Search results for: trend detection

Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Nadeem Qaiser, Sherjeel Munsif Khan, Muhammad Mustafa Hussian, Vincent Tung

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One of the emerging fields in the detection of chronic diseases is based on the point-of-care (POC) early monitoring of the symptoms and thus provides a state-of-the-art personalized healthcare system. Nowadays, wearable and flexible sensors are being used for analyzing sweat, glucose, blood pressure, and other skin conditions. However, localized jaw-bone swelling called parotid-swelling caused by some viruses has never been tracked before. To track physical motion or deformations, strain sensors, especially piezoresistive ones, are widely used. This work, for the first time, reports carbon nanotubes (CNTs)-based piezoresistive sensing patch that is highly flexible and stretchable and can record muscle deformations in real-time. The developed patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. To calibrate the volumetric muscle expansion, we fabricated the pneumatic actuator that experienced volumetric expansion and thus redefined the gauge factor. Moreover, we employ a Bluetooth-low-energy system that can send information about muscle activity in real-time to a smartphone app. We utilized COMSOL calculations to reveal the mechanical robustness of the patch. The experiments showed the sensing patch's greater cyclability, making it a patch for personal healthcare and an excellent choice for monitoring the real-time POC monitoring of the human muscle swelling.

Keywords: piezoresistive strain sensor, FEM simulations, CNTs sensor, flexible

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Authors: Emily Kislik, Gabriel Mantilla Saltos, Gladys Torres, Mercy Borbor-Córdova

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The Galápagos Marine Reserve (GMR) is an internationally-recognized region of consistent upwelling events, high productivity, and rich biodiversity. Despite its high-nutrient, low-chlorophyll condition, the archipelago has experienced phytoplankton blooms, especially in the western section between Isabela and Fernandina Islands. However, little is known about how climate variability will affect future phytoplankton standing stock in the Galápagos, and no consistent protocols currently exist to quantify phytoplankton biomass, identify species, or monitor for potential harmful algal blooms (HABs) within the archipelago. This analysis investigates physical, chemical, and biological oceanic variables that contribute to algal blooms within the GMR, using 4 km Aqua MODIS satellite imagery and 0.125-degree wind stress data from January 2003 to December 2016. Furthermore, this study analyzes chlorophyll-a concentrations at varying spatial scales— within the greater archipelago, as well as within five smaller bioregions based on species biodiversity in the GMR. Seasonal and interannual trend analyses, correlations, and hotspot identification were performed. Results demonstrate that chlorophyll-a is expressed in two seasons throughout the year in the GMR, most frequently in September and March, with a notable hotspot in the Elizabeth Bay bioregion. Interannual chlorophyll-a trend analyses revealed highest peaks in 2003, 2007, 2013, and 2016, and variables that correlate highly with chlorophyll-a include surface temperature and particulate organic carbon. This study recommends future in situ sampling locations for phytoplankton monitoring, including the Elizabeth Bay bioregion. Conclusions from this study contribute to the knowledge of oceanic drivers that catalyze primary productivity and consequently affect species biodiversity within the GMR. Additionally, this research can inform policy and decision-making strategies for species conservation and management within bioregions of the Galápagos.

Keywords: bioregions, ecological monitoring, phytoplankton, remote sensing

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Authors: Prince Suka Neekia Momta, Rita Iheoma Achonyeulo

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Ant Tracking seismic attribute applied over 4-seconds seismic volume revealed structural features triggered by clay diapirism, growth fault development, rapid deltaic sedimentation and intense drilling. The attribute was extracted on vertical seismic sections and time slices. Mega tectonic structures such as growth faults and clay diapirs are visible on vertical sections with obscured minor lineaments or fractures. Fractures are distinctively visible on time slices yielding recognizable patterns corroborating established geologic models. This model seismic attribute enabled the understanding of fluid flow characteristics and production responses. Three structural patterns recognized in the field include: major growth faults, minor faults or lineaments and network of fractures. Three growth faults mapped on seismic section form major deformation bands delimiting the area into three blocks or depocenters. The growth faults trend E-W, dip down-to-south in the basin direction, and cut across the study area. The faults initiating from about 2000ms extended up to 500ms, and tend to progress parallel and opposite to the growth direction of an upsurging diapiric structure. The diapiric structures form the major deformational bands originating from great depths (below 2000ms) and rising to about 1200ms where series of sedimentary layers onlapped and pinchout stratigraphically against the diapir. Several other secondary faults or lineaments that form parallel streaks to one another also accompanied the growth faults. The fracture networks have no particular trend but form a network surrounding the well area. Faults identified in the study area have potentials for structural hydrocarbon traps whereas the presence of fractures created a fractured-reservoir condition that enhanced rapid fluid flow especially water. High aquifer flow potential aided by possible fracture permeability resulted in rapid decline in oil rate. Through the application of Ant Tracking attribute, it is possible to obtain detailed interpretation of structures that can have direct influence on oil and gas production.

Keywords: seismic, attributes, production, structural

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Authors: Restuning Widiasih, Katherine Nelson, Joan Skinner

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Muslim husbands have significant roles in the family including their roles in women’s health and illness. However, studies that explore Muslim husbands’ participation in women’s health is limited. The objective of this study was to uncover Muslim husbands’ participation in women’ health and illness including cancer prevention and screening. A descriptive exploratory approach was used involving 20 Muslim women from urban and rural areas of West Java Province, Indonesia. Muslim women shared experience related to their husbands support and activities in women’s health and illness. The data from the interviews were analyzed using the Comparative Analysis for Interview (CAI). Women perceived that husbands fully supported their health by providing opportunities for activities, and reminding them about healthy food, their workloads, and family planning. Husbands actively involved when women faced health issues including sharing knowledge and experience, discussing any health problems, advising for medical check-ups, and accompanying them for treatments. The analysis also found that husbands were less active and offered less advice regarding prevention and early detection of cancer. This study highlights the significant involvement of Muslim husbands in women’s health and illness, yet a lack of support from husbands related to screening and cancer prevention. This condition could be a burden for Muslim women to participate in health programs related to cancer prevention and early detection. Health education programs to improve Muslim husbands’ understanding of women’s health is needed.

Keywords: descriptive exploratory study, Muslim husbands, Muslim women, women's health and illness

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Authors: Huajuan Shi

Abstract:

Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

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Authors: M. Moreno, M. Alique, D. Otero, C. Delgado, P. Lacharmoise, L. Gracia, L. Pires, A. Moya

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Marine environments are increasingly threatened by heavy metal contamination, including mercury (Hg), lead (Pb), and cadmium (Cd), posing significant risks to ecosystems and human health. Traditional monitoring techniques often fail to provide the spatial and temporal resolution needed for real-time detection of these contaminants, especially in remote or harsh environments. SEAWIZARD addresses these challenges by leveraging the flexibility, adaptability, and cost-effectiveness of printed electronics, with the integration of microfluidics to develop a compact, portable, and reusable sensor platform designed specifically for real-time monitoring of heavy metal ions in seawater. The SEAWIZARD sensor is a multiparametric Lab-on-Chip (LoC) device, a miniaturized system that integrates several laboratory functions into a single chip, drastically reducing sample volumes and improving adaptability. This platform integrates three printed graphene electrodes for the simultaneous detection of Hg, Cd and Pb via square wave voltammetry. These electrodes share the reference and the counter electrodes to improve space efficiency. Additionally, it integrates printed pH and temperature sensors to correct environmental interferences that may impact the accuracy of metal detection. The pH sensor is based on a carbon electrode with iridium oxide electrodeposited while the temperature sensor is graphene based. A protective dielectric layer is printed on top of the sensor to safeguard it in harsh marine conditions. The use of flexible polyethylene terephthalate (PET) as the substrate enables the sensor to conform to various surfaces and operate in challenging environments. One of the key innovations of SEAWIZARD is its integrated microfluidic layer, fabricated from cyclic olefin copolymer (COC). This microfluidic component allows a controlled flow of seawater over the sensing area, allowing for significant improved detection limits compared to direct water sampling. The system’s dual-channel design separates the detection of heavy metals from the measurement of pH and temperature, ensuring that each parameter is measured under optimal conditions. In addition, the temperature sensor is finely tuned with a serpentine-shaped microfluidic channel to ensure precise thermal measurements. SEAWIZARD also incorporates custom electronics that allow for wireless data transmission via Bluetooth, facilitating rapid data collection and user interface integration. Embedded artificial intelligence further enhances the platform by providing an automated alarm system, capable of detecting predefined metal concentration thresholds and issuing warnings when limits are exceeded. This predictive feature enables early warnings of potential environmental disasters, such as industrial spills or toxic levels of heavy metal pollutants, making SEAWIZARD not just a detection tool, but a comprehensive monitoring and early intervention system. In conclusion, SEAWIZARD represents a significant advancement in printed electronics applied to environmental sensing. By combining flexible, low-cost materials with advanced microfluidics, custom electronics, and AI-driven intelligence, SEAWIZARD offers a highly adaptable and scalable solution for real-time, high-resolution monitoring of heavy metals in marine environments. Its compact and portable design makes it an accessible, user-friendly tool with the potential to transform water quality monitoring practices and provide critical data to protect marine ecosystems from contamination-related risks.

Keywords: lab-on-chip, printed electronics, real-time monitoring, microfluidics, heavy metal contamination

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Authors: Meysam Saidi

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While the specific externalities and required regulatory measures in relation to Islamic banking are fairly uncertain, the business is growing across the world. Unofficial data indicate that the Islamic Finance market is growing with annual rate of 15% and it has reached 1.3 $ trillion size. This trend is associated with inherent systematic connection of Islamic financial institutions to other entities and different sectors of economies. Islamic banking has been subject of market development policies in major economies, most notably the UK. This trend highlights the need for identification of distinct risk features of Islamic banking and crafting customized regulatory measures. So far there has not been a significant systemic crisis in this market which can be attributed to its distinct nature. However, the significant growth and spread of its products worldwide necessitate an in depth study of its nature for customized congruent regulatory measures. In the post financial crisis era some market analysis and reports suggested that the Islamic banks fairly weathered the crisis. As far as heavily blamed conventional financial products such as subprime mortgage backed securities and speculative credit default swaps were concerned the immunity claim can be considered true, as Islamic financial institutions were not directly exposed to such products. Nevertheless, similar to the experience of the conventional banking industry, it can be only a matter of time for Islamic banks to face failures that can be specific to the nature of their business. Using the experience of conventional banking regulations and identifying those peculiarities of Islamic banking that need customized regulatory approach can aid to prevent major failures. Frank Knight has stated that “We perceive the world before we react to it, and we react not to what we perceive, but always to what we infer”. The debate over congruent Islamic banking regulations might not be an exception to Frank Knight’s statement but I will try to base my discussion on concrete evidences. This paper first analyzes both theoretical and actual features of Islamic banking in order to ascertain to its peculiarities in terms of market stability and other externalities. Next, the paper discusses distinct features of Islamic financial transactions and banking which might require customized regulatory measures. Finally, the paper explores how a more transparent path for the Islamic banking regulations can be drawn.

Keywords: Islamic banking, regulation, risks, capital requirements, customer protection, financial stability

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Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

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Authors: Sandeep Sharma, Sarabjit Singh

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As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

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Authors: Shreya Srivastava, Sagnik Dey

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Aerosol direct radiative forcing (ADRF) refers to the alteration of the Earth's energy balance from the scattering and absorption of solar radiation by aerosol particles. India experiences substantial ADRF due to high aerosol loading from various sources. These aerosols' radiative impact depends on their physical characteristics (such as size, shape, and composition) and atmospheric distribution. Quantifying ADRF is crucial for understanding aerosols’ impact on the regional climate and the Earth's radiative budget. In this study, we have taken radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 22 years (2000-2021) over the Indian subcontinent. Except for a few locations, the short-wave DARF exhibits aerosol cooling at the TOA (values ranging from +2.5 W/m2 to -22.5W/m2). Cooling due to aerosols is more pronounced in the absence of clouds. Being an aerosol hotspot, higher negative ADRF is observed over the Indo-Gangetic Plain (IGP). Aerosol Forcing Efficiency (AFE) shows a decreasing seasonal trend in winter (DJF) over the entire study region while an increasing trend over IGP and western south India during the post-monsoon season (SON) in clear-sky conditions. Analysing atmospheric heating and AOD trends, we found that only the aerosol loading is not governing the change in atmospheric heating but also the aerosol composition and/or their vertical profile. We used a Multi-angle Imaging Spectro-Radiometer (MISR) Level-2 Version 23 aerosol products to look into aerosol composition. MISR incorporates 74 aerosol mixtures in its retrieval algorithm based on size, shape, and absorbing properties. This aerosol mixture information was used for analysing long-term changes in aerosol composition and dominating aerosol species corresponding to the aerosol forcing value. Further, ADRF derived from this method is compared with around 35 studies across India, where a plane parallel Radiative transfer model was used, and the model inputs were taken from the OPAC (Optical Properties of Aerosols and Clouds) utilizing only limited aerosol parameter measurements. The result shows a large overestimation of TOA warming by the latter (i.e., Model-based method).

Keywords: aerosol radiative forcing (ARF), aerosol composition, MISR, CERES, SBDART

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Authors: Afri Ananda Nugroho

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Privatization is one of the public policies closely related to the role of government in the economy due to the failure of the centralized system in the communist countries. This paper will discuss the basic issues of privatization as a global trend, the purpose of privatization, implementation, and impact on the success of State-Owned Enterprises (BUMN) in Indonesia. The analysis is done by looking at some important issues about the privatization problem, and some public policies are being applied such as why and how privatization is necessary and what impact it has. This paper also discusses the implications for top leaders of State-Owned Enterprises.

Keywords: privatization, state-owned enterprises, Indonesia, public policy

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Authors: Ahmed A. Farag, M. A. Hgazy

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The influence of three Schiff bases (SB-I, SB-II, and SB-III) on the corrosion of carbon steel in 0.5 M H2SO4 solution was studied by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The inhibition efficiency increases with the concentration of the Schiff bases and follow the trend: SB-III > SB-II > SB-I. Tafel polarization measurements revealed that the three tested inhibitors function as anodic inhibitors. The thermodynamic parameters Kads and ΔGºads are calculated and discussed. The Langmuir isotherm equation was found to provide an accurate description of the adsorption behaviour of the investigated Schiff bases. Depending on the results, the inhibitive mechanism was proposed.

Keywords: Schiff bases, corrosion inhibitors, EIS, adsorption

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Authors: Revaz Gvelesiani

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Nowadays, social inequality increasingly strengthens the trend from business entrepreneurship to social entrepreneurship. It can be said that business entrepreneurs, according to their interests, move towards social entrepreneurship. Effectively operating markets create mechanisms, which lead to 'good' behavior. This is the most important feature of the rationally functioning society. As for the prospects of social entrepreneurship, expansion of entrepreneurship concept at the social arena may lead to such an outcome, when people who are skeptical about business, become more open towards entrepreneurship as a type of activity. This is the way which by means of increased participation in entrepreneurship promotes fair distribution of wealth. Today 'entrepreneurship for all' is still a dream, although the one, which may come true.

Keywords: social entrepreneurship, business entrepreneurship, functions of entrepreneurship, social inequality, social interests, interest groups, interest conflicts

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Authors: Olivier Chaigneau, Damianos Oikonomidis, Marie-Cecile Delmas

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GAMBAS (Galileo Advanced features for the Maritime domain: Breakthrough Applications for Safety and security) is a project funded by the European Space Program Agency (EUSPA) aiming at identifying the search-and-rescue and ship security alert system needs for maritime users (including operators and fishing stakeholders) and developing operational concepts to answer these needs. The general objective of the GAMBAS project is to support the deployment of Galileo exclusive features in the maritime domain in order to improve safety and security at sea, detection of illegal activities and associated surveillance means, resilience to natural and human-induced emergency situations, and develop, integrate, demonstrate, standardize and disseminate these new associated capabilities. The project aims to demonstrate: improvement of the SAR (Search And Rescue) and SSAS (Ship Security Alert System) detection and response to maritime distress through the integration of new features into the beacon for SSAS in terms of cost optimization, user-friendly aspects, integration of Galileo and OS NMA (Open Service Navigation Message Authentication) reception for improved authenticated localization performance and reliability, and at sea triggering capabilities, optimization of the responsiveness of RCCs (Rescue Co-ordination Centre) towards the distress situations affecting vessels, the adaptation of the MCCs (Mission Control Center) and MEOLUT (Medium Earth Orbit Local User Terminal) to the data distribution of SSAS alerts.

Keywords: Galileo new advanced features, maritime, safety, security

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Huajuan Shi

Abstract:

Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

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Authors: Rafsan Al Mamun, Md. Motaharul Islam, Rabana Tajrin, Nabiha Noor, Shafinaz Qader

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In an attempt to enrich the lives of billions of people by providing proper information, security and a way of communicating with others, the need for efficient and improved satellites is constantly growing. Thus, there is an increasing demand for better error detection and correction (EDAC) schemes, which are capable of protecting the data onboard the satellites. The paper is aimed towards detecting and correcting such errors using a special algorithm called the Hamming Code, which uses the concept of parity and parity bits to prevent single-bit errors onboard a satellite in Low Earth Orbit. This paper focuses on the study of Low Earth Orbit satellites and the process of generating the Hamming Code matrix to be used for EDAC using computer programs. The most effective version of Hamming Code generated was the Hamming (16, 11, 4) version using MATLAB, and the paper compares this particular scheme with other EDAC mechanisms, including other versions of Hamming Codes and Cyclic Redundancy Check (CRC), and the limitations of this scheme. This particular version of the Hamming Code guarantees single-bit error corrections as well as double-bit error detections. Furthermore, this version of Hamming Code has proved to be fast with a checking time of 5.669 nanoseconds, that has a relatively higher code rate and lower bit overhead compared to the other versions and can detect a greater percentage of errors per length of code than other EDAC schemes with similar capabilities. In conclusion, with the proper implementation of the system, it is quite possible to ensure a relatively uncorrupted satellite storage system.

Keywords: bit-flips, Hamming code, low earth orbit, parity bits, satellite, single error upset

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Authors: Katarina Pogacnik, Ursa Zakrajsek, Nejc Sirk, Ziga Lampret

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Due to an increase in living standards along with global population growth and a trend of urbanization, municipalities and regions are faced with an ever rising energy demand. A challenge has arisen for cities around the world to modify the energy supply chain in order to reduce its consumption and CO₂ emissions. The aim of our work is the development of a computational-analytical platform for dynamic support in decision-making and the determination of economic and technical indicators of energy efficiency in a smart city, named Energy Atlas. Similar products in this field focuse on a narrower approach, whereas in order to achieve its aim, this platform encompasses a wider spectrum of beneficial and important information for energy planning on a local or regional scale. GIS based interactive maps provide an extensive database on the potential, use and supply of energy and renewable energy sources along with climate, transport and spatial data of the selected municipality. Beneficiaries of Energy atlas are local communities, companies, investors, contractors as well as residents. The Energy Atlas platform consists of three modules named E-Planning, E-Indicators and E-Cooperation. The E-Planning module is a comprehensive data service, which represents a support towards optimal decision-making and offers a sum of solutions and feasibility of measures and their effects in the area of efficient use of energy and renewable energy sources. The E-Indicators module identifies, collects and develops optimal data and key performance indicators and develops an analytical application service for dynamic support in managing a smart city in regards to energy use and sustainable environment. In order to support cooperation and direct involvement of citizens of the smart city, the E-cooperation is developed with the purpose of integrating the interdisciplinary and sociological aspects of energy end-users. Interaction of all the above-described modules contributes to regional development because it enables for a precise assessment of the current situation, strategic planning, detection of potential future difficulties and also the possibility of public involvement in decision-making. From the implementation of the technology in Slovenian municipalities of Ljubljana, Piran, and Novo mesto, there is evidence to suggest that the set goals are to be achieved to a great extent. Such thorough urban energy planning tool is viewed as an important piece of the puzzle towards achieving a low-carbon society, circular economy and therefore, sustainable society.

Keywords: circular economy, energy atlas, energy management, energy planning, low-carbon society

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Authors: Donghyun Lee, Cam Nguyen

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Multi-band systems offer a great deal of benefit in modern communication and radar systems. In particular, multi-band antenna-array radar systems with their extended frequency diversity provide numerous advantages in detection, identification, locating and tracking a wide range of targets, including enhanced detection coverage, accurate target location, reduced survey time and cost, increased resolution, improved reliability and target information. An accurate calibration is a critical issue in antenna array systems. The amplitude and phase errors in multi-band and multi-polarization antenna array transceivers result in inaccurate target detection, deteriorated resolution and reduced reliability. Furthermore, the digital beam former without the RF domain phase-shifting is less immune to unfiltered interference signals, which can lead to receiver saturation in array systems. Therefore, implementing integrated front-end architecture, which can support calibration function with low insertion and filtering function from the farthest end of an array transceiver is of great interest. We report a dual K/Ka-band T/R/Calibration switch module with quasi-elliptic dual-bandpass filtering function implementing a Q-enhanced metamaterial transmission line. A unique dual-band frequency response is incorporated in the reception and calibration path of the proposed switch module utilizing the composite right/left-handed meta material transmission line coupled with a Colpitts-style negative generation circuit. The fabricated fully integrated T/R/Calibration switch module in 0.18-μm BiCMOS technology exhibits insertion loss of 4.9-12.3 dB and isolation of more than 45 dB in the reception, transmission and calibration mode of operation. In the reception and calibration mode, the dual-band frequency response centered at 24.5 and 35 GHz exhibits out-of-band rejection of more than 30 dB compared to the pass bands below 10.5 GHz and above 59.5 GHz. The rejection between the pass bands reaches more than 50 dB. In all modes of operation, the IP1-dB is between 4 and 11 dBm. Acknowledgement: This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: microwaves, millimeter waves, T/R switch, wireless communications, wireless communications

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization

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Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

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This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

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Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

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Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

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Authors: Waleed K. Saib, Abdulsalam M. Alhawsawi, Essam Banoqitah

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The idea of this research is to design a radiation spectrometer using LSO scintillation detector coupled to a C series of SiPM (silicon photomultiplier). The device can be used to detects gamma and X-ray radiation. This device is also designed to estimates the activity of the source contamination. The SiPM will detect light in the visible range above the threshold and read them as counts. Three gamma sources were used for these experiments Cs-137, Am-241 and Co-60 with various activities. These sources are applied for four experiments operating the SiPM as a spectrometer, energy resolution, pile-up set and efficiency. The SiPM is connected to a MCA to perform as a spectrometer. Cerium doped Lutetium Silicate (Lu₂SiO₅) with light yield 26000 photons/Mev coupled with the SiPM. As a result, all the main features of the Cs-137, Am-241 and Co-60 are identified in MCA. The experiment shows how photon energy and probability of interaction are inversely related. Total attenuation reduces as photon energy increases. An analytical calculation was made to obtain the FWHM resolution for each gamma source. The FWHM resolution for Am-241 (59 keV) is 28.75 %, for Cs-137 (662 keV) is 7.85 %, for Co-60 (1173 keV) is 4.46 % and for Co-60 (1332 keV) is 3.70%. Moreover, the experiment shows that the dead time and counts number decreased when the pile-up rejection was disabled and the FWHM decreased when the pile-up was enabled. The efficiencies were calculated at four different distances from the detector 2, 4, 8 and 16 cm. The detection efficiency was observed to declined exponentially with increasing distance from the detector face. Conclusively, the SiPM board operated with an LSO scintillator crystal as a spectrometer. The SiPM energy resolution for the three gamma sources used was a decent comparison to other PMTs.

Keywords: PMT, radiation, radiation detection, scintillation detectors, silicon photomultiplier, spectrometer

Procedia PDF Downloads 155

Authors: Hong Yu, Ion Matei

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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

Procedia PDF Downloads 195

Authors: Kasun U. G. Palihakkara

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Although vaccine coverage in Sri Lanka is close to 100%, with the widely spreading vaccine rejection trend reaching South Asian regions, it is essential to catch on whether Sri Lankans are being misinformed from the common misconceptions regarding vaccines. As the rates of target diseases decrease, parents become less accepting of even minor common adverse events. It is essential to preserve the integrity of immunization programs and protect public health by finding out the prevalence of anti-immunization trends. The primary objective of this study was to assess the immunization practices and prevalence of trends related to anti-immunization among urban community in Sri Lanka. A descriptive cross-sectional quantitative study on 323 participants using convenient sampling with 213 self-administered questionnaires. Additionally, 110 online questionnaires were distributed. 31% of the study population does not maintain immunization records for their children. While majority seek information regarding immunization from reliable sources such as the family physician or specialist pediatricians, 30% also refer to unreliable sources such as online communities for their opinion. 31% of study population had not vaccinated for Japanese encephalitis. 73% of the study population had encountered with side effects of vaccination such as fever & 47% believed that such side effects are rare. 52% of the population had hostile attitude regarding the administration of several doses multiple vaccines within a child’s first year. Diseases like polio had been successfully eradicated from Sri Lanka with the help of vigorous vaccination programs. However, majority of the study population believe that there’s no need to keep vaccinating the children for those eradicated diseases and exposing the child for adverse effects of such vaccines. Majority of the population were aware of the existing misconceptions regarding immunization. The most popular misconceptions about vaccines popular among the study population were the MMR (Measles, Mumps, and Rubella) vaccine being a possible cause leading to autism and bowel disease and children getting infected with the disease even after they get vaccinated, may be due to the inactivated vaccine. Disturbingly 22% of the study population believed that vaccines are useless in preventing diseases nowadays. These data obtained from the urban study population reveals that even though statistically Sri Lankan immunization coverage is 100%, there is a possibility of anti-vaccination trend arising in Sri Lanka due to the prevalence of various misconceptions and rumors related to it. Therefore these data recommend the need for thorough awareness among the mothers.

Keywords: anti-vaccination, immunization, infectious diseases, pediatric health

Procedia PDF Downloads 139

Authors: Ananya Pal, Neelakshi Sarkar, Debraj Saha, Dipanwita Das, Subhashish Kamal Guha, Bibhuti Saha, Runu Chakravarty

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Presently, tenofovir disoproxil fumurate (TDF) is the most effective anti-viral agent for the treatment of hepatitis B virus (HBV) in individuals co-infected with HIV and HBV as TDF has activity to suppress both wild-type and lamivudine (3TC)-resistant HBV. However, suboptimal response to TDF was reported in HIV-HBV co-infected individuals with prior 3TC therapy from different countries recently. The incidence of 3TC-resistant HBV strains is quite high in HIV-HBV co-infected patients experiencing long-term anti-retroviral therapy (ART) in eastern India. In spite of this risk, most of the patients with long-term 3TC treatment are continued with the same anti-viral agent in this country. Only a few have received TDF in addition to 3TC in the ART regimen since TDF has been available in India for the treatment of HIV-infected patients in 2012. In this preliminary study, we investigated the virologic and biochemical parameters among HIV-HBV co-infected patients who are non-responders to 3TC treatment during the continuation of 3TC or TDF add-on to 3TC in their ART regimen. Fifteen HIV-HBV co-infected patients who experienced long-term 3TC (mean duration months 36.87 ± 24.08 months) were identified with high HBV viremia ( > 20,000 IU/ml) or harbouring 3TC-resistant HBV. These patients receiving ART from School of Tropical Medicine Kolkata, the main ART centre in eastern India were followed-up semi-annually for next three visits. Different virologic parameters including quantification of plasma HBV load by real-time PCR, detection of hepatitis B e antigen (HBeAg) by commercial ELISA and anti-viral resistant mutations by sequencing were studied. During three follow-up among study subjects, 86%, 47%, and 43% had 3TC-mono-therapy (mean treatment-duration 41.54±18.84, 49.67±11.67, 54.17±12.37 months respectively) whereas 14%, 53%, and 57% experienced TDF in addition to 3TC (mean treatment duration 4.5±2.12, 16.56±11.06, and 23±4.07 months respectively). Mean CD4 cell-count in patients receiving 3TC was tended to be lower during third follow-up as compared to the first and the second [520.67±380.30 (1st), 454.8±196.90 (2nd), and 397.5±189.24 (3rd) cells/mm3) and similar trend was seen in patients experiencing TDF in addition to 3TC [334.5±330.218 (1st), 476.5±194.25 (2nd), and 461.17±269.89 (3rd) cells/mm3]. Serum HBV load was increased during successive follow-up of patients with 3TC-mono-therapy. Initiation of TDF lowered serum HBV-load among 3TC-non-responders at the time of second visit ( < 2,000 IU/ml), interestingly during third follow-up, mean HBV viremia increased >1 log IU/ml (mean 3.56±2.84 log IU/ml). Persistence of 3TC-resistant double and triple mutations was also observed in both the treatment regimens. Mean serum alanine aminotransferase remained elevated in these patients during this follow-up study. Persistence of high HBV viraemia and 3TC-resistant mutation in HBV during the continuation of 3TC might lead to major public health threat in India. The inclusion of TDF in the ART regimen of 3TC non-responder HIV-HBV co-infected patients showed adverse treatment response in terms of virologic and biochemical parameters. Therefore, serious attention is necessary for proper management of long-term 3TC experienced HIV-HBV co-infected patients with high HBV viraemia or 3TC-resistant HBV mutants in India.

Keywords: HBV, HIV, TDF, 3TC-resistant

Procedia PDF Downloads 374

Authors: Rajendra N. Ghosh, Paramjeet Singh, Niranjan Khandelwal, Sameer Vyas, Pratibha Singhi, Naveen Sankhyan

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Background: Tuberculoma and neurocysticercosis (NCC) are two most common intracranial infections in developing country. They often simulate on neuroimaging and in absence of typical imaging features cause significant diagnostic dilemmas. Differentiation is extremely important to avoid empirical exposure to antitubercular medications or nonspecific treatment causing disease progression. Purpose: Better characterization and differentiation of CNS tuberculoma and NCC by using morphological and multiple advanced functional MRI. Material and Methods: Total fifty untreated patients (20 tuberculoma and 30 NCC) were evaluated by using conventional and advanced sequences like CISS, SWI, DWI, DTI, Magnetization transfer (MT), T2Relaxometry (T2R), Perfusion and Spectroscopy. rCBV,ADC,FA,T2R,MTR values and metabolite ratios were calculated from lesion and normal parenchyma. Diagnosis was confirmed by typical biochemical, histopathological and imaging features. Results: CISS was most useful sequence for scolex detection (90% on CISS vs 73% on routine sequences). SWI showed higher scolex detection ability. Mean values of ADC, FA,T2R from core and rCBV from wall of lesion were significantly different in tuberculoma and NCC (P < 0.05). Mean values of rCBV, ADC, T2R and FA for tuberculoma and NCC were (3.36 vs1.3), (1.09x10⁻³vs 1.4x10⁻³), (0.13 x10⁻³ vs 0.09 x10⁻³) and (88.65 ms vs 272.3 ms) respectively. Tuberculomas showed high lipid peak, more choline and lower creatinine with Ch/Cr ratio > 1. T2R value was most significant parameter for differentiation. Cut off values for each significant parameters have proposed. Conclusion: Quantitative MRI in combination with conventional sequences can better characterize and differentiate similar appearing tuberculoma and NCC and may be incorporated in routine protocol which may avoid brain biopsy and empirical therapy.

Keywords: advanced functional MRI, differentiation, neurcysticercosis, tuberculoma

Procedia PDF Downloads 568

Authors: Folajimi Oladimeji Shorunke

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Background: As of October 9, 2023, diphtheria has been noted to be re-emerging in four African countries: Algeria, Guinea, Niger, and Nigeria. 14,587 cases with a case fatality rate of 4.1% have been reported across these regions, with Nigeria alone responsible for over 90% of the cases. In Taraba State Nigeria, the index case of Diphtheria was reported on epidemic week 34, August 24, 2023 with 75 confirmed cases found 3 months after the index case and a case fatality of 1.3%. it described the distribution, trend and common symptoms found during the Outbreak. Methods: The Taraba State Diphtheria Outbreak line list on the Surveillance Outbreak Response Management & Analysis System (SORMAS) for all its 16 local government areas (LGAs) was analyzed using descriptive statistics (graphs, chats and maps) for the period between 24th August to 25th November 2023. Primary data was collected through the use of case investigation forms and variables like Age, gender, date of disease onset, LGA of residence, and symptoms exhibited were collected. Naso-pharyngeal and oro-pharyngeal samples were also collected for Laboratory confirmation. The most common diphtheria symptoms during the outbreak were also highlighted. Results: A total of 75 Diphtheria cases were diagnosed in 10 of the 16 LGAs in Taraba State between 24th August to 25th November 2023, 72% of the cases were female, with the age range 0-9 years having the highest proportion of 34 (45.3%), the number of positive diagnosis reduces with age among cases. The Northern part of the State had the highest proportion of cases, 68 (90.7%), with Ardo-Kola LGA having the highest 28 (29%). The remaining 9.2% of cases is shared among the middle belt and southern part of the State. The Epi-curve took the characteristic shape of a propagated infection with peaks at the 37th, 39th and 45th epidemic weeks. The most common symptoms found in cases were fever 71 (94.7%), pharyngitis 65( 86.7%), tonsillitis 60 (80%), and laryngitis 53 (71%). Conclusions: The number of confirmed cases of Diphtheria in Taraba State, Nigeria between 24th August to 25th November 2023 is 75. The condition is higher among females than male and mostly affected children between ages 0-9 with the northern part of the state most affected. The most common symptoms exhibited by cases include fever, pharyngitis, tonsillitis and laryngitis.

Keywords: diphtheria outbreak, taraba nigeria, descriptive epidemiology, trend

Procedia PDF Downloads 69

Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

Procedia PDF Downloads 275