Search results for: accuracy estimate

Authors: Malay K. Pramanik

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In recent years, climate change has become a major threat and has been widely documented in the geographic distribution of many plant species. However, the impacts of climate change on the distribution of ecologically vulnerable medicinal species remain largely unknown. The identification of a suitable habitat for a species under climate change scenario is a significant step towards the mitigation of biodiversity decline. The study, therefore, aims to predict the impact of current, and future climatic scenarios on the distribution of the threatened Garcinia indica across the northern Western Ghats using Maximum Entropy (MaxEnt) modelling. The future projections were made for the year 2050 and 2070 with all Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6.0, and 8.5) using 56 species occurrence data, and 19 bioclimatic predictors from the BCC-CSM1.1 model of the Intergovernmental Panel for Climate Change’s (IPCC) 5th assessment. The bioclimatic variables were minimised to a smaller number of variables after a multicollinearity test, and their contributions were assessed using jackknife test. The AUC value of 0.956 ± 0.023 indicates that the model performs with excellent accuracy. The study identified that temperature seasonality (39.5 ± 3.1%), isothermality (19.2 ± 1.6%), and annual precipitation (12.7 ± 1.7%) would be the major influencing variables in the current and future distribution. The model predicted 10.5% (19318.7 sq. km) of the study area as moderately to very highly suitable, while 82.60% (151904 sq. km) of the study area was identified as ‘unsuitable’ or ‘very low suitable’. Our predictions of climate change impact on habitat suitability suggest that there will be a drastic reduction in the suitability by 5.29% and 5.69% under RCP 8.5 for 2050 and 2070, respectively. Finally, the results signify that the model might be an effective tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios.

Keywords: Garcinia Indica, maximum entropy modelling, climate change, MaxEnt, Western Ghats, medicinal plants

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

Procedia PDF Downloads 182

Authors: Man Wai Lei, Charles Mark Zaroff

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Objective: A majority of studies suggest that people with Autism Spectrum Disorder (ASD) have multisensory integration deficits. However, normal and even supranormal multisensory integration abilities have also been reported. Additionally, little of this work has been undertaken utilizing a dimensional conceptualization of ASD; i.e., a broader autism phenotype. Utilizing methodology that controls for common potential confounds, the current study aimed to examine if deficits in multisensory integration are associated with ASD traits in a non-clinical population. The contribution of affective versus non-affective components of sensory hypersensitivity to multisensory integration was also examined. Methods: Participants were 147 undergraduate university students in Macau, a Special Administrative Region of China, of Chinese ethnicity, aged 16 to 21 (Mean age = 19.13; SD = 1.07). Participants completed the Autism-Spectrum Quotient, the Sensory Perception Quotient, and the Adolescent/Adult Sensory Profile, in order to measure ASD traits, non-affective, and affective aspects of sensory/perceptual hypersensitivity, respectively. In order to explore multisensory integration across visual and haptic domains, participants were asked to judge which one of two equally weighted, but different sized cylinders was heavier, as a means of detecting the presence of the size-weight illusion (SWI). Results: ASD trait level was significantly and negatively correlated with susceptibility to the SWI (p < 0.05); this correlation was not associated with either accuracy in weight discrimination or gender. Examining the top decile of the non-normally distributed SWI scores revealed a significant negative association with sensation avoiding, but not other aspects of effective or non-effective sensory hypersensitivity. Conclusion and Implications: Within the normal population, a greater degree of ASD traits is associated with a lower likelihood of multisensory integration; echoing was often found in individuals with a clinical diagnosis of ASD, and providing further evidence for the dimensional nature of this disorder. This tendency appears to be associated with dysphoric emotional reactions to sensory input.

Keywords: Autism Spectrum Disorder, dimensional, multisensory integration, size-weight illusion

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Authors: Katarzyna Socha, Konrad Mielcarek, Grzegorz Kangowski, Renata Markiewicz-Zukowska, Anna Puscion-Jakubik, Jolanta Soroczynska, Maria H. Borawska

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Nuts have high nutritional value. They are a good source of polyunsaturated fatty acids, dietary fiber, vitamins (B₁, B₆, E, K) and minerals: magnesium, selenium, zinc (Zn). Zn is an essential element for proper functioning and development of human organism. Due to antioxidant and anti-inflammatory properties, Zn has an influence on immunological and central nervous system. It also affects proper functioning of reproductive organs and has beneficial impact on the condition of skin, hair, and nails. The objective of this study was estimation of Zn content in edible nuts. The research material consisted of 10 types of nuts, 12 samples of each type: almonds, brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios, and walnuts. The samples of nuts were digested in concentrated nitric acid using microwave mineralizer (Berghof, Germany). The concentration of Zn was determined by flame atomic absorption spectrometry method with Zeeman background correction (Hitachi, Japan). The accuracy of the method was verified on certified reference material: Simulated Diet D. The statistical analysis was performed using Statistica v. 13.0 software. For comparison between the groups, t-Student test was used. The highest content of Zn was shown in pine nuts and cashews: 78.57 ± 21.9, 70.02 ± 10,2 mg/kg, respectively, significantly higher than in other types of nuts. The lowest content of Zn was found in macadamia nuts: 16.25 ± 4.1 mg/kg. The consumption of a standard 42-gram portion of almonds, brazil nuts, cashews, peanuts, pecans, and pine nuts covers the daily requirement for Zn above 15% of recommended daily allowances (RDA) for women, while in the case of men consumption all of the above types of nuts, except peanuts. Selected types of nuts can be a good source of Zn in the diet.

Keywords: atomic absorption spectrometry, microelement, nuts, zinc

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Xiaoyang Zhao, Kaiying Wang

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The choice of stocking density in poultry farming is a potential way for determining welfare level of poultry. However, it is difficult to measure stocking densities in poultry farming because of a lot of variables such as species, age and weight, feeding way, house structure and geographical location in different broiler houses. A method was proposed in this paper to measure the differences of young broilers reared under different stocking densities by sound analysis. Vocalisations of broilers were recorded and analysed under different stocking densities to identify the relationship between sounds and stocking densities. Recordings were made continuously for three-week-old chickens in order to evaluate the variation of sounds emitted by the animals at the beginning. The experimental trial was carried out in an indoor reared broiler farm; the audio recording procedures lasted for 5 days. Broilers were divided into 5 groups, stocking density treatments were 8/m², 10/m², 12/m² (96birds/pen), 14/m² and 16/m², all conditions including ventilation and feed conditions were kept same except from stocking densities in every group. The recordings and analysis of sounds of chickens were made noninvasively. Sound recordings were manually analysed and labelled using sound analysis software: GoldWave Digital Audio Editor. After sound acquisition process, the Mel Frequency Cepstrum Coefficients (MFCC) was extracted from sound data, and the Support Vector Machine (SVM) was used as an early detector and classifier. This preliminary study, conducted in an indoor reared broiler farm shows that this method can be used to classify sounds of chickens under different densities economically (only a cheap microphone and recorder can be used), the classification accuracy is 85.7%. This method can predict the optimum stocking density of broilers with the complement of animal welfare indicators, animal productive indicators and so on.

Keywords: broiler, stocking density, poultry farming, sound monitoring, Mel Frequency Cepstrum Coefficients (MFCC), Support Vector Machine (SVM)

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Authors: Dipti Chand, Riya Saboo

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OBJECTIVES: Early and correct diagnosis may present a significant clinical challenge in diagnosis of patients presenting to Emergency Department with Acute Dyspnoea. The common cause of acute dyspnoea and respiratory distress in Emergency Department are Decompensated Heart Failure (HF), Chronic Obstructive Pulmonary Disease (COPD), Asthma, Pneumonia, Acute Respiratory Distress Syndrome (ARDS), Pulmonary Embolism (PE), and other causes like anaemia. The aim of the study was to measure NT-pro Brain Natriuretic Peptide (BNP) and exhaled End-Tidal Carbon dioxide (ETCO2) in patients presenting with dyspnoea. MATERIAL AND METHODS: This prospective, cross-sectional and observational study was performed at the Government Medical College and Hospital, Nagpur, between October 2019 and October 2021 in patients admitted to the Medicine Intensive Care Unit. Three groups of patients were compared: (1) HFrelated acute dyspnoea group (n = 52), (2) pulmonary (COPD/PE)-related acute dyspnoea group (n = 31) and (3) sepsis with ARDS-related dyspnoea group (n = 13). All patients underwent initial clinical examination with a recording of initial vital parameters along with on-admission ETCO2 measurement, NT-proBNP testing, arterial blood gas analysis, lung ultrasound examination, 2D echocardiography, chest X-rays, and other relevant diagnostic laboratory testing. RESULTS: 96 patients were included in the study. Median NT-proBNP was found to be high for the Heart Failure group (11,480 pg/ml), followed by the sepsis group (780 pg/ml), and pulmonary group had an Nt ProBNP of 231 pg/ml. The mean ETCO2 value was maximum in the pulmonary group (48.610 mmHg) followed by Heart Failure (31.51 mmHg) and the sepsis group (19.46 mmHg). The results were found to be statistically significant (P < 0.05). CONCLUSION: NT-proBNP has high diagnostic accuracy in differentiating acute HF-related dyspnoea from pulmonary (COPD and ARDS)-related acute dyspnoea. The higher levels of ETCO2 help in diagnosing patients with COPD.

Keywords: NT PRO BNP, ETCO2, dyspnoea, lung USG

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Authors: Di Yao, Gunther Prokop, Kay Buttner

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Dynamic parameters, including the center of gravity, mass and inertia moments of vehicle, play an essential role in vehicle simulation, collision test and real-time control of vehicle active systems. To identify the important vehicle dynamic parameters, a systematic parameter identification procedure is studied in this work. In the first step of the procedure, a conceptual parallel manipulator (virtual test rig), which possesses three rotational degrees-of-freedom, is firstly proposed. To realize kinematic characteristics of the conceptual parallel manipulator, the kinematic analysis consists of inverse kinematic and singularity architecture is carried out. Based on the Euler's rotation equations for rigid body dynamics, the dynamic model of parallel manipulator and derivation of measurement matrix for parameter identification are presented subsequently. In order to reduce the sensitivity of parameter identification to measurement noise and other unexpected disturbances, a parameter optimization process of searching for optimal exciting trajectory of parallel manipulator is conducted in the following section. For this purpose, the 321-Euler-angles defined by parameterized finite-Fourier-series are primarily used to describe the general exciting trajectory of parallel manipulator. To minimize the condition number of measurement matrix for achieving better parameter identification accuracy, the unknown coefficients of parameterized finite-Fourier-series are estimated by employing an iterative algorithm based on MATLAB®. Meanwhile, the iterative algorithm will ensure the parallel manipulator still keeps in an achievable working status during the execution of optimal exciting trajectory. It is showed that the proposed procedure and methods in this work can effectively identify the vehicle dynamic parameters and could be an important application of parallel manipulator in the fields of parameter identification and test rig development.

Keywords: parameter identification, parallel manipulator, singularity architecture, dynamic modelling, exciting trajectory

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Authors: Kanchan Negi

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Background: Modern and urban occupational culture have driven demands for people to work long hours and weekends and take work to home at times. Research on the health effects of these exhaustive temporal work patterns is scant or contradictory. This study examines the relationship between work patterns and wellbeing in a sample of women living in the metropolitan hub of Delhi. Method: This study is based on the data collected from 360 currently married women between age 29 and 49 years, working in the urban capital hub of India, i.e., Delhi. The women interviewed were professionals from the education, health, banking and information and technology (IT) sector. Bivariate analysis was done to study the characteristics of the sample. Logistic regression analysis was used to estimate the physical and psychological wellbeing across occupational characteristics. Results: Most of the working women were below age 35 years; around 30% of women worked in the education sector, 23% in health, 21% in banking and 26% in the IT sector. Over 55% of women were employed in the private sector and only 36% were permanent employees. Nearly 30% of women worked for more than the standard 8 hours a day. The findings from logistic regression showed that compared to women working in the education sector, those who worked in the banking and IT sector more likely to have physical and psychological health issues (OR 2.07-4.37, CI 1.17-4.37); women who bear dual burden of responsibilities had higher odds of physical and psychological health issues than women who did not (OR 1.19-1.85 CI 0.96-2.92). Women who worked for more than 8 hours a day (OR 1.15, CI 1.01-1.30) and those who worked for more than five days a week (OR 1.25, CI 1.05-1.35) were more likely to have physical health issues than women who worked for 6-8 hours a day and five days e week, respectively. Also, not having flexible work timings and compensatory holidays increased the odds of having physical and psychological health issues among working women (OR 1.17-1.29, CI 1.01-1.47). Women who worked in the private sector, those employed temporarily and who worked in the non-conducive environments were more likely to have psychological health issues as compared to women in the public sector, permanent employees and those who worked in a conducive environment, respectively (OR 1.33-1.67, CI 1.09-2.91). Women who did not have poor work-life balance had reduced the odds of psychological health issues than women with poor work-life balance (OR 0.46, CI 0.25-0.84). Conclusion: Poor wellbeing significantly linked to strenuous and rigid work patterns, suggesting that modern and urban work culture may contribute to the poor wellbeing of working women. Noticing the recent decline in female workforce participation in Delhi, schemes like Flexi-timings, compensatory holidays, work-from-home and daycare facilities for young ones must be welcomed; these policies already exist in some private sector firms, and the public sectors companies should also adopt such changes to ease the dual burden as homemaker and career maker. This could encourage women in the urban areas to readily take up the jobs with less juggle to manage home and work.

Keywords: occupational characteristics, urban India, well-being, working women

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Authors: April Hueimin Lu, Liangj-Ju Yao, Jun-Tin Lin, Susan Siru Liu

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This paper discusses the application of aerial survey technology and 3D laser scanning technology in the surveying and mapping work of the settlements and slate houses of the old Taiwanese aborigines. The relics of old Taiwanese aborigines with thousands of history are widely distributed in the deep mountains of Taiwan, with a vast area and inconvenient transportation. When constructing the basic data of cultural assets, it is necessary to apply new technology to carry out efficient and accurate settlement mapping work. In this paper, taking the old Paiwan as an example, the aerial survey of the settlement of about 5 hectares and the 3D laser scanning of a slate house were carried out. The obtained orthophoto image was used as an important basis for drawing the settlement map. This 3D landscape data of topography and buildings derived from the aerial survey is important for subsequent preservation planning as well as building 3D scan provides a more detailed record of architectural forms and materials. The 3D settlement data from the aerial survey can be further applied to the 3D virtual model and animation of the settlement for virtual presentation. The information from the 3D scanning of the slate house can also be used for further digital archives and data queries through network resources. The results of this study show that, in large-scale settlement surveys, aerial surveying technology is used to construct the topography of settlements with buildings and spatial information of landscape, as well as the application of 3D scanning for small-scale records of individual buildings. This application of 3D technology, greatly increasing the efficiency and accuracy of survey and mapping work of aboriginal settlements, is much helpful for further preservation planning and rejuvenation of aboriginal cultural heritage.

Keywords: aerial survey, 3D scanning, aboriginal settlement, settlement architecture cluster, ecological landscape area, old Paiwan settlements, slat house, photogrammetry, SfM, MVS), Point cloud, SIFT, DSM, 3D model

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Authors: Kadhim Alabady

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Background: Autism poses a particularly large public health challenge and an inspiring lifelong challenge for many families; it is a lifelong challenge of a different kind. Purpose: Therefore, it is important to understand what the key challenges are and how to improve the lives of children who are affected with autism in Dubai. Method: In order to carry out this research we have used a qualitative methodology. We performed structured in–depth interviews and focus groups with mental health professionals working at: Al Jalila hospital (AJH), Dubai Autism Centre (DAC), Dubai Rehabilitation Centre for Disabilities, Latifa hospital, Private Sector Healthcare (PSH). In addition to that, we conducted quantitative approach to estimate ASD prevalence or incidence data due to lack of registry. ASD estimates are based on research from national and international documents. This approach was applied to increase the validity of the findings by using a variety of data collection techniques in order to explore issues that might not be highlighted through one method alone. Key findings: Autism is the most common of the Pervasive Developmental Disorders. Dubai Autism Center estimates it affects 1 in 146 births (0.68%). If we apply these estimates to the total number of births in Dubai for 2014, it is predicted there would be approximately 199 children (of which 58 were Nationals and 141 were Non–Nationals) suffering from autism at some stage. 16.4% of children (through their families) seek help for ASD assessment between the age group 6–18+. It is critical to understand and address factors for seeking late–stage diagnosis, as ASD can be diagnosed much earlier and how many of these later presenters are actually diagnosed with ASD. Autism spectrum disorder (ASD) is a public health concern in Dubai. Families do not consult GPs for early diagnosis for a variety of reasons including cultural reasons. Recommendations: Effective school health strategies is needed and implemented by nurses who are qualified and experienced in identifying children with ASD. There is a need for the DAC to identify and develop a closer link with neurologists specializing in Autism, to work alongside and for referrals. Autism can be attributed to many factors, some of those are neurological. Currently, when families need their child to see a neurologist they have to go independently and search through the many that are available in Dubai and who are not necessarily specialists in Autism. Training of GP’s to aid early diagnosis of Autism and increase awareness. Since not all GP’s are trained to make such assessments increasing awareness about where to send families for a complete assessment and the necessary support. There is an urgent need for an adult autism center for when the children leave the safe environment of the school at 18 years. These individuals require a day center or suitable job training/placements where appropriate. There is a need for further studies to cover the needs of people with an Autism Spectrum Disorder (ASD).

Keywords: autism spectrum disorder, autism, pervasive developmental disorders, incidence

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Mohammad Hossain Mohammadi, Saif Al Ameri, Sana Ziaei, Jinane Mounsef

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Automated enumeration of the number of hand fingers is widely used in several motion gaming and distance control applications, and is discussed in several published papers as a starting block for hand recognition systems. The automated finger enumeration technique should not only be accurate, but also must have a fast response for a moving-picture input. The high performance of video in motion games or distance control will inhibit the program’s overall speed, for image processing software such as Matlab need to produce results at high computation speeds. Since an automated finger enumeration with minimum error and processing time is desired, a comparative study between two finger enumeration techniques is presented and analyzed in this paper. In the pre-processing stage, various image processing functions were applied on a real-time video input to obtain the final cleaned auto-cropped image of the hand to be used for the two techniques. The first technique uses the known morphological tool of skeletonization to count the number of skeleton’s endpoints for fingers. The second technique uses a radial distance method to enumerate the number of fingers in order to obtain a one dimensional hand representation. For both discussed methods, the different steps of the algorithms are explained. Then, a comparative study analyzes the accuracy and speed of both techniques. Through experimental testing in different background conditions, it was observed that the radial distance method was more accurate and responsive to a real-time video input compared to the skeletonization method. All test results were generated in Matlab and were based on displaying a human hand for three different orientations on top of a plain color background. Finally, the limitations surrounding the enumeration techniques are presented.

Keywords: comparative study, hand recognition, fingertip detection, skeletonization, radial distance, Matlab

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Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Kevin Potoczny, Katsuichiro Goda

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The St. Lawrence lowlands extend from Ottawa to Quebec City and are known for large deposits of sensitive Leda clay. Leda clay deposits are responsible for many large landslides, such as the 1993 Lemieux and 2010 St. Jude (4 fatalities) landslides. Due to the large extent and sensitivity of Leda clay, regional hazard analysis for landslides is an important tool in risk management. A 2018 regional study by Farzam et al. on the susceptibility of Leda clay slopes to landslide hazard uses 1 arc second topographical data. A qualitative method known as Hazus is used to estimate susceptibility by checking for various criteria in a location and determine a susceptibility rating on a scale of 0 (no susceptibility) to 10 (very high susceptibility). These criteria are slope angle, geological group, soil wetness, and distance from waterbodies. Given the flat nature of St. Lawrence lowlands, the current assessment fails to capture local slopes, such as the St. Jude site. Additionally, the data did not allow one to analyze failure planes accurately. This study majorly improves the analysis performed by Farzam et al. in two aspects. First, regional assessment with high resolution data allows for identification of local locations that may have been previously identified as low susceptibility. This then provides the opportunity to conduct a more refined analysis on the failure plane of the slope. Slopes derived from 1 arc second data are relatively gentle (0-10 degrees) across the region; however, the 1- and 2-meter resolution 2022 HRDEM provided by NRCAN shows that short, steep slopes are present. At a regional level, 1 arc second data can underestimate the susceptibility of short, steep slopes, which can be dangerous as Leda clay landslides behave retrogressively and travel upwards into flatter terrain. At the location of the St. Jude landslide, slope differences are significant. 1 arc second data shows a maximum slope of 12.80 degrees and a mean slope of 4.72 degrees, while the HRDEM data shows a maximum slope of 56.67 degrees and a mean slope of 10.72 degrees. This equates to a difference of three susceptibility levels when the soil is dry and one susceptibility level when wet. The use of GIS software is used to create a regional susceptibility map across the St. Lawrence lowlands at 1- and 2-meter resolutions. Failure planes are necessary to differentiate between small and large landslides, which have so far been ignored in regional analysis. Leda clay failures can only retrogress as far as their failure planes, so the regional analysis must be able to transition smoothly into a more robust local analysis. It is expected that slopes within the region, once previously assessed at low susceptibility scores, contain local areas of high susceptibility. The goal is to create opportunities for local failure plane analysis to be undertaken, which has not been possible before. Due to the low resolution of previous regional analyses, any slope near a waterbody could be considered hazardous. However, high-resolution regional analysis would allow for more precise determination of hazard sites.

Keywords: hazus, high-resolution DEM, leda clay, regional analysis, susceptibility

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Authors: Usha S. Adiga, Sachidanada Adiga

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Introduction: Diabetes Mellitus (DM) is one of the most common non-communicable diseases which imposes a large economic burden on the global health-care system. Cost of illness studies in India have assessed the health care cost of DM, but have certain limitations due to lack of standardization of the methods used, improper documentation of data, lack of follow up, etc. The objective of the study was to estimate the cost of illness of uncomplicated versus complicated type 2 diabetes mellitus in Coastal Karnataka, India. The study also aimed to find out the trend of cost of illness of the disease over a decade. Methodology: A prevalence based bottom-up approach study was carried out in two tertiary care hospitals located in Coastal Karnataka after ethical approval. Direct Medical costs like annual laboratory costs, pharmacy cost, consultation charges, hospital bed charges, surgical /intervention costs of 238 diabetics and 340 diabetic patients respectively from two hospitals were obtained from the medical record sections. Patients were divided into six groups, uncomplicated diabetes, diabetic retinopathy(DR), nephropathy(DN), neuropathy(DNeu), diabetic foot(DF), and ischemic heart disease (IHD). Different costs incurred in 2008 and 2017 in these groups were compared, to study the trend of cost of illness. Kruskal Wallis test followed by Dunn’s test were used to compare median costs between the groups and Spearman's correlation test was used for correlation studies. Results: Uncomplicated patients had significantly lower costs (p <0.0001) compared to other groups. Patients with IHD had highest Medical expenses (p < 0.0001), followed by DN and DF (p < 0.0001 ). Annual medical costs incurred were 1.8, 2.76, 2.77, 1.76, and 4.34 times higher in retinopathy, nephropathy, diabetic foot, neuropathy and IHD patients as compared to the cost incurred in managing uncomplicated diabetics. Other costs also showed a similar pattern of rising. A positive correlation was observed between the costs incurred and duration of diabetes, a negative correlation between the glycemic status and cost incurred. The cost incurred in the management of DM in 2017 was found to be elevated 1.4 - 2.7 times when compared to that in 2008. Conclusion: It is evident from the study that the economic burden due to diabetes mellitus is substantial. It poses a significant financial burden on the healthcare system, individual and society as a whole. There is a need for the strategies to achieve optimal glycemic control and operationalize regular and early screening methods for complications so as to reduce the burden of the disease.

Keywords: COI, diabetes mellitus, a bottom up approach, economics

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

Procedia PDF Downloads 248

Authors: Yusra Pintaningrum, Ilma Fahira Basyir, Sony Hilal Wicaksono, Vito A. Damay

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Background: Coronary artery calcium (CAC) scores play an important role in improving prognostic accuracy and can be selectively used to guide the allocation of statin therapy for atherosclerotic cardiovascular disease outcomes and potentially associated with the occurrence of MACE (Major Adverse Cardiovascular Event) and MI (Myocardial Infarction). Objective: This systematic review and meta-analysis aim to analyze the findings of a study about CAC Score and statin treatment effect on MI and MACE risk. Methods: Search for published scientific articles using the PRISMA (Preferred Reporting, Items for Systematic Reviews and Meta-Analysis) method conducted on PubMed, Cochrane Library, and Medline databases published in the last 20 years on “coronary artery calcium” AND “statin” AND “cardiovascular disease” Further systematic review and meta-analysis using RevMan version 5.4 were performed based on the included published scientific articles. Results: Based on 11 studies included with a total of 1055 participants, we performed a meta-analysis and found that individuals with CAC score > 0 increased risk ratio of MI 8.48 (RR = 9.48: 95% CI: 6.22 – 14.45) times and MACE 2.48 (RR = 3.48: 95% CI: 2.98 – 4.05) times higher than CAC score 0 individual. Statin compared against non-statin treatment showed a statistically insignificant overall effect on the risk of MI (P = 0.81) and MACE (P = 0.89) in an individual with elevated CAC score 1 – 100 (P = 0.65) and > 100 (P = 0.11). Conclusions: This study found that an elevated CAC scores individual has a higher risk of MI and MACE than a non-elevated CAC score individual. There is no significant effect of statin compared against non-statin treatment to reduce MI and MACE in elevated CAC score individuals of 1 – 100 or > 100.

Keywords: coronary artery calcium, statin, cardiovascular disease, myocardial infarction, MACE

Procedia PDF Downloads 88

Authors: Mariem Saied, Jens Gustedt, Gilles Muller

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We present Dido, a source-to-source auto-generation and optimization framework for multi-dimensional stencil computations. It enables a large programmer community to easily and safely implement stencil codes on distributed-memory parallel architectures with Ordered Read-Write Locks (ORWL) as an execution and communication back-end. ORWL provides inter-task synchronization for data-oriented parallel and distributed computations. It has been proven to guarantee equity, liveness, and efficiency for a wide range of applications, particularly for iterative computations. Dido consists mainly of an implicitly parallel domain-specific language (DSL) implemented as a source-level transformer. It captures domain semantics at a high level of abstraction and generates parallel stencil code that leverages all ORWL features. The generated code is well-structured and lends itself to different possible optimizations. In this paper, we enhance Dido to handle both Jacobi and Gauss-Seidel grid traversals. We integrate temporal blocking to the Dido code generator in order to reduce the communication overhead and minimize data transfers. To increase data locality and improve intra-node data reuse, we coupled the code generation technique with the polyhedral parallelizer Pluto. The accuracy and portability of the generated code are guaranteed thanks to a parametrized solution. The combination of ORWL features, the code generation pattern and the suggested optimizations, make of Dido a powerful code generation framework for stencil computations in general, and for distributed-memory architectures in particular. We present a wide range of experiments over a number of stencil benchmarks.

Keywords: stencil computations, ordered read-write locks, domain-specific language, polyhedral model, experiments

Procedia PDF Downloads 117

Authors: Mahmoud Huleihel, George Abu-Aqil, Manal Suleiman, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman

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Urinary tract infections (UTIs) are considered to be the most common bacterial infections worldwide, which are caused mainly by Escherichia (E.) coli (about 80%). Klebsiella pneumoniae (about 10%) and Pseudomonas aeruginosa (about 6%). Although antibiotics are considered as the most effective treatment for bacterial infectious diseases, unfortunately, most of the bacteria already have developed resistance to the majority of the commonly available antibiotics. Therefore, it is crucial to identify the infecting bacteria and to determine its susceptibility to antibiotics for prescribing effective treatment. Classical methods are time consuming, require ~48 hours for determining bacterial susceptibility. Thus, it is highly urgent to develop a new method that can significantly reduce the time required for determining both infecting bacterium at the species level and diagnose its susceptibility to antibiotics. Fourier-Transform Infrared (FTIR) spectroscopy is well known as a sensitive and rapid method, which can detect minor molecular changes in bacterial genome associated with the development of resistance to antibiotics. The main goal of this study is to examine the potential of FTIR spectroscopy, in tandem with machine learning algorithms, to identify the infected bacteria at the species level and to determine E. coli susceptibility to different antibiotics directly from patients' urine in about 30minutes. For this goal, 1600 different E. coli isolates were isolated for different patients' urine sample, measured by FTIR, and analyzed using different machine learning algorithm like Random Forest, XGBoost, and CNN. We achieved 98% success in isolate level identification and 89% accuracy in susceptibility determination.

Keywords: urinary tract infections (UTIs), E. coli, Klebsiella pneumonia, Pseudomonas aeruginosa, bacterial, susceptibility to antibiotics, infrared microscopy, machine learning

Procedia PDF Downloads 154

Authors: Hamidreza Hoshyarmanesh, Sanju Lama, Garnette R. Sutherland

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In tele-operative robotic surgery, an ideal haptic device should be equipped with an intuitive and smooth end-effector to cover the surgeon’s hand/wrist degrees of freedom (DOF) and translate the hand joint motions to the end-effector of the remote manipulator with low effort and high level of comfort. This research introduces the design and development of a microsurgery-specific end-effector, a gimbal mechanism possessing 4 passive and 1 active DOFs, equipped with a bipolar forceps and haptic feedback. The robust gimbal structure is comprised of three light-weight links/joint, pitch, yaw, and roll, each consisting of low-friction support and a 2-channel accurate optical position sensor. The third link, which provides the tool roll, was specifically designed to grip the tool prongs and accommodate a low mass geared actuator together with a miniaturized capstan-rope mechanism. The actuator is able to generate delicate torques, using a threaded cylindrical capstan, to emulate the sense of pinch/coagulation during conventional microsurgery. While the tool left prong is fixed to the rolling link, the right prong bears a miniaturized drum sector with a large diameter to expand the force scale and resolution. The drum transmits the actuator output torque to the right prong and generates haptic force feedback at the tool level. The tool is also equipped with a hall-effect sensor and magnet bar installed vis-à-vis on the inner side of the two prongs to measure the tooltip distance and provide an analogue signal to the control system. We believe that such a haptic end-effector could significantly increase the accuracy of telerobotic surgery and help avoid high forces that are known to cause bleeding/injury.

Keywords: end-effector, force generation, haptic interface, robotic surgery, surgical tool, tele-operation

Procedia PDF Downloads 109

Authors: Shay Kee Tan, Kok Haur Ng, Jennifer So-Kuen Chan

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This paper extends the conditional autoregressive range (CARR) model to multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain in-sample estimates and forecasts of volatilities for these individual and pairwise sum series. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions with the scaled realised open-to-close volatility measure as the proxy for the unobserved true volatility. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba, Engle, Kraft, and Kroner type of generalized autoregressive conditional heteroskedasticity (BEKK-GARCH) models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using Kupiec test to confirm the accuracy of the VaR forecasts.

Keywords: range-based volatility, correlation, multivariate CARR-return model, value-at-risk, conditional value-at-risk

Procedia PDF Downloads 89

Authors: S. Chowdhury, A. Corlett

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Large tsunamis strike the British Columbia coast every few hundred years. The Cascadia Subduction Zone, which extends along the Pacific coast from Vancouver Island to Northern California is one of the most seismically active regions in Canada. Significant earthquakes have occurred in this region, including the 1700 Cascade Earthquake with an estimated magnitude of 9.2. Based on geological records, experts have predicted a 'great earthquake' of a similar magnitude within this region may happen any time. This earthquake is expected to generate a large tsunami that could impact the coastal communities on Vancouver Island. Since many of these communities are in remote locations, they are more likely to be vulnerable, as the post-earthquake relief efforts would be impacted by the damage to critical road infrastructures. To assess the coastal vulnerability within these communities, a hydrodynamic model has been developed using MIKE-21 software. We have considered a 500 year probabilistic earthquake design criteria including the subsidence in this model. The bathymetry information was collected from Canadian Hydrographic Services (CHS), and National Oceanic Atmospheric and Administration (NOAA). The arial survey was conducted using a Cessna-172 aircraft for the communities, and then the information was converted to generate a topographic digital elevation map. Both survey information was incorporated into the model, and the domain size of the model was about 1000km x 1300km. This model was calibrated with the tsunami occurred off the west coast of Moresby Island on October 28, 2012. The water levels from the model were compared with two tide gauge stations close to the Vancouver Island and the output from the model indicates the satisfactory result. For this study, the design water level was considered as High Water Level plus the Sea Level Rise for 2100 year. The hourly wind speeds from eight directions were collected from different wind stations and used a 200-year return period wind speed in the model for storm events. The regional model was set for 12 hrs simulation period, which takes more than 16 hrs to complete one simulation using double Xeon-E7 CPU computer plus a K-80 GPU. The boundary information for the local model was generated from the regional model. The local model was developed using a high resolution mesh to estimate the coastal flooding for the communities. It was observed from this study that many communities will be effected by the Cascadia tsunami and the inundation maps were developed for the communities. The infrastructures inside the coastal inundation area were identified. Coastal vulnerability planning and resilient design solutions will be implemented to significantly reduce the risk.

Keywords: tsunami, coastal flooding, coastal vulnerable, earthquake, Vancouver, wave propagation

Procedia PDF Downloads 123

Authors: Sara Mosallanejad, Bogdan Z. Dlugogorski, Jeff Gore, Mohammednoor Altarawneh

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Ammonium nitrate (NH­₄NO₃, AN) is commonly used as the main component of AN emulsion and fuel oil (ANFO) explosives, that use extensively in civilian and mining operations for underground development and tunneling applications. The emulsion formulation and wettability of AN prills, which affect the physical stability and detonation of ANFO, highly depend on the surface tension, density, viscosity of the used liquid. Therefore, for engineering applications of this material, the determination of density and surface tension of concentrated aqueous solutions of AN is essential. The molecular dynamics (MD) simulation method have been used to investigate the density and the surface tension of high concentrated ammonium nitrate solutions; up to its solubility limit in water. Non-polarisable models for water and ions have carried out the simulations, and the electronic continuum correction model (ECC) uses a scaling of the charges of the ions to apply the polarisation implicitly into the non-polarisable model. The results of calculated density and the surface tension of the solutions have been compared to available experimental values. Our MD simulations show that the non-polarisable model with full-charge ions overestimates the experimental results while the reduce-charge model for the ions fits very well with the experimental data. Ions in the solutions show repulsion from the interface using the non-polarisable force fields. However, when charges of the ions in the original model are scaled in line with the scaling factor of the ECC model, the ions create a double ionic layer near the interface by the migration of anions toward the interface while cations stay in the bulk of the solutions. Similar ions orientations near the interface were observed when polarisable models were used in simulations. In conclusion, applying the ECC model to the non-polarisable force field yields the density and surface tension of the AN solutions with high accuracy in comparison to the experimental measurements.

Keywords: ammonium nitrate, electronic continuum correction, non-polarisable force field, surface tension

Procedia PDF Downloads 212

Authors: Tomas Makaras, Gintaras Svecevičius

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

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

Procedia PDF Downloads 258

Authors: Jayme R. T. Silva, Paulo G. P. Toro, Angelo Passaro, Giannino P. Camillo, Antonio C. Oliveira

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An in-house C++ code has been developed, at the Prof. Henry T. Nagamatsu Laboratory of Aerothermodynamics and Hypersonics from the Institute of Advanced Studies (Brazil), to estimate the aerothermodynamic properties around the Hypersonic Vehicle Integrated to the Scramjet. In the future, this code will be applied to the design of the Brazilian Scramjet Technological Demonstrator 14-X B. The first step towards accomplishing this objective, is to apply the in-house C++ code at the leading edge of a flat plate, simulating the leading edge of the 14-X Hypersonic Vehicle, making possible the wave phenomena of oblique shock and boundary layer to be analyzed. The development of modern hypersonic space vehicles requires knowledge regarding the characteristics of hypersonic flows in the vicinity of a leading edge of lifting surfaces. The strong interaction between a shock wave and a boundary layer, in a high supersonic Mach number 4 viscous flow, close to the leading edge of the plate, considering no slip condition, is numerically investigated. The small slip region is neglecting. The study consists of solving the fluid flow equations for unstructured meshes applying the SIMPLE algorithm for Finite Volume Method. Unstructured meshes are generated by the in-house software ‘Modeler’ that was developed at Virtual’s Engineering Laboratory from the Institute of Advanced Studies, initially developed for Finite Element problems and, in this work, adapted to the resolution of the Navier-Stokes equations based on the SIMPLE pressure-correction scheme for all-speed flows, Finite Volume Method based. The in-house C++ code is based on the two-dimensional Navier-Stokes equations considering non-steady flow, with nobody forces, no volumetric heating, and no mass diffusion. Air is considered as calorically perfect gas, with constant Prandtl number and Sutherland's law for the viscosity. Solutions of the flat plate problem for Mach number 4 include pressure, temperature, density and velocity profiles as well as 2-D contours. Also, the boundary layer thickness, boundary conditions, and mesh configurations are presented. The same problem has been solved by the academic license of the software Ansys Fluent and for another C++ in-house code, which solves the fluid flow equations in structured meshes, applying the MacCormack method for Finite Difference Method, and the results will be compared.

Keywords: boundary-layer, scramjet, simple algorithm, shock wave

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

Procedia PDF Downloads 121

Authors: Christopher B. Obida, George A. Blackburn, James D. Whyatt, Kirk T. Semple

Abstract:

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study, a method was developed for delineating river networks using Sentinel 1 imagery. Unsupervised classification was applied to multi-temporal Sentinel 1 data to discriminate water bodies from other land covers then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines, which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available water body products from United States Geological Survey, European Space Agency and OpenStreetMap and a river network derived from a Shuttle Rader Topography Mission Digital Elevation Model. From both raster-based and vector-based accuracy assessments, it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The geometric river network that was constructed permitted a flow routing analysis which is important for a variety of environmental management and planning applications. The extracted network will potentially be applied for modelling dispersion of hydrocarbon pollutants in Ogoniland, a part of the Niger Delta. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries where such data are deficient.

Keywords: Sentinel 1, image processing, river delineation, large scale mapping, data comparison, geometric network

Procedia PDF Downloads 128

Authors: Pinna Beatrice., Laini Alex, Negro Giovanni, Burgazzi Gemma, Viaroli Pierluigi, Vezza Paolo

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Habitat models rarely consider macroinvertebrates as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the entire community. This research aimed to provide an approach to model the habitat of the macroinvertebrate community. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the meso-habitat scale. Using different datasets gathered from both field data collection and 2D hydrodynamic simulations, the model has been calibrated in the Trebbia river (2019 campaign), and then validated in the Trebbia, Taro, and Enza rivers (2020 campaign). The three rivers are characterized by a braiding morphology, gravel riverbeds, and summer low flows. The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R² coefficient (R²𝒸ᵥ) of the training dataset is 0.71 for the Trebbia River (2019), whereas the R² coefficient for the validation datasets (Trebbia, Taro, and Enza Rivers 2020) is 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and to possible river morphological modifications. Lastly, the proposed approach allows extending the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to flow design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Keywords: ecological flows, macroinvertebrate community, mesohabitat, river habitat modeling

Procedia PDF Downloads 79

Authors: Gao Youtao, Zhao Tanran, Jin Bingyu, Xu Bo

Abstract:

Global navigation satellite system(GNSS) can deliver navigation information for spacecraft orbiting on low-Earth orbits and medium Earth orbits. However, the GNSS cannot navigate the spacecraft on high-Earth orbit or deep space probes effectively. With the deep space exploration becoming a hot spot of aerospace, the demand for a deep space satellite navigation system is becoming increasingly prominent. Many researchers discussed the feasibility and performance of a satellite navigation system on periodic orbits around the Earth-Moon libration points which can be called Lagrangian point satellite navigation system. Autonomous orbit determination (AOD) is an important performance for the Lagrangian point satellite navigation system. With this ability, the Lagrangian point satellite navigation system can reduce the dependency on ground stations. AOD also can greatly reduce total system cost and assure mission continuity. As the elliptical restricted three-body problem can describe the Earth-Moon system more accurately than the circular restricted three-body problem, we study the autonomous orbit determination of Lagrangian navigation constellation using only crosslink range based on elliptical restricted three body problem. Extended Kalman filter is used in the autonomous orbit determination. In order to compare the autonomous orbit determination results based on elliptical restricted three-body problem to the results of autonomous orbit determination based on circular restricted three-body problem, we give the autonomous orbit determination position errors of a navigation constellation include four satellites based on the circular restricted three-body problem. The simulation result shows that the Lagrangian navigation constellation can achieve long-term precise autonomous orbit determination using only crosslink range. In addition, the type of the libration point orbit will influence the autonomous orbit determination accuracy.

Keywords: extended Kalman filter, autonomous orbit determination, quasi-periodic orbit, navigation constellation

Procedia PDF Downloads 270