show how such techniques can detect structural features that would otherwise remain unseen, as well as moisture ingress.

Keywords: tunnel, GPR, health monitoring, moisture ingress, rebar position

Procedia PDF Downloads 119

Authors: Tarek Dandashi

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WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

Procedia PDF Downloads 262

Authors: Reza Fardid, Aliyeh Alipour

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Introduction: Coronary heart disease (CHD) is the most common and deadliest diseases. A coronary angiography is an important tool for the diagnosis and treatment of this disease. Because angiography is performed by exposure to ionizing radiation, it can lead to harmful effects. Ionizing radiation induces double-stranded breaks in DNA, which is a potentially life-threatening injury. The purpose of the present study is an investigation of the phosphorylation of histone H2AX in the location of the double-stranded break in Peripheral blood lymphocytes as an indication of Biological effects of radiation on angiography patients. Materials and Methods: This method is based on measurement of the phosphorylation of histone (gamma-H2AX, gH2AX) level on serine 139 after formation of DNA double-strand break. 5 cc of blood from 24 patients with angiography were sampled before and after irradiation. Blood lymphocytes were removed, fixed and were stained with specific ϒH2AX antibodies. Finally, ϒH2AX signal as an indicator of the double-strand break was measured with Flow Cytometry Technique. Results and discussion: In all patients, an increase was observed in the number of breaks in double-stranded DNA after irradiation (20.15 ± 14.18) compared to before exposure (1.52 ± 0.34). Also, the mean of DNA double-strand break was showed a linear correlation with DAP. However, although induction of DNA double-strand breaks associated with radiation dose in patients, the effect of individual factors such as radiosensitivity and regenerative capacity should not be ignored. If in future we can measure DNA damage response in every patient angiography and it will be used as a biomarker patient dose, will look very impressive on the public health level. Conclusion: Using flow cytometry readings which are done automatically, it is possible to detect ϒH2AX in the number of blood cells. Therefore, the use of this technique could play a significant role in monitoring patients.

Keywords: coronary angiography, DSB of DNA, ϒH2AX, ionizing radiation

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Authors: Farmesk Abubaker, Francesco Tortorici, Marco Capogni, Concetta Sutera, Vincenzo Bellini

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This project concerns with the detection efficiency of the portable triple-to-double coincidence ratio (TDCR) at the National Institute of Metrology of Ionizing Radiation (INMRI-ENEA) which allows direct activity measurement and radionuclide standardization for pure-beta emitter or pure electron capture radionuclides. The dependency of the simulated detection efficiency of the TDCR, by using Monte Carlo simulation Geant4 code, on the Birks factor (kB) and defocusing parameter has been examined especially for low energy beta-emitter radionuclides such as 3H and 14C, for which this dependency is relevant. The results achieved in this analysis can be used for selecting the best kB factor and the defocusing parameter for computing theoretical TDCR parameter value. The theoretical results were compared with the available ones, measured by the ENEA TDCR portable detector, for some pure-beta emitter radionuclides. This analysis allowed to improve the knowledge of the characteristics of the ENEA TDCR detector that can be used as a traveling instrument for in-situ measurements with particular benefits in many applications in the field of nuclear medicine and in the nuclear energy industry.

Keywords: Birks constant, defocusing parameter, GEANT4 code, TDCR parameter

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Authors: L. Pavithra, R. Sharmila, Shivani Sridhar

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Columns severe structural damage needs proportioning a significant portion of earthquake energy can be dissipated yielding in the beams. Presence of axial load along with cyclic loading has a significant influence on column. The objective of this paper is to present the analytical results of columns subjected to biaxial cyclic loading.

Keywords: RC column, Seismic behaviour, cyclic behaviour, biaxial testing, ductile behaviour

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: Laura Elena De León Prado, Dora Alicia Cortés Hernández, Javier Sánchez

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Among the different cancer treatments that are currently used, hyperthermia has a promising potential due to the multiple benefits that are obtained by this technique. In general terms, hyperthermia is a method that takes advantage of the sensitivity of cancer cells to heat, in order to damage or destroy them. Within the different ways of supplying heat to cancer cells and achieve their destruction or damage, the use of magnetic nanoparticles has attracted attention due to the capability of these particles to generate heat under the influence of an external magnetic field. In addition, these nanoparticles have a high surface area and sizes similar or even lower than biological entities, which allow their approaching and interaction with a specific region of interest. The most used magnetic nanoparticles for hyperthermia treatment are those based on iron oxides, mainly magnetite and maghemite, due to their biocompatibility, good magnetic properties and chemical stability. However, in order to fulfill more efficiently the requirements that demand the treatment of magnetic hyperthermia, there have been investigations using ferrites that incorporate different metallic ions, such as Mg, Mn, Co, Ca, Ni, Cu, Li, Gd, etc., in their structure. This paper reports the synthesis of nanosized Mg_xMn_1-xFe₂O₄ (x = 0.3 and 0.4) ferrites by sol-gel method and their evaluation in terms of heating capability and in vitro hemolysis to determine the potential use of these nanoparticles as thermoseeds for the treatment of cancer by magnetic hyperthermia. It was possible to obtain ferrites with nanometric sizes, a single crystalline phase with an inverse spinel structure and a behavior near to that of superparamagnetic materials. Additionally, at concentrations of 10 mg of magnetic material per mL of water, it was possible to reach a temperature of approximately 45°C, which is within the range of temperatures used for the treatment of hyperthermia. The results of the in vitro hemolysis assay showed that, at the concentrations tested, these nanoparticles are non-hemolytic, as their percentage of hemolysis is close to zero. Therefore, these materials can be used as thermoseeds for the treatment of cancer by magnetic hyperthermia.

Keywords: ferrites, heating capability, hemolysis, nanoparticles, sol-gel

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Makda Yeshitela Kifele

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Hair treatment prevents the hair from loss of volume, changing colour, and damaging its properties of the hair. Hair is the beauty of human beings that makes people beautiful and takes the other hearts to see them and to give them an appreciation for their effort to treat their hair and save it from damage. There are different methods to protect human hair from loss and damage that influence human psychology better than the problems. Chemicals products are available in the world that keeps safely the hair and provide beauty for the hair. But chemical products have side effects and are not cost-effective. Even some of the chemicals are allergic for users and left some changes in the hair. Indigenous hair treatment is an effective method that reduces the bad effects and the problems of the chemical that are lefts in human being’slife. Indigenous hair treatment can treat the hair safely and effectively that does not have much effect or spots in the human hair the users rather, it improves some attributes of the hair such that shine, quality, quantity improvements, length, and flexibility can be modified by these indigenous treatments. Rate is the local plant that plays a significant role in hair treatment. Rate is the local plant that can be available everywhere in the country, and anybody can be used for hair treatments. For this research, 50 women are identified as sample populations with different hair characteristics. The treatments were collected from the fields and squeezed into the pots to be prepared as specimens. The squeezed plants were deposited in the refrigerator for three days with some amounts of salts to prevent some bacteria. Chemical analysis has been done to sort out some detrimental substances. So the result showed that there are no detrimental substances that affect the hair properties and the health of the users. The sample population used the oil for one month without any other oily cosmetics that disturbs the treatment. The output is very effective and brings shining the hair, preventing greying of the hair, showing fast-growing, increasing the volume of the hair, and becoming flexible and curly, straight hair, thicker, and with no allergic effects.

Keywords: indigenous, chemicals, curly, treatment

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Authors: Chukwukelue Uzodinma Umenyilorah

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The history of the crises in Niger-Delta is readily traceable to the post-colonial oil boom of the early 70s. Prior to this time, it was widely believed that the people of Niger-Delta; especially those in the present day Rivers, Delta and Bayelsa States enjoyed a peaceful coexistence pretty much as the rest of Nigerians. In the early 70s however, crude oil was discovered in commercial quantities in these areas and tranquility has become a far cry over the years ever since then. First, a number of multi-national oil explorers moved into the Niger-Delta for business, and then certain conditions resulted in sundry instances of oil spillage, which caused a lot of environmental damage, destroying nearly all of the people’s sources of livelihood. The result was a multiple chain reaction ranging from incessant agitations from the natives to institutionalized dialogue between the oil business owners, the natives and the government, and then to a proposition of compensation packages for the affected communities. The said compensation, which was meant to bring peace seem to have brought even more crises instead. Corruption and greed crept in, money changed hands, suffering increased and so was the agitation from the people. The whole turn of events gradually snowballed into the formation of various militant groups who are now fingered as responsible for the sundry cases of violence in the Niger-Delta. The oil boom can, therefore, be said to be the immediate cause of the Niger-Delta crises, but there are other remote causes as well; including poverty, neglect and illiteracy to mention but a few. This study is therefore aimed at examining the various reasons behind the seemingly unending crises in the Niger-Delta. It will also take a critical look at the roles played by the various parties in the Niger-Delta crises from the 70s to date; as well as the various human and environmental devastations done in the area with a view to making informed suggestions on how to stop further damage and start fixing that, which is already done. Esiaba Irobi’s Cemetery Road and Hangmen Also Die seem to vividly capture the realities of the Niger-Delta situation, and shall, therefore, be reviewed in this study.

Keywords: corruption, Niger-delta, oil boom, post-colonial

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Authors: J. J. Rickard, A. Belli, P. Goldberg Oppenheimer

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Medical diagnostics, environmental monitoring, homeland security and forensics increasingly demand specific and field-deployable analytical technologies for quick point-of-care diagnostics. Although technological advancements have made optical methods well-suited for miniaturization, a highly-sensitive detection technique for minute sample volumes is required. Raman spectroscopy is a well-known analytical tool, but has very weak signals and hence is unsuitable for trace level analysis. Enhancement via localized optical fields (surface plasmons resonances) on nanoscale metallic materials generates huge signals in surface-enhanced Raman scattering (SERS), enabling single molecule detection. This enhancement can be tuned by manipulation of the surface roughness and architecture at the sub-micron level. Nevertheless, the development and application of SERS has been inhibited by the irreproducibility and complexity of fabrication routes. The ability to generate straightforward, cost-effective, multiplex-able and addressable SERS substrates with high enhancements is of profound interest for SERS-based sensing devices. While most SERS substrates are manufactured by conventional lithographic methods, the development of a cost-effective approach to create nanostructured surfaces is a much sought-after goal in the SERS community. Here, a method is established to create controlled, self-organized, hierarchical nanostructures using electrohydrodynamic (HEHD) instabilities. The created structures are readily fine-tuned, which is an important requirement for optimizing SERS to obtain the highest enhancements. HEHD pattern formation enables the fabrication of multiscale 3D structured arrays as SERS-active platforms. Importantly, each of the HEHD-patterned individual structural units yield a considerable SERS enhancement. This enables each single unit to function as an isolated sensor. Each of the formed structures can be effectively tuned and tailored to provide high SERS enhancement, while arising from different HEHD morphologies. The HEHD fabrication of sub-micrometer architectures is straightforward and robust, providing an elegant route for high-throughput biological and chemical sensing. The superior detection properties and the ability to fabricate SERS substrates on the miniaturized scale, will facilitate the development of advanced and novel opto-fluidic devices, such as portable detection systems, and will offer numerous applications in biomedical diagnostics, forensics, ecological warfare and homeland security.

Keywords: hierarchical electrohydrodynamic patterning, medical diagnostics, point-of care devices, SERS

Procedia PDF Downloads 345

Authors: Kedar Baral

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This study was carried out in Mustang, Kaski, Tanahun, Baitadi, and Jhapa districts of Nepal. The study explored the spatial and temporal pattern of HWC, socio economic factors associated with it, impacts of conflict on life / livelihood of people and survival of wildlife species, and impact of climate change and forest fire onHWC. Study also evaluated people’s attitude towards wildlife conservation and assessed relevant policies and programs. Questionnaire survey was carried out with the 250 respondents, and both socio-demographic and HWC related information werecollected. Secondary information were collected from Divisional Forest Offices and Annapurna Conservation Area Project.HWC events were grouped by season /months/sites (forest type, distances from forest, and settlement), and the coordinates of the events were exported to ArcGIS. Collected data were analyzed using descriptive statistics in Excel and R Program. A total of 1465 events were recorded in 5 districts during 2015 and 2019. Out of that, livestock killing, crop damage, human attack, and cattle shed damage events were 70 %, 12%, 11%, and 7%, respectively. Among 151 human attack cases, 23 people were killed, and 128 were injured. Elephant in Terai, common leopard and monkey in Middle Mountain, and snow leopard in high mountains were found as major problematic animals. Common leopard attacks were found more in the autumn, evening, and on human settlement area. Whereas elephant attacks were found higher in winter, day time, and on farmland. Poor people farmers were found highly victimized, and they were losing 26% of their income due to crop raiding and livestock depredation. On the other hand, people are killing many wildlife in revenge, and this number is increasing every year. Based on the people's perception, climate change is causing increased temperature and forest fire events and decreased water sources within the forest. Due to the scarcity of food and water within forests, wildlife are compelled to dwell at human settlement area, hence HWC events are increasing. Nevertheless, more than half of the respondents were found positive about conserving entire wildlife species. Forests outside PAs are under the community forestry (CF) system, which restored the forest, improved the habitat, and increased the wildlife.However, CF policies and programs were found to be more focused on forest management with least priority on wildlife conservation and HWC mitigation. Compensation / relief scheme of government for wildlife damage was found some how effective to manage HWC, but the lengthy process, being applicable to the damage of few wildlife species and highly increasing events made it necessary to revisit. Based on these facts, the study suggest to carry out awareness generation activities to the poor farmers, linking the property of people with the insurance scheme, conducting habitat management activities within CF, promoting the unpalatable crops, improvement of shed house of livestock, simplifying compensation scheme and establishing a fund at the district level and incorporating the wildlife conservation and HWCmitigation programs in CF. Finally, the study suggests to carry out rigorous researches to understand the impacts of current forest management practices on forest, biodiversity, wildlife, and HWC.

Keywords: community forest, conflict mitigation, wildlife conservation, climate change

Procedia PDF Downloads 117

Authors: Akram Khelaifia, Salah Guettala, Nesreddine Djafar Henni, Rachid Chebili

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Reinforced concrete shear walls are pivotal in protecting buildings from seismic forces by providing strength and stiffness. This study highlights the importance of strategically placing shear walls and optimizing the shear wall-to-floor area ratio in building design. Nonlinear analyses were conducted on an eight-story building situated in a high seismic zone, exploring various scenarios of shear wall positioning and ratios to floor area. Employing the performance-based seismic design (PBSD) approach, the study aims to meet acceptance criteria such as inter-story drift ratio and damage levels. The results indicate that concentrating shear walls in the middle of the structure during the design phase yields superior performance compared to peripheral distributions. Utilizing shear walls that fully infill the frame and adopting compound shapes (e.g., Box, U, and L) enhances reliability in terms of inter-story drift. Conversely, the absence of complete shear walls within the frame leads to decreased stiffness and degradation of shorter beams. Increasing the shear wall-to-floor area ratio in building design enhances structural rigidity and reliability regarding inter-story drift, facilitating the attainment of desired performance levels. The study suggests that a shear wall ratio of 1.0% is necessary to meet validation criteria for inter-story drift and structural damage, as exceeding this percentage leads to excessive performance levels, proving uneconomical as structural elements operate near the elastic range.

Keywords: nonlinear analyses, pushover analysis, shear wall, plastic hinge, performance level

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Authors: Derin Ureten

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Plastic pollution does not disappear, it gets smaller and smaller through photolysis which are caused mainly by sun’s radiation, thermal oxidation, thermal degradation, and biodegradation which is the action of organisms digesting larger plastics. All plastic pollutants have exceedingly harmful effects on the environment. Together with the COVID-19 pandemic, the number of plastic products such as masks and gloves flowing into the environment has increased more than ever. However, microplastics are not the only pollutants in water, one of the most tenacious and toxic pollutants are heavy metals. Heavy metal solutions are also capable of causing varieties of health problems in organisms such as cancer, organ damage, nervous system damage, and even death. The aim of this research is to prove that microplastics can be used in wastewater treatment systems by proving that they could adsorb heavy metals in solutions. Experiment for this research will include two heavy metal solutions; one including microplastics in a heavy metal contaminated water solution, and one that just includes heavy metal solution. After being sieved, absorbance of both mediums will be measured with the help of a spectrometer. Iron (III) chloride (FeCl3) will be used as the heavy metal solution since the solution becomes darker as the presence of this substance increases. The experiment will be supported by Pure Nile Red powder in order to observe if there are any visible differences under the microscope. Pure Nile Red powder is a chemical that binds to hydrophobic materials such as plastics and lipids. If proof of adsorbance could be observed by the rates of the solutions' final absorbance rates and visuals ensured by the Pure Nile Red powder, the experiment will be conducted with different temperature levels in order to analyze the most accurate temperature level to proceed with removal of heavy metals from water. New wastewater treatment systems could be generated with the help of microplastics, for water contaminated with heavy metals.

Keywords: microplastics, heavy metal, pollution, adsorbance, wastewater treatment

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Authors: A. I. Khalafalla, K. A. Al-Busada, I. M. El-Sabagh

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Pox and pox-like diseases of camels are a group of exanthematous skin conditions that have become increasingly important economically. They may be caused by three distinct viruses: camelpox virus (CMPV), camel contagious ecthyma virus (CCEV) and camel papillomavirus (CAPV). These diseases are difficult to differentiate based on clinical presentation in disease outbreaks. Molecular methods such as PCR targeting species-specific genes have been developed and used to identify CMPV and CCEV, but not simultaneously in a single tube. Recently, multiplex PCR has gained reputation as a convenient diagnostic method with cost- and time–saving benefits. In the present communication, we describe the development, optimization and validation a multiplex PCR assays able to detect simultaneously the genome of the three viruses in one single test allowing for rapid and efficient molecular diagnosis. The assay was developed based on the evaluation and combination of published and new primer sets, and was applied to the detection of 110 tissue samples. The method showed high sensitivity, and the specificity was confirmed by PCR-product sequencing. In conclusion, this rapid, sensitive and specific assay is considered a useful method for identifying three important viruses in specimens from camels and as part of a molecular diagnostic regime.

Keywords: multiplex PCR, diagnosis, pox and pox-like diseases, camels

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Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

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Authors: Abdulsalam M. Alhawsawi

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Estimation of a radiation detector's efficiency plays a significant role in calculating the activity of radioactive samples. Detector efficiency is measured using sources that emit a variety of energies from low to high-energy photons along the energy spectrum. Some photon energies are hard to find in lab settings either because check sources are hard to obtain or the sources have short half-lives. This work aims to develop a method to determine the efficiency of a High Purity Germanium Detector (HPGe) based on the 662 keV gamma ray photon emitted from Cs-137. Cesium-137 is readily available in most labs with radiation detection and health physics applications and has a long half-life of ~30 years. Several photon efficiencies were calculated using the MCNP5 simulation code. The simulated efficiency of the 662 keV photon was used as a base to calculate other photon efficiencies in a point source and a Marinelli Beaker form. In the Marinelli Beaker filled with water case, the efficiency of the 59 keV low energy photons from Am-241 was estimated with a 9% error compared to the MCNP5 simulated efficiency. The 1.17 and 1.33 MeV high energy photons emitted by Co-60 had errors of 4% and 5%, respectively. The estimated errors are considered acceptable in calculating the activity of unknown samples as they fall within the 95% confidence level.

Keywords: MCNP5, MonteCarlo simulations, efficiency calculation, absolute efficiency, activity estimation, Cs-137

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Authors: J. DeBoard, R. Dietrich, J. Hughes, K. Yurko, G. Harms

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Alzheimer’s disease (AD) is a predominant type of dementia and is likely a major cause of neural network impairment. The pathogenesis of this neurodegenerative disorder has yet to be fully elucidated. There are currently no known cures for the disease, and the best hope is to be able to detect it early enough to impede its progress. Beyond age and genetics, another prevalent risk factor for AD might be traumatic brain injury (TBI), which has similar neurodegenerative hallmarks. Our research focuses on obtaining information and methods to be able to predict when neurodegenerative effects might occur at a clinical level by observation of events at a cellular and molecular level in model mice. First, we wish to introduce our evidence that brain damage can be observed via brain imaging prior to the noticeable loss of neuromuscular control in model mice of AD. We then show our evidence that some blood biomarkers might be able to be early predictors of AD in the same model mice. Thus, we were interested to see if we might be able to predict which mice might show long-term neurodegenerative effects due to differing degrees of TBI and what level of TBI causes further damage and earlier death to the AD model mice. Upon application of TBIs via an apparatus to effectively induce extremely mild to mild TBIs, wild-type (WT) mice and AD mouse models were tested for cognition, neuromuscular control, olfactory ability, blood biomarkers, and brain imaging. Experiments are currently still in process, and more results are therefore forthcoming. Preliminary data suggest that neuromotor control diminishes as well as olfactory function for both AD and WT mice after the administration of five consecutive mild TBIs. Also, seizure activity increases significantly for both AD and WT after the administration of the five TBI treatment. If future data supports these findings, important implications about the effect of TBI on those at risk for AD might be possible.

Keywords: Alzheimer's disease, blood biomarker, neurodegeneration, neuromuscular control, olfaction, traumatic brain injury

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

Procedia PDF Downloads 134

Authors: Ghodbane Azeddine, Saad Maarouf, Boland Jean-Francois, Thibeault Claude

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This paper proposes a new design of an active fault-tolerant flight control system against abrupt actuator faults. This overall system combines a multiple time scale sliding mode controller for fault compensation and a geometric approach for fault detection and diagnosis. The proposed control system is able to accommodate several kinds of partial and total actuator failures, by using available healthy redundancy actuators. The overall system first estimates the correct fault information using the geometric approach. Then, and based on that, a new reconfigurable control law is designed based on the multiple time scale sliding mode technique for on-line compensating the effect of such faults. This approach takes advantages of the fact that there are significant difference between the time scales of aircraft states that have a slow dynamics and those that have a fast dynamics. The closed-loop stability of the overall system is proved using Lyapunov technique. A case study of the non-linear model of the F16 fighter, subject to the rudder total loss of control confirms the effectiveness of the proposed approach.

Keywords: actuator faults, fault detection and diagnosis, fault tolerant flight control, sliding mode control, multiple time scale approximation, geometric approach for fault reconstruction, lyapunov stability

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Authors: Khalid Alhudaib

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Potato is considered as one of the most important and potential vegetable crops in Saudi Arabia. Alfalfa mosaic virus (AMV), genus Alfamovirus, family Bromoviridae is among the broad spread of viruses in potato. During spring and fall growing seasons of potato in 2015 and 2016, several field visits were conducted in the four major growing areas of potato cultivation (Riyadh-Qaseem-Hail-Hard). The presence of AMV was detected in samples using ELISA, dot blot hybridization and/or RT-PCR. The highest occurrence of AMV was observed as 18.6% in Qaseem followed by Riyadh with 15.2% while; the lowest infection rates were recorded in Hard and Hail, 8.3 and 10.4%, respectively. The sequences of seven isolates of AMV obtained in this study were determined and the sequences were aligned with the other sequences available in the GenBank database. Analyses confirmed the low variability among AMV isolated in this study, which means that all AMV isolates may originate from the same source. Due to high incidence of AMV, other economic susceptible crops may become affected by high incidence of this virus in potato crops. This requires accurate examination of potato seed tubers to prevent the spread of the virus in Saudi Arabia. The obtained results indicated that the hybridization and ELISA are suitable techniques in the routine detection of AMV in a large number of samples while RT-PCR is more sensitive and essential for molecular characterization of AMV.

Keywords: Alfamovirus, AMV, Alfalfa mosaic virus, PCR, potato

Procedia PDF Downloads 177

Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

Procedia PDF Downloads 227

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

Procedia PDF Downloads 64

Authors: Juliane Spaak

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A life-cycle assessment was performed, looking at seven retrofit projects in New Zealand using LCAQuickV3.5. The study found that retrofits save up to 80% of embodied carbon emissions for the structural elements compared to a new building. In other words, it is only a 20% carbon investment to transform and extend a building’s life. In addition, the systems were evaluated by looking at environmental impacts over the design life of these buildings and resilience using FEMA P58 and PACT software. With the increasing interest in Zero Carbon targets, significant changes in the building and construction sector are required. Emissions for buildings arise from both embodied carbon and operations. Based on the significant advancements in building energy technology, the focus is moving more toward embodied carbon, a large portion of which is associated with the structure. Since older buildings make up most of the real estate stock of our cities around the world, their reuse through structural retrofit and wider refurbishment plays an important role in extending the life of a building’s embodied carbon. New Zealand’s building owners and engineers have learned a lot about seismic issues following a decade of significant earthquakes. Recent earthquakes have brought to light the necessity to move away from constructing code-minimum structures that are designed for life safety but are frequently ‘disposable’ after a moderate earthquake event, especially in relation to a structure’s ability to minimize damage. This means weaker buildings sit as ‘carbon liabilities’, with considerably more carbon likely to be expended remediating damage after a shake. Renovating and retrofitting older assets plays a big part in reducing the carbon profile of the buildings sector, as breathing new life into a building’s structure is vastly more sustainable than the highest quality ‘green’ new builds, which are inherently more carbon-intensive. The demolition of viable older buildings (often including heritage buildings) is increasingly at odds with society’s desire for a lower carbon economy. Bringing seismic resilience and carbon best practice together in decision-making can open the door to commercially attractive outcomes, with retrofits that include structural and sustainability upgrades transforming the asset’s revenue generation. Across the global real estate market, tenants are increasingly demanding the buildings they occupy be resilient and aligned with their own climate targets. The relationship between seismic performance and ‘sustainable design’ has yet to fully mature, yet in a wider context is of profound consequence. A whole-of-life carbon perspective on a building means designing for the likely natural hazards within the asset’s expected lifespan, be that earthquake, storms, damage, bushfires, fires, and so on, ¬with financial mitigation (e.g., insurance) part, but not all, of the picture.

Keywords: retrofit, sustainability, earthquake, reuse, carbon, resilient

Procedia PDF Downloads 73

Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

Procedia PDF Downloads 235

Authors: Thilini M. Yatanwala

Abstract:

CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

Procedia PDF Downloads 183

Authors: K. Sekulová, M. Šimon

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This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated.

Keywords: ergonomics, musculoskeletal disorders, occupational diseases, risk factors

Procedia PDF Downloads 550

Authors: Peter Modregger, Özgül Öztürk

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Glass fabric reinforced thermoplastic (GFRT) are composite materials, which combine low weight and resilient mechanical properties rendering them especially suitable for automobile construction. However, defects in the glass fabric as well as in the polymer matrix can occur during manufacturing, which may compromise component lifetime or even safety. One type of these defects is local fiber shifts, which can be difficult to detect. Recently, we have experimentally demonstrated the reliable detection of local fiber shifts by X-ray scattering based on the edge-illumination (EI) principle. EI constitutes a novel X-ray imaging technique that utilizes two slit masks, one in front of the sample and one in front of the detector, in order to simultaneously provide absorption, phase, and scattering contrast. The principle of contrast formation is as follows. The incident X-ray beam is split into smaller beamlets by the sample mask, resulting in small beamlets. These are distorted by the interaction with the sample, and the distortions are scaled up by the detector masks, rendering them visible to a pixelated detector. In the experiment, the sample mask is laterally scanned, resulting in Gaussian-like intensity distributions in each pixel. The area under the curves represents absorption, the peak offset refraction, and the width of the curve represents the scattering occurring in the sample. Here, scattering is caused by the numerous glass fiber/polymer matrix interfaces. In our recent publication, we have shown that the standard deviation of the absorption and scattering values over a selected field of view can be used to distinguish between intact samples and samples with local fiber shift defects. The quantification of defect detection performance was done by using p-values (p=0.002 for absorption and p=0.009 for scattering) and contrast-to-noise ratios (CNR=3.0 for absorption and CNR=2.1 for scattering) between the two groups of samples. This was further improved for the scattering contrast to p=0.0004 and CNR=4.2 by utilizing a harmonic decomposition analysis of the images. Thus, we concluded that local fiber shifts can be reliably detected by the X-ray scattering contrasts provided by EI. However, a potential application in, for example, production monitoring requires fast data acquisition times. For the results above, the scanning of the sample masks was performed over 50 individual steps, which resulted in long total scan times. In this paper, we will demonstrate that reliable detection of local fiber shift defects is also possible by using single images, which implies a speed up of total scan time by a factor of 50. Additional performance improvements will also be discussed, which opens the possibility for real-time acquisition. This contributes a vital step for the translation of EI to industrial applications for a wide variety of materials consisting of numerous interfaces on the micrometer scale.

Keywords: defects in composites, X-ray scattering, local fiber shifts, X-ray edge Illumination

Procedia PDF Downloads 63