Search results for: pre-processing image algorithms

Authors: Thangavelu Muthukumar, Thotapalli Parvathaleswara Sastry

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Collagen is the most abundantly available connective tissue protein, which is being used as a biomaterial for various biomedical applications. Presently, fish wastes are disposed improperly which is causing serious environmental pollution resulting in offensive odour. Fish scales are promising source of Type I collagen. Medicinal plants have been used since time immemorial for treatment of various ailments of skin and dermatological disorders especially cuts, wounds, and burns. Developing biomaterials from the natural sources which are having wound healing properties within the search of a common man is the need of hour, particularly in developing and third world countries. With these objectives in view we have developed a wound dressing material containing fish scale collagen (FSC) incorporated with Macrotyloma uniflorum plant extract (PE). The wound dressing composite was characterized for its physiochemical properties using conventional methods. SEM image revealed that the composite has fibrous and porous surface which helps in transportation of oxygen as well as absorbing wound fluids. The biomaterial has shown 95% biocompatibility with required mechanical strength and has exhibited antimicrobial properties. This biomaterial has been used as a wound dressing material in experimental wounds of rats. The healing pattern was evaluated by macroscopic observations, panimetric studies, biochemical, histopathological observations. The results showed faster healing pattern in the wounds treated with CSPE compared to the other composites used in this study and untreated control. These experiments clearly suggest that CSPE can be used as wound/burn dressing materials.

Keywords: collagen, wound dressing, Macrotyloma uniflorum, burn dressing

Procedia PDF Downloads 393

Authors: Maria Pavlova

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In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

Procedia PDF Downloads 204

Authors: Arpita Mondal, Aurobinda Routray, Sreeraj Puravankara, Rajashree Biswas

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The most difficult part of designing a battery management system (BMS) is battery modeling. A good battery model can capture the dynamics which helps in energy management, by accurate model-based state estimation algorithms. So far the most suitable and fruitful model is the equivalent circuit model (ECM). However, in real-time applications, the model parameters are time-varying, changes with current, temperature, state of charge (SOC), and aging of the battery and this make a great impact on the performance of the model. Therefore, to increase the equivalent circuit model performance, the parameter estimation has been carried out in the frequency domain. The battery is a very complex system, which is associated with various chemical reactions and heat generation. Therefore, it’s very difficult to select the optimal model structure. As we know, if the model order is increased, the model accuracy will be improved automatically. However, the higher order model will face the tendency of over-parameterization and unfavorable prediction capability, while the model complexity will increase enormously. In the time domain, it becomes difficult to solve higher order differential equations as the model order increases. This problem can be resolved by frequency domain analysis, where the overall computational problems due to ill-conditioning reduce. In the frequency domain, several dominating frequencies can be found in the input as well as output data. The selective frequency domain estimation has been carried out, first by estimating the frequencies of the input and output by subspace decomposition, then by choosing the specific bands from the most dominating to the least, while carrying out the least-square, recursive least square and Kalman Filter based parameter estimation. In this paper, a second order battery model consisting of three resistors, two capacitors, and one SOC controlled voltage source has been chosen. For model identification and validation hybrid pulse power characterization (HPPC) tests have been carried out on a 2.6 Ah LiFePO₄ battery.

Keywords: equivalent circuit model, frequency estimation, parameter estimation, subspace decomposition

Procedia PDF Downloads 128

Authors: Farid Adisaputra Gumilang

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Kucing Liar Underground Mine is a future mine of PT Freeport Indonesia PTFI that is currently being developed. In the development process, problems were found when blasting the tunnels; there were overbreak, and void occur caused by geological contact or poor rock conditions. Geotechnical engineers must evaluate not only the remnant capacity of ground support systems but also investigate the depth of rock mass yield within pillars. To prevent the potential hazard caused by void zones, geotechnical engineers must ensure the planned drift is mined in the best location where people can work safely. GPR, or Ground penetrating radar, is a geophysical method that can image the subsurface. This non-destructive method uses electromagnetic radiation and detects the reflected signals from subsurface structures. The GPR survey measurements are conducted 48 meters along the drift that has a poor ground condition with 150MHz antenna with several angles (roof, wall, and floor). Concern grounds are determined by the continuity of reflector/low reflector in the radargram section. Concern grounds are determined by the continuity of reflector/low reflector in the radargram section. In this paper, processing data using instantaneous amplitude to identify the void zone. In order to have a good interpretation and result, it combines with the geological information and borehole camera data, so the calibrated GPR data allows the geotechnical engineer to determine the safe location to change the drift location.

Keywords: underground mine, ground penetrating radar, reflectivity, borehole camera

Procedia PDF Downloads 59

Authors: P. Selva, B. Lorraina, J. Alexis, A. Seror, A. Longuet, C. Mary, F. Denard

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Over the past four decades, the fatigue behavior of nickel-based alloys has been widely studied. However, in recent years, significant advances in the fabrication process leading to grain size reduction have been made in order to improve fatigue properties of aircraft turbine discs. Indeed, a change in particle size affects the initiation mode of fatigue cracks as well as the fatigue life of the material. The present study aims to investigate the fatigue behavior of a newly developed nickel-based superalloy under biaxial-planar loading. Low Cycle Fatigue (LCF) tests are performed at different stress ratios so as to study the influence of the multiaxial stress state on the fatigue life of the material. Full-field displacement and strain measurements as well as crack initiation detection are obtained using Digital Image Correlation (DIC) techniques. The aim of this presentation is first to provide an in-depth description of both the experimental set-up and protocol: the multiaxial testing machine, the specific design of the cruciform specimen and performances of the DIC code are introduced. Second, results for sixteen specimens related to different load ratios are presented. Crack detection, strain amplitude and number of cycles to crack initiation vs. triaxial stress ratio for each loading case are given. Third, from fractographic investigations by scanning electron microscopy it is found that the mechanism of fatigue crack initiation does not depend on the triaxial stress ratio and that most fatigue cracks initiate from subsurface carbides.

Keywords: cruciform specimen, multiaxial fatigue, nickel-based superalloy

Procedia PDF Downloads 275

Authors: Manish Pandey, Gurinderjit Kaur, Meenu Talwar, Sachin Chauhan, Jagbir Gill

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Health-care management systems are a unit of nice connection as a result of the supply a straightforward and fast management of all aspects relating to a patient, not essentially medical. What is more, there are unit additional and additional cases of pathologies during which diagnosing and treatment may be solely allotted by victimization medical imaging techniques. With associate ever-increasing prevalence, medical pictures area unit directly acquired in or regenerate into digital type, for his or her storage additionally as sequent retrieval and process. Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Forecasting may be a prediction of what's going to occur within the future, associated it's an unsure method. Owing to the uncertainty, the accuracy of a forecast is as vital because the outcome foretold by foretelling the freelance variables. A forecast management should be wont to establish if the accuracy of the forecast is within satisfactory limits. Fuzzy regression strategies have normally been wont to develop shopper preferences models that correlate the engineering characteristics with shopper preferences relating to a replacement product; the patron preference models offer a platform, wherever by product developers will decide the engineering characteristics so as to satisfy shopper preferences before developing the merchandise. Recent analysis shows that these fuzzy regression strategies area units normally will not to model client preferences. We tend to propose a Testing the strength of Exponential Regression Model over regression toward the mean Model.

Keywords: health-care management systems, fuzzy regression, data mining, forecasting, fuzzy membership function

Procedia PDF Downloads 261

Authors: Didem Venus Yildiz, Murat Hocaoglu, Murat Dursun, Taner Akkan

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The shoulder joint is a more complex structure than the hip or knee joints. In addition to the overall complexity of the shoulder joint, two different factors influence the insufficient outcome of shoulder replacement: the shoulder prosthesis design is far from fully developed and it is difficult to place these shoulder prosthesis due to shoulder anatomy. The glenohumeral joint is the most complex joint of the human shoulder. There are various treatments for shoulder failures such as total shoulder arthroplasty, reverse total shoulder arthroplasty. Due to its reverse design than normal shoulder anatomy, reverse total shoulder arthroplasty has different physiological and biomechanical properties. Post-operative achievement of this arthroplasty is depend on improved design of reverse total shoulder prosthesis. Designation achievement can be increased by several biomechanical and computational analysis. In this study, data of human both shoulders with right side fracture was collected by 3D Computer Tomography (CT) machine in dicom format. This data transferred to 3D medical image processing software (Mimics Materilise, Leuven, Belgium) to reconstruct patient’s left and right shoulders’ bones geometry. Provided 3D geometry model of the fractured shoulder was used to constitute of reverse total shoulder prosthesis by 3-matic software. Finite element (FE) analysis was conducted for comparison of intact shoulder and prosthetic shoulder in terms of stress distribution and displacements. Body weight physiological reaction force of 800 N loads was applied. Resultant values of FE analysis was compared for both shoulders. The analysis of the performance of the reverse shoulder prosthesis could enhance the knowledge of the prosthetic design.

Keywords: reverse shoulder prosthesis, biomechanics, finite element analysis, 3D printing

Procedia PDF Downloads 141

Authors: Matthias Stange

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In the early modern period, a different image of man began to prevail in Europe. The focus was on the self-determined human being and his abilities. At first, these developments could be seen in Italian painting and architecture, which again oriented itself to the concepts and forms of antiquity. For example, through the discovery of perspective representation by Brunelleschi or later the orthogonal projection by Alberti, after the ancient knowledge of optics had been forgotten in the Middle Ages. The understanding of reality in the Middle Ages was not focused on the sensually perceptible world but was determined by ecclesiastical dogmas. The empirical part of this study examines the rediscovery and development of perspective. With the paradigm of antiquity, the figure of the architect was also recognised again - the cultural man trained theoretically and practically in numerous subjects, as Vitruvius describes him. In this context, the role of the architect, the influence on the painting of the Quattrocento as well as the influence on architectural representation in the Baroque period are examined. Baroque is commonly associated with the idea of illusionistic appearance as opposed to the tangible reality presented in the Renaissance. The study has shown that the central perspective projection developed by Filippo Brunelleschi enabled another understanding of seeing and the dissemination of painted images. Brunelleschi's development made it possible to understand the sight of nature as a reflection of what is presented to the viewer's eye. Alberti later shortened Brunelleschi's central perspective representation for practical use in painting. In early modern Italian architecture and painting, these developments apparently supported each other. The pictorial representation of architecture initially served the development of an art form before it became established in building practice itself.

Keywords: Alberti, Brunelleschi, central perspective projection, orthogonal projection, quattrocento, baroque

Procedia PDF Downloads 67

Authors: V. G. Cappellesso, D. M. G. da Silva, J. A. Arndt, N. dos Santos Petry, A. B. Masuero, D. C. C. Dal Molin

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Concrete elements are subject to cracking, which can be an access point for deleterious agents that can trigger pathological manifestations reducing the service life of these structures. Finding ways to minimize or eliminate the effects of this aggressive agents’ penetration, such as the sealing of these cracks, is a manner of contributing to the durability of these structures. The cementitious self-healing phenomenon can be classified in two different processes. The autogenous self-healing that can be defined as a natural process in which the sealing of this cracks occurs without the stimulation of external agents, meaning, without different materials being added to the mixture, while on the other hand, the autonomous seal-healing phenomenon depends on the insertion of a specific engineered material added to the cement matrix in order to promote its recovery. This work aims to evaluate the autogenous self-healing of concretes produced with different water/cement ratios and exposed to wet/dry cycles, considering two ages of crack openings, 3 days and 28 days. The self-healing phenomenon was evaluated using two techniques: crack healing measurement using ultrasonic waves and image analysis performed with an optical microscope. It is possible to observe that by both methods, it possible to observe the self-healing phenomenon of the cracks. For young ages of crack openings and lower water/cement ratios, the self-healing capacity is higher when compared to advanced ages of crack openings and higher water/cement ratios. Regardless of the crack opening age, these concretes were found to stabilize the self-healing processes after 80 days or 90 days.

Keywords: sealf-healing, autogenous, water/cement ratio, curing cycles, test methods

Procedia PDF Downloads 142

Authors: Kyle Yatich Terik, Collins Oduor

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The current healthcare system has made healthcare more accessible and efficient by the use of information technology through the implementation of computer algorithms that generate menus based on the diagnosis. While many systems just like these have been created over the years, their main objective is to help healthy individuals calculate their calorie intake and assist them by providing food selections based on a pre-specified calorie. That application has been proven to be useful in some ways, and they are not suitable for monitoring, planning, and managing hospital patients, especially that critical condition their dietary needs. The system also addresses a number of objectives, such as; the main objective is to be able to design, develop and implement an efficient, user-friendly as well as and interactive dietary management system. The specific design development objectives include developing a system that will facilitate a monitoring feature for users using graphs, developing a system that will provide system-generated reports to the users, dietitians, and system admins, design a system that allows users to measure their BMI (Body Mass Index), the system will also provide food template feature that will guide the user on a balanced diet plan. In order to develop the system, further research was carried out in Kenya, Nairobi County, using online questionnaires being the preferred research design approach. From the 44 respondents, one could create discussions such as the major challenges encountered from the manual dietary system, which include no easily accessible information of the calorie intake for food products, expensive to physically visit a dietitian to create a tailored diet plan. Conclusively, the system has the potential of improving the quality of life of people as a whole by providing a standard for healthy living and allowing individuals to have readily available knowledge through food templates that will guide people and allow users to create their own diet plans that consist of a balanced diet.

Keywords: DMS, dietitian, patient, administrator

Procedia PDF Downloads 142

Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

Procedia PDF Downloads 99

Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

Procedia PDF Downloads 52

Authors: Vaibhav Barve

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Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

Procedia PDF Downloads 277

Authors: Erhong Sun, Xuchun Ye

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Technophobia, namely the fear or dislike of modern advanced technologies, plays a central role in age-related digital divides and is considered a new risk factor for older adults, which can affect the daily lives of people through low adherence to digital living. Indeed, there is considerable heterogeneity in the group of older adults who feel technophobia. Therefore, the aim of this study was to identify different technophobia typologies of older people and to examine their associations with the subjective age factor. A sample of 704 retired elderly over the age of 55 was recruited in China. Technophobia and subjective age were assessed with a questionnaire, respectively. Latent profile analysis was used to identify technophobia subgroups, using three dimensions including techno-anxiety, techno-paranoia, and privacy concerns as indicators. The association between the identified technophobia subgroups and subjective age was explored. In summary, four different technophobia typologies were identified among older adults in China. Combined with an investigation of personal background characteristics and subjective age, it draws a more nuanced image of the technophobia phenome among older adults in China. First, not all older adults suffer from technophobia, with about half of the elderly subjects belonging to the profiles of “Low-technophobia” and “Medium-technophobia.” Second, privacy concern plays an important role in the classification of technophobia among older adults. Third, subjective age might be a protective factor for technophobia in older adults. Although the causal direction between identified technophobia typologies and subjective age remains uncertain, our suggests that future interventions should better focus on subjective age by breaking the age stereotype of technology to reduce the negative effect of technophobia on older. Future development of this research will involve extensive investigation of the detailed impact of technophobia in senior populations, measurement of the negative outcomes, as well as formulation of innovative educational and clinical pathways.

Keywords: technophobia, older adults, latent profile analysis, subjective age

Procedia PDF Downloads 59

Authors: Quoc Hung Le Pham

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Human beings are motivated to satisfy the physiological needs and psychological needs. In the fundamental needs, bodily excretion is the most basic one, while physiological excretion refers to the final products produced in the process of discharging the body. This physiological process is a common human phenomenon. For instance, bodily secretion is totally natural, but people of various nationalities through the times avoid saying it directly. Terms like ‘shit’ are often negatively regarded as dirty, smelly and vulgar; it will lead people to negative thinking. In fact, it is in the psychology of human beings to avoid such unsightly terms. Especially in social situations where you have to take care of your image, and you have to release. The best way to solve this is to approach the use of euphemism. People prefer to say it as ‘answering nature's call’ or ‘to pass a motion’ instead. Chinese and Vietnamese nations are referring to use euphemisms to replace bodily secretions, so this research will take this phenomenon as the object aims to explore the similarities and dissimilarities between two languages euphemism. The basic of the niche of this paper is human physiological phenomenon excretion. As the preliminary results show, in expressing bodily secretions the deeply impacting factor is language and cultural factors. On language factor terms, two languages are using assonance to replace human nature discharge, whilst the dissimilarities are metonymy, loan word and personification. On culture factor terms, the convergences are metonymy and application of the semantically-contrary-word-euphemism, whilst the difference is Chinese euphemism using allusion but Vietnamese euphemism does not.

Keywords: cultural factors, euphemism, human needs, language factors

Procedia PDF Downloads 278

Authors: Kheireddine El-Boubbou

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Drug delivery to target human acute myeloid leukemia (AML) using a nanoparticulate chemotherapeutic formulation that can deliver drugs selectively to AML cancer is hugely needed. In this work, we report the development of a nanoformulation made of polymeric-stabilized multifunctional magnetic iron oxide nanoparticles (PMNP) loaded with the anticancer drug Doxorubicin (Dox) as a promising drug carrier to treat AML. Dox@PMNP conjugates simultaneously exhibited high drug content, maximized fluorescence, and excellent release properties. Nanoparticulate uptake and cell death following addition of Dox@PMNPs were then evaluated in different types of human AML target cells, as well as on normal human cells. While the unloaded MNPs were not toxic to any of the cells, Dox@PMNPs were found to be highly toxic to the different AML cell lines, albeit at different inhibitory concentrations (IC₅₀ values), but showed very little toxicity towards the normal cells. In comparison, free Dox showed significant potency concurrently to all the cell lines, suggesting huge potentials for the use of Dox@PMNPs as selective AML anticancer cargos. Live confocal imaging, fluorescence and electron microscopy confirmed that Dox is indeed delivered to the nucleus in relatively short periods of time, causing apoptotic cell death. Importantly, this targeted payload may potentially enhance the effectiveness of the drug in AML patients and may further allow physicians to image leukemic cells exposed to Dox@PMNPs using MRI.

Keywords: magnetic nanoparticles, drug delivery, acute myeloid leukemia, iron oxide, cancer nanotherapy

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Authors: Driss Haddouche

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Pastoral crisis in Algeria has its origins in rangeland degradation which are the main factor in any activity in the steppe zones. Indeed, faced with the increasing human and animal population on a living space smaller and smaller, there is an overuse of what remains of the steppe range lands, consequently the not sustainability of biomass production. Knowing the amount of biomass available, the practice of grazing options, taking into account the use of "Use Factor" factor remains an essential method for managing pastoral resources. This factor has three options: at 40% Conservative pasture; at 60 % the beginning of overgrazing; at 80% destructive grazing. Accessibility on the pasture is based on our field observations of a type any flock along a grazing cycle. The main purpose of these observations is to highlight the speed of herd grazing situation. Several individuals from the herd were timed to arrive at an average duration of about 5 seconds to move between two tufts of grass, separated by a distance of one meter. This gives a rate of 5 s/m (0.72 km/h) flat. This speed varies depending on the angle of the slope. Knowing the speed and slope of each pixel of the study area, given by the digital elevation model of Spot Image (MNE) and whose pitch is 15 meters, a map of pasture according to the distances is generated. Knowing the stocking and biomass available, the examination of the common Mécheria at regular distances (8.64 km or 12 hours of grazing, 17.28 km or 24 hours of grazing and 25.92 Km or 36 hours of grazing), offers three different options (conservation grazing resource: utilization at 40%; overgrazing statements for use at 60% and grazing destructive for use by more than 80%) for each distance traveled by sheep from the starting point is the town of Mécheria.

Keywords: pastoral crisis, biomass, animal charge, use factor, Algeria

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Authors: Zahra Asgharpour, Eric Renteria, Sebastian De Boodt

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There is a growing trend towards personalization of medical care, as evidenced by the emphasis on outcomes based medicine, the latest developments in CT and MR imaging and personalized treatment in a variety of surgical disciplines. 3D Printing has been introduced and applied in the medical field since 2000. The first applications were in the field of dental implants and custom prosthetics. According to recent publications, 3D printing in the medical field has been used in a wide range of applications which can be organized into several categories including implants, prosthetics, anatomical models and tissue bioprinting. Some of these categories are still in their infancy stage of the concept of proof while others are in application phase such as the design and manufacturing of customized implants and prosthesis. The approach of 3D printing in this category has been successfully used in the health care sector to make both standard and complex implants within a reasonable amount of time. In this study, some of the clinical applications of 3D printing in design and manufacturing of a patient-specific hip implant would be explained. In cases where patients have complex bone geometries or are undergoing a complex revision on hip replacement, the traditional surgical methods are not efficient, and hence these patients require patient-specific approaches. There are major advantages in using this new technology for medical applications, however, in order to get this technology widely accepted in medical device industry, there is a need for gaining more acceptance from the medical device regulatory offices. This is a challenge that is moving onward and will help the technology find its way at the end as an accepted manufacturing method for medical device industry in an international scale. The discussion will conclude with some examples describing the future directions of 3D Medical Printing.

Keywords: CT/MRI, image processing, 3D printing, medical devices, patient specific implants

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Authors: Chen-En Lin, Lih-Rou Rau, Jiunn-Woei Liaw, Shiao-Wen Tsai

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The unique optical properties of plasmon resonance metallic particles have attracted considerable applications in the fields of physics, chemistry and biology. Metal-Enhanced Fluorescence (MEF) effect is one of the useful applications. MEF effect stated that fluorescence intensity can be quenched or be enhanced depending on the distance between fluorophores and the metal nanoparticles. Silver nanoparticles have used widely in antibacterial studies. However, the major limitation for silver nanoparticles (AgNPs) in biomedical application is well-known cytotoxicity on cells. There were numerous literatures have been devoted to overcome the disadvantage. The aim of the study is to evaluate the cytotoxicity and biocompatibility of silica coated AgNPs against NIH-3T3 cells. The results were shown that NIH-3T3 cells started to detach, shrink, become rounded and finally be irregular in shape after 24 h of exposure at 10 µg/ml AgNPs. Besides, compared with untreated cells, the cell viability significantly decreased to 60% and 40% which were exposed to 10 µg/ml and 20 µg/ml AgNPs respectively. The result was consistent with previously reported findings that AgNPs induced cytotoxicity was concentration dependent. However, the morphology and cell viability of cells appeared similar to the control group when exposed to 20 µg/ml of silica coated AgNPs. We further utilized the dark-field hyperspectral imaging system to analysis the optical properties of the intracellular nanoparticles. The image displayed that the red shift of the surface plasmonic resonances band of the enclosed AgNPs further confirms the agglomerate of the AgNPs rather than their distribution in cytoplasm. In conclusion, the study demonstrated the silica coated of AgNPs showed well biocompatibility and significant lower cytotoxicity compared with bare AgNPs.

Keywords: silver nanoparticles, silica, cell viability, morphology

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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 149

Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

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Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Hsiu-Chuan Hsueh, Kuei-Feng Shen Jr., Chia-Ling Chao, Hui-Chuan Pan

Abstract:

This article described a 75 years old woman who has suffered from intestinal perforation and accepted surgery with temporary enterostomy, the operation makes her depressed, refused relatives and friend's care, facing low willingness to participate in various activities due to fear of changing body appearance caused by surgery and leave enterostomy. The author collected information through observation talks, physical evaluation, and medical records during the period of care from November 14 to November 30, 2016, we used the four aspects of physiology, psychology, society and spirituality as a whole sexual assessment to establish the nursing problems of patient, included of acute pain, disturbance of body image,coping ineffective individual. For patient care issues, to encouraged case to express their inner feelings and take part in self-care programs through providing good therapeutic interpersonal relationships with their families. However, it provided clear information about the disease and follow-up treatment plan, give compliments in a timely manner, enhanced self-confidence of individual cases and their motivation to participate in self-care of stoma, further face the disease in a positive manner. At the same time, cross-section team care model and individual care measures were developed to enhance the care skills after returning home and at the same time assist the individual in facing the psychological impact caused by stoma. Hope to provide this experience, as a reference for the future care of the disease.

Keywords: enterostomy, intestinal perforation, nursing experience, ostomy

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Authors: Jin-Hui Lee, Myung-Min Oh

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The application of temporary abiotic stresses before crop harvest is a potential strategy to enhance phytochemical content. The objective of this study was to determine the effect of various UV-A LED lights on the growth and content of bioactive compounds in kale (Brassica oleracea var. acephala). Fourteen-day-old kale seedlings were cultivated in a plant factory with artificial lighting (air temperature of 20℃, relative humidity of 60%, photosynthesis photon flux density (PPFD) of 125 µmol·m⁻²·s⁻¹) for 3 weeks. Kale plants were irradiated by four types of UV-A LEDs (peak wavelength; 365, 375, 385, and 395 nm) with 30 W/m² for 7 days. As a result, image chlorophyll fluorescence (Fv/Fm) value of kale leaves was lower as the UV-A LEDs peak wavelength was shorter. Fresh and dry weights of shoots and roots of kale plants were significantly higher in the plants under UV-A than the control at 7 days of treatment. In particular, the growth was significantly increased with a longer peak wavelength of the UV-A LEDs. The results of leaf area and specific leaf weight showed a similar pattern with those of growth characteristics. Chlorophyll content was highest in kale leaves subjected to UV-A LEDs with the peak wavelength of 395 nm at 3 days of treatment compared with the control. Total phenolic contents of UV-A LEDs with the peak wavelength of 395 nm at 5 and 6 days of treatment were 44% and 47% higher than those of the control, respectively. Antioxidant capacity showed almost the same pattern as the results of total phenol content. The activity of phenylalanine ammonia-lyase was approximately 11% and 8% higher in the UV-A LEDs with the peak wavelength of 395 nm compared to the control at 5 and 6 days of treatment, respectively. Our results imply that the UV-A LEDs with relative longer peak wavelength were effective to improve growth as well as the content of bioactive compounds of kale plants.

Keywords: bioactive compounds, growth, Kale, UV-A LEDs

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Authors: Shailly Kumar

Abstract:

Gender is a social construction resulting in defining roles and responsibilities to carried out according to masculine and feminine traits. The main aim of the study was to explore gender awareness among college going students of Delhi. The objectives of studies were to find out (i) the understanding of term gender and roles and responsibilities associated with male and female as masculine and feminine traits in our society. (ii)Gender images representing the attributes and characteristics attached to particular gender. (iii) Gender discrimination prevailing among girls and boys in our society. (iv)Gender stereotypes resulting in gendering with respect to religion, culture, family and media. The sample of study consisted of 100 undergraduate college girl students. The findings of study stated that the students had this understanding that sex is a natural phenomenon and gender is socially constructed. Gender defines the roles and responsibilities among two sexes. On a gender image students concluded that males are represented as a powerful members of society showing physical strength and violence, force and society gave the power to men oppress and subjugate women in society that's why women are treated inferior and given secondary position in society. On gender discrimination, girl students stated that they faced discrimination at all level such as family, media ,education, workplace etc .There is strong prevailing gender stereotypes among girls and boys with respect to religious practices, choice of career ,preference of child etc. This study concluded that students were aware of gendered practices in various domains of life. The study helped to interpret the notions and perceptions of students towards gendering of social spaces and in their lives.

Keywords: gender, gender awareness, gender role, masculinity and feminity

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Authors: Vijaykumar M. Baragi, Ramtilak Gattu, Gabriela Trifan, John L. Woodard, K. Meyers, Tim S. Halstead, Eric Hipple, Ewart Mark Haacke, Randall R. Benson

Abstract:

NFL players, by virtue of their exposure to repetitive head injury, are at least twice as likely to develop Alzheimer's disease (AD) and dementia as the general population. Early recognition and intervention prior to onset of clinical symptoms could potentially avert/delay the long-term consequences of these diseases. Since AD is thought to have a long preclinical incubation period, the aim of the current research was to determine whether former NFL players, referred to a depression center, showed evidence of incipient dementia in their structural imaging prior to diagnosis of dementia. Thus, to identify neuroimaging markers of AD, against which former NFL players would be compared, we conducted a comprehensive volumetric analysis using a cohort of early stage AD patients (ADNI) to produce a set of brain regions demonstrating sensitivity to early AD pathology (i.e., the “AD fingerprint”). A cohort of 46 former NFL players’ brain MRIs were then interrogated using the AD fingerprint. Brain scans were done using a T1-weighted MPRAGE sequence. The Free Surfer image analysis suite (version 6.0) was used to obtain the volumetric and cortical thickness data. A total of 55 brain regions demonstrated significant atrophy or ex vacuo dilatation bilaterally in AD patients vs. healthy controls. Of the 46 former NFL players, 19 (41%) demonstrated a greater than expected number of atrophied/dilated AD regions when compared with age-matched controls, presumably reflecting AD pathology.

Keywords: alzheimers, neuroimaging biomarkers, traumatic brain injury, free surfer, ADNI

Procedia PDF Downloads 142

Authors: O’Day Luke

Abstract:

Purpose: To validate virtual non-contrast reconstructions generated from dual-layer spectral computed tomography (DL-CT) data as an alternative for the acquisition of a dedicated true non-contrast dataset during multiphase contrast studies. Material and methods: Thirty-three patients underwent a routine multiphase clinical CT examination, using Dual-Layer Spectral CT, from March to August 2021. True non-contrast (TNC) and virtual non-contrast (VNC) datasets, generated from both portal venous and arterial phase imaging were evaluated. For every patient in both true and virtual non-contrast datasets, a region-of-interest (ROI) was defined in aorta, liver, fluid (i.e. gallbladder, urinary bladder), kidney, muscle, fat and spongious bone, resulting in 693 ROIs. Differences in attenuation for VNC and TNV images were compared, both separately and combined. Consistency between VNC reconstructions obtained from the arterial and portal venous phase was evaluated. Results: Comparison of CT density (HU) on the VNC and TNC images showed a high correlation. The mean difference between TNC and VNC images (excluding bone results) was 5.5 ± 9.1 HU and > 90% of all comparisons showed a difference of less than 15 HU. For all tissues but spongious bone, the mean absolute difference between TNC and VNC images was below 10 HU. VNC images derived from the arterial and the portal venous phase showed a good correlation in most tissue types. The aortic attenuation was somewhat dependent however on which dataset was used for reconstruction. Bone evaluation with VNC datasets continues to be a problem, as spectral CT algorithms are currently poor in differentiating bone and iodine. Conclusion: Given the increasing availability of DL-CT and proven accuracy of virtual non-contrast processing, VNC is a promising tool for generating additional data during routine contrast-enhanced studies. This study shows the utility of virtual non-contrast scans as an alternative for true non-contrast studies during multiphase CT, with potential for dose reduction, without loss of diagnostic information.

Keywords: dual-layer spectral computed tomography, virtual non-contrast, true non-contrast, clinical comparison

Procedia PDF Downloads 127