Search results for: medical image processing

Authors: Rao Muhammad Afzal Khan

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Nano technology is emerging as a useful technology in nearly all areas of Science and Technology. Its role in medical imaging is attracting the researchers towards existing and new imaging modalities and techniques. This presentation gives an overview of the development of the work done throughout the world. Furthermore, it lays an idea into the scope of the future use of this technology for diagnosing different diseases. A comparative analysis has also been discussed with an emphasis to detect diseases, in general, and cancer, in particular.

Keywords: medical imaging, cancer detection, diagnosis, nano-imaging, nanotechnology

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Authors: Kshitij Sawke, Pradnyavant Kamble, Shrikant Patil

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The study investigates the effect on wear rate of laser clad of cast iron with silicon carbide. Metal components fail their desired use because they wear, which causes them to lose their functionality. The laser has been used as a heating source to create a melt pool over the surface of cast iron, and then a layer of hard silicon carbide is deposited. Various combinations of power and feed rate of laser have experimented. A suitable range of laser processing parameters was identified. Wear resistance and wear rate properties were evaluated and the result showed that the wear resistance of the laser treated samples was exceptional to that of the untreated samples.

Keywords: laser clad, processing parameters, wear rate, wear resistance

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Joanna Dzieńdziora

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Lobbying is an instrument of influence in various decision-making processes. It is also the underestimated issue as a research problem. The lack of research on the modern lobbyist competencies is the most crucial element. The paper presents attempts of finding answers to the following questions: Who should run the lobbying activity? What competencies should a lobbyist possess in order to implement lobbying activities effectively? Searching for answers for the mentioned above questions requires positioning the opportunity to change the image of lobbying in the area of competencies of entities that provide lobbying activities. The aim of the paper is presenting the lobbyist competencies profile in the framework of his professional role. The essence of lobbying activity and its significance in the modern economy as well as areas, the scope of lobbying activities, diagnosis of a modern lobbyist’s competences, lobbyist’s competencies profile that is focused on the professionalization of the lobbying activity, will have been presented in this paper. Indicated research tasks let emerge lobbyist’s competencies in the way that allows identifying and elaborating the lobbyist competencies profile. The profile lets improve lobbying activities. Its elaboration is based on the author’s research results analysis. Taking into consideration the shortages within the theory and research on the lobbying activity, the implementation of this research enables to fill the cognitive gap existing in the theory of management sciences.

Keywords: competencies, competencies profile, lobbying, lobbyist

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Authors: Lie-Fen Lin, Tzu-Hsuan Lin, Ching-Heng Lin

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Hospice care for terminally ill patients provides not only a better quality of life but also cost-saving benefits. However, the utilization of home-based hospice care (HBH care) remains low even for countries covered by National Health Insurance (NHI) programs in Taiwan. In the current commercial insurance policy, only hospital-based hospice benefits were covered. It may have an influence on the insureds chosen to receive end-of-life care in a hospitalized manner. Thus, how to propose a feasible method to advocate HBH care utilization rate of public health policies is an important issue. A total of 130,219 cancer decedents in the year 2011-2013 from the National Health Insurance Research Database (NHIRD) in Taiwan were included in this study. By adding a day volume pays benefits of HBH care as a commercial insurance rider, will provide alternative benefits for the insureds. A multiple-state Markov chain model was incorporated to estimate the transition intensities of patients in different states at the end of their lives (Non-hospice, HBH, hospital-based hospice), and the premiums were estimated. HBH care insurance benefits provide financial support and reduce the burden of care for patients. The rate-making of this product is very sensitive while the utilization rate is rising, especially for high ages. The proposed HBH care insurance is a feasible way to reduce the financial burden, enhance the care quality and family satisfaction of insureds. Meanwhile, insurance companies can participate in advocating a good medical policy to enhance the social image. In addition, the medical costs of NHI can reduce effectively.

Keywords: home-based hospice care, commercial insurance, Markov chain model, the day volume pays

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Authors: Mahdi Farajzadeh Ajirlou

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Prior evidence of normal cardiac anatomy is desirable to relieve the anxiety of cases with a family history of congenital heart disease or to offer the option of early gestation termination or close follow-up should a cardiac anomaly be proved. Fetal heart discovery plays an important part in the opinion of the fetus, and it can reflect the fetal heart function of the fetus, which is regulated by the central nervous system. Acquisition of ventricular volume and inflow data would be useful to quantify more valve regurgitation and ventricular function to determine the degree of cardiovascular concession in fetal conditions at threat for hydrops fetalis. This study discusses imaging the fetal heart with transvaginal ultrasound, Doppler ultrasound, three-dimensional ultrasound (3DUS) and four-dimensional (4D) ultrasound, spatiotemporal image correlation (STIC), glamorous resonance imaging and cardiac catheterization. Doppler ultrasound (DUS) image is a kind of real- time image with a better imaging effect on blood vessels and soft tissues. DUS imaging can observe the shape of the fetus, but it cannot show whether the fetus is hypoxic or distressed. Spatiotemporal image correlation (STIC) enables the acquisition of a volume of data concomitant with the beating heart. The automated volume accession is made possible by the array in the transducer performing a slow single reach, recording a single 3D data set conforming to numerous 2D frames one behind the other. The volume accession can be done in a stationary 3D, either online 4D (direct volume scan, live 3D ultrasound or a so-called 4D (3D/ 4D)), or either spatiotemporal image correlation-STIC (off-line 4D, which is a circular volume check-up). Fetal cardiovascular MRI would appear to be an ideal approach to the noninvasive disquisition of the impact of abnormal cardiovascular hemodynamics on antenatal brain growth and development. Still, there are practical limitations to the use of conventional MRI for fetal cardiovascular assessment, including the small size and high heart rate of the mortal fetus, the lack of conventional cardiac gating styles to attend data accession, and the implicit corruption of MRI data due to motherly respiration and unpredictable fetal movements. Fetal cardiac MRI has the implicit to complement ultrasound in detecting cardiovascular deformations and extracardiac lesions. Fetal cardiac intervention (FCI), minimally invasive catheter interventions, is a new and evolving fashion that allows for in-utero treatment of a subset of severe forms of congenital heart deficiency. In special cases, it may be possible to modify the natural history of congenital heart disorders. It's entirely possible that future generations will ‘repair’ congenital heart deficiency in utero using nanotechnologies or remote computer-guided micro-robots that work in the cellular layer.

Keywords: fetal, cardiac MRI, ultrasound, 3D, 4D, heart disease, invasive, noninvasive, catheter

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Authors: Murat Demir Aydin, Elanur Celebi

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Adhesively bonded joints are used as an alternative to traditional joining methods due to the important advantages they provide. The most important consideration in the use of adhesively bonded joints is that these joints have appropriate requirements for their use in terms of safety. In order to ensure control of this condition, damage analysis of the adhesively bonded joints should be performed by determining the mechanical properties of the adhesives. When the literature is investigated; it is generally seen that the mechanical properties of adhesives are determined by traditional measurement methods. In this study, to determine the mechanical properties of adhesives, the Digital Image Correlation (DIC) method, which can be an alternative to traditional measurement methods, has been used. The DIC method is a new optical measurement method which is used to determine the parameters of displacement and strain in an appropriate and correct way. In this study, tensile tests of Thick Adherent Shear Test (TAST) samples formed using DP410 liquid structural adhesive and steel materials and bulk tensile specimens formed using and DP410 liquid structural adhesive was performed. The displacement and strain values of the samples were determined by DIC method and the shear stress-strain curves of the adhesive for TAST specimens and the tensile strain curves of the bulk adhesive specimens were obtained. Various methods such as numerical methods are required as conventional measurement methods (strain gauge, mechanic extensometer, etc.) are not sufficient in determining the strain and displacement values of the very thin adhesive layer such as TAST samples. As a result, the DIC method removes these requirements and easily achieves displacement measurements with sufficient accuracy.

Keywords: structural adhesive, adhesively bonded joints, digital image correlation, thick adhered shear test (TAST)

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Jou-An Chung

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The purpose of this study is to investigate the verb bias of the Mandarin verbs in children’s reading materials and provide the criteria for categorization. Verb bias varies cross-linguistically. As Mandarin and English are typological different, this study hopes to shed light on Mandarin verb bias with the use of corpus and provide thorough and detailed criteria for analysis. Moreover, this study focuses on children’s reading materials since it is a significant issue in understanding children’s sentence processing. Therefore, investigating verb bias of Mandarin verbs in children’s reading materials is also an important issue and can provide further insights into children’s sentence processing. The small corpus is built up for this study. The corpus consists of the collection of school textbooks and Mandarin Daily News for children. The files are then segmented and POS tagged by JiebaR (Chinese segmentation with R). For the ease of analysis, the one-word character verbs and intransitive verbs are excluded beforehand. The total of 20 high frequency verbs are hand-coded and are further categorized into one of the three types, namely DO type, SC type and other category. If the frequency of taking Other Type exceeds the threshold of 25%, the verb is excluded from the study. The results show that 10 verbs are direct object bias verbs, and six verbs are sentential complement bias verbs. The paired T-test was done to assure the statistical significance (p = 0.0001062 for DO bias verb, p=0.001149 for SC bias verb). The result has shown that in children’s reading materials, the DO biased verbs are used more than the SC bias verbs since the simplest structure of sentences is easier for children’s sentence comprehension or processing. In sum, this study not only discussed verb bias in child's reading materials but also provided basic coding criteria for verb bias analysis in Mandarin and underscored the role of context. Sentences are easier for children’s sentence comprehension or processing. In sum, this study not only discussed verb bias in child corpus, but also provided basic coding criteria for verb bias analysis in Mandarin and underscored the role of context.

Keywords: corpus linguistics, verb bias, child language, psycholinguistics

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the nature of semantic processing in Chinese. Language and cognition related to the issue of aging are examined from the perspective of picture naming and category fluency tasks. Twenty Chinese-speaking adults (ranging from 25 to 45 years old) and twenty Chinese-speaking seniors (ranging from 65 to 75 years old) in Taiwan participated in this study. Each of them individually completed two tasks: a picture naming task and a category fluency task. Instruments for the naming task were sixty black-and-white pictures: thirty-five object and twenty-five action pictures. Category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). Participants were asked to report as many items within a category as possible in one minute. Scores of action fluency and of object fluency were a summation of correct responses in these two categories. Category effects (actions vs. objects) and age effects were examined in these tasks. Objects were further divided into two major types: living objects and non-living objects. Actions were also categorized into two major types: action verbs and process verbs. Reaction time to each picture/question was additionally calculated and analyzed. Results of the category fluency task indicated that the content of information in Chinese seniors was comparatively deteriorated, thus producing smaller number of semantic-lexical items. Significant group difference was also found in the results of reaction time. Category Effect was significant for both Chinese adults and seniors in the semantic fluency task. Findings in the present study helped characterize the nature of semantic processing in Chinese-speaking adults and seniors and contributed to the issue of language and aging.

Keywords: semantic processing, aging, Chinese, category effects

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Authors: Loveline Ambo Angwah

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The processing of cassava from tubers or roots into food using crude and rudimentary method (hand peeling, grating, frying and to sun drying) is a very cumbersome and difficult process. The crude methods are time consuming and labour intensive. While on the other hand, modern processing method, that is using machines to perform the various processes as washing, peeling, grinding, oven drying, fermentation and frying is easier, less time consuming, and less labour intensive. Rudimentarily, cassava roots are processed into numerous products and utilized in various ways according to local customs and preferences. For the people of Ngwo village, cassava is transformed locally into flour or powder form called ‘cumcum’. It is also sucked into water to give a kind of food call ‘water fufu’ and fried to give ‘garri’. The leaves are consumed as vegetables. Added to these, its relative high yields; ability to stay underground after maturity for long periods give cassava considerable advantage as a commodity that is being used by poor rural folks in the community, to fight poverty. It plays a major role in efforts to alleviate the food crisis because of its efficient production of food energy, year-round availability, tolerance to extreme stress conditions, and suitability to present farming and food systems in Africa. Improvement of cassava processing and utilization techniques would greatly increase labor efficiency, incomes, and living standards of cassava farmers and the rural poor, as well as enhance the-shelf life of products, facilitate their transportation, increase marketing opportunities, and help improve human and livestock nutrition. This paper presents a general overview of crude ways in cassava processing and utilization methods now used by subsistence and small-scale farmers in Ngwo village of the North West region in Cameroon, and examine the opportunities of improving processing technologies. Cassava needs processing because the roots cannot be stored for long because they rot within 3-4 days of harvest. They are bulky with about 70% moisture content, and therefore transportation of the tubers to markets is difficult and expensive. The roots and leaves contain varying amounts of cyanide which is toxic to humans and animals, while the raw cassava roots and uncooked leaves are not palatable. Therefore, cassava must be processed into various forms in order to increase the shelf life of the products, facilitate transportation and marketing, reduce cyanide content and improve palatability.

Keywords: cassava roots, crude ways, food system, poverty

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Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Hala Kama Ahmed Rashwan

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Ensuring the patient safety especially at intensive care units and those exposed to hospital tools and equipment is one of the most important challenges facing healthcare today. Outbreak of food poisoning as a result of food-borne pathogens has been reported in many hospitals and care homes all over the world due to hospital meals. Patient safety of eating hospital meals is a fundamental principle of healthcare; it is new healthcare disciplines that assure the food raw materials, food storage, meals processing, and control of kitchen errors that often lead to adverse healthcare events. The aim of this article is to promote any hospital in attaining the hygienic practices and better quality system during processing of the ready-to- eat meals for intensive care units patients according to the WHO safety guidelines.

Keywords: hospitals, meals, safety, intensive care

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Authors: Andrey Norov

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Based on the generalization of scientific and production experience and the latest developments of JSC “NIUIF”, the oldest (founded in September 1919) and the only Russian research institute for phosphorus-containing fertilizers, this paper shows the factors that determine the reasonable choice of a method for processing phosphate raw materials into complex fertilizers. These factors primarily include the composition of phosphate raw materials and the impurities contained in it, as well as some parameters of the process mode, wastelessness, ecofriendliness, energy saving, maximum use of the heat of chemical reactions, fire and explosion safety, efficiency, productive capacity, the required product range and the possibility of creating flexible technologies, compliance with BAT principles, etc. The presented data allow to choose the right technology for complex granular fertilizers, depending on the abovementioned factors.

Keywords: BAT, ecofriendliness, energy saving, phosphate raw materials, wastelessness

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Authors: Hsiao-Yung Wang

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This paper aims to elaborate methodology of visual rhetoric with constant reference to Mikhail Bakhtin’s concept of “chronotope”. First, it attempts to outline Ronald Barthes, the most representative scholar of visual rhetoric and structuralism, perspective on visual rhetoric and its time-space category by referring to the concurrent word-image, the symbolic systematicity, the outer dialogicity. Second, an alternative approach is explored for grasping the dynamics and functions of visual rhetoric by articulating Mikhail Bakhtin’s concept of “chronotope.” Furthermore, that visual rhetorical consciousness could be identified as “the meaning parabola which projects from word to image,” “the symbolic system which proceeds from sequence to disorder,” “the ideological environment which struggles from the local to the global.” Last but not least, primary vision of the 2014 Taipei LGBT parade would be analyzed preliminarily to evaluate the effectiveness and persuasiveness embodied by specific visual rhetorical strategies. How Bakhtin’s concept of “chronotope” to explain the potential or possible ideological struggle deployed by visual rhetoric might be interpreted empirically and extensively.

Keywords: barthes, chronotope, Mikhail Bakhtin, Taipei LGBT parade, visual rhetoric

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Authors: Gamith Mendis

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Even though the doctors are knowledgeable, there's a gap between knowing and being ethical. This is a barrier to establish an ethical doctor-patient relationship. Current health system has oriented in a way that gives a meaning to both the doctor and the patient through intermediate entities. For the doctor, the meaning of the doctor-patient relationship is given through the financial benefits, promotions, and social status. For the patient, the meaning is given through curing of the disease. It is obvious that both are independent entities between the doctor and the patient. As the philosophers like Husserl and Heidegger have pointed out, our subjective world will give the immediate meaningfulness to us. We should seek this immediate meaningfulness of the doctor-patient relationship. The present research has used the existential methodology as guided self-reflections on the lived experiences of a doctor and his students. In this approach, two important aspects have been understood. The first is, establishing the fact that being ethical is itself giving meaningfulness to the doctor’s being without any mediate entities. Simply, it is enjoying being an honest being. The second is by being-with-the-patient while treating the disease; both the doctor and the patient can enjoy the meaningfulness of their human relationship. The medical students and the doctors should focus on this meaningfulness. For that, this discussion should be actively incorporated into the medical curriculum with programs of practical guidance to medical students and should be discussed in patient-care reviews in the health setting within a satisfactory framework.

Keywords: doctor-patient relationship, medical education, medical ethics, medical humanities, qualitative health research

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Authors: S. S. Singh, A. Bhattacharya, S. Bhattacharya

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The eukaryotic exosome complex plays a pivotal role in RNA biogenesis, maturation, surveillance and differential expression of various RNAs in response to varying environmental signals. The exosome is composed of evolutionary conserved nine core subunits and the associated exonucleases Rrp6 and Rrp44. Rrp6p is crucial for the processing of rRNAs, other non-coding RNAs, regulation of polyA tail length and termination of transcription. Rrp6p, a 3’-5’ exonuclease is required for degradation of 5’-external transcribed spacer (ETS) released from the rRNA precursors during the early steps of pre-rRNA processing. In the parasitic protist Entamoeba histolytica in response to growth stress, there occurs the accumulation of unprocessed pre-rRNA and 5’ ETS sub fragment. To understand the processes leading to this accumulation, we looked for Rrp6 and the exosome subunits in E. histolytica, by in silico approaches. Of the nine core exosomal subunits, seven had high percentage of sequence similarity with the yeast and human. The EhRrp6 homolog contained exoribonuclease and HRDC domains like yeast but its N- terminus lacked the PMC2NT domain. EhRrp6 complemented the temperature sensitive phenotype of yeast rrp6Δ cells suggesting conservation of biological activity. We showed 3’-5’ exoribonuclease activity of EhRrp6p with in vitro-synthesized appropriate RNAs substrates. Like the yeast enzyme, EhRrp6p degraded unstructured RNA, but could degrade the stem-loops slowly. Furthermore, immunolocalization revealed that EhRrp6 was nuclear-localized in normal cells but was diminished from nucleus during serum starvation, which could explain the accumulation of 5’ETS during stress. Our study shows functional conservation of EhRrp6p in E.histolytica, an early-branching eukaryote, and will help to understand the evolution of exosomal components and their regulatory function.

Keywords: entamoeba histolytica, exosome complex, rRNA processing, Rrp6

Procedia PDF Downloads 185

Authors: Ethelbert C. Njoku

Abstract:

The primary determination of any individual is the achievement of wholesome health. This is applicable to the government too. This desire becomes a reality with the efforts of medical practitioners who work day and night to ensure that the health of people is not compromised in any form. To achieve this laudable goal, the psychological wellbeing of the practitioners must be unparalleled. They must be psychologically fit in order to deliver as expected. More so, the organization must be able to provide the basic ingredients of trust in the daily management of the organization. Significantly, proper Interpersonal Communication Competence remains a necessity in the overall realization of this goal. 200 participants took part in the study, and they were selected through convenient sampling method from hospitals in Imo State. The current study adopted cross sectional survey design in trying to find out if Interpersonal Communication Competence and Organizational Trust can predict Psychological Wellbeing of medical practitioners in Imo State. Standard Multiple Regression Analysis was used for data analysis. Interestingly, the results indicate that interpersonal communication competence and organizational trust predicted psychological wellbeing among medical practitioners. The implication of this study hinges on the fact that since Interpersonal Communication Competence and Organizational Trust are important for psychological wellbeing of medical practitioners, the government and managers should try to provide opportunities that enhance these variables in the organization for the psychological wellbeing of medical practitioners.

Keywords: interpersonal communication competence, medical practitioners, organizational trust, psychological wellbeing

Procedia PDF Downloads 142

Authors: Faci Youcef, Ahmed Mebtouche, Djillali Allou, Maalem Badredine

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The inclination of the bolt in a fastened joint of composite material during a tensile test can be influenced by several parameters, including material properties, bolt diameter and length, the type of composite material being used, the size and dimensions of the bolt, bolt preload, surface preparation, the design and configuration of the joint, and finally testing conditions. These parameters should be carefully considered and controlled to ensure accurate and reliable results during tensile testing of composite materials with fastened joints. Our work focuses on the effect of the stacking sequence and the geometry of specimens. An experimental test is carried out to obtain the inclination of a bolt during a tensile test of a composite material using acoustic emission and digital image correlation. Several types of damage were obtained during the load. Digital image correlation techniques permit the obtaining of the inclination of bolt angle value during tensile test. We concluded that the inclination of the bolt during a tensile test of a composite material can be related to the damage that occurs in the material. It can cause stress concentrations and localized deformation in the material, leading to damage such as delamination, fiber breakage, matrix cracking, and other forms of failure.

Keywords: damage, inclination, analyzed, carbon

Procedia PDF Downloads 42

Authors: Gary Pattemore, Michael Edgar, Andrew Job, Marina Auad, Kathryn Job

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As critical commodities become more scarce, significant time and resources have been used to better understand complicated ore bodies and extract their full potential. These challenging ore bodies provide several pain points for geologists and engineers to overcome, poor handling of these issues flows downs stream to the processing plant affecting throughput rates and recovery. Many open cut mines utilise blast hole drilling to extract additional information to feed back into the modelling process. This method requires samples to be collected during or after blast hole drilling. Samples are then sent for assay with turnaround times varying from 1 to 12 days. This method is time consuming, costly, requires human exposure on the bench and collects elemental data only. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum to scan samples, collars or take down hole measurements for minerals and moisture content and grade abundances. Automation of this process using unmanned vehicles and on-board processing reduces human in pit exposure to ensure ongoing safety. On-board processing allows data to be integrated into modelling workflows with immediacy. The preliminary results demonstrate numerous direct and indirect benefits from this new technology, including rapid and accurate grade estimates, moisture content and mineralogy. These benefits allow for faster geo modelling updates, better informed mine scheduling and improved downstream blending and processing practices. The paper presents recommendations for implementation of the technology in open cut mining environments.

Keywords: grade control, hyperspectral scanning, artificial intelligence, autonomous mining, machine learning

Procedia PDF Downloads 94

Authors: Saeed Hosseini, Ali Reza Tahavvor

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Sound processing is one the subjects that newly attracts a lot of researchers. It is efficient and usually less expensive than other methods. In this paper the flow generated sound is used to estimate the flow speed of free flows. Many sound samples are gathered. After analyzing the data, a parameter named wave power is chosen. For all samples, the wave power is calculated and averaged for each flow speed. A curve is fitted to the averaged data and a correlation between the wave power and flow speed is founded. Test data are used to validate the method and errors for all test data were under 10 percent. The speed of the flow can be estimated by calculating the wave power of the flow generated sound and using the proposed correlation.

Keywords: the flow generated sound, free stream, sound processing, speed, wave power

Procedia PDF Downloads 397

Authors: Jason Gica, Yi-Hsieng Samuel Wu, Deng-Jye Yang, Yi-Chen Chen

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Mycotoxins, harmful secondary metabolites produced by certain fungi species, pose significant risks to animals and humans worldwide. Their stable properties lead to contamination during grain harvesting, transportation, and storage, as well as in processed food products. The prevalence of mycotoxin contamination has attracted significant attention due to its adverse impact on food safety and global trade. The secondary contamination pathway from animal products has been identified as an important route of exposure, posing health risks for livestock and humans consuming contaminated products. Pork, one of the highly consumed meat products in Taiwan according to the National Food Consumption Database, plays a critical role in the nation's diet and economy. Given its substantial consumption, pork processing products are a significant component of the food supply chain and a potential source of mycotoxin contamination. This study is paramount for formulating effective regulations and strategies to mitigate mycotoxin-related risks in the food supply chain. By establishing a reliable analytical method, this research contributes to safeguarding public health and enhancing the quality of pork processing products. The findings will serve as valuable guidance for policymakers, food industries, and consumers to ensure a safer food supply chain in the face of emerging mycotoxin challenges. An innovative and efficient analytical approach is proposed using Ultra-High Performance Liquid Chromatography coupled with Temperature Control Fluorescence Detector Light (UHPLC-TCFLD) to determine multiple mycotoxins in pork meat samples due to its exceptional capacity to detect multiple mycotoxins at the lowest levels of concentration, making it highly sensitive and reliable for comprehensive mycotoxin analysis. Additionally, its ability to simultaneously detect multiple mycotoxins in a single run significantly reduces the time and resources required for analysis, making it a cost-effective solution for monitoring mycotoxin contamination in pork processing products. The research aims to optimize the efficient mycotoxin QuEChERs extraction method and rigorously validate its accuracy and precision. The results will provide crucial insights into mycotoxin levels in pork processing products.

Keywords: multiple-mycotoxin analysis, pork processing products, QuEChERs, UHPLC-TCFLD, validation

Procedia PDF Downloads 46

Authors: Sheila Shahidi

Abstract:

UV protection by fabrics has recently become a focus of great interest, particularly in connection with environmental degradation or ozone layer depletion. Fabrics provide simple and convenient protection against UV radiation (UVR), but not all fabrics offer sufficient UV protection. To describe the degree of UVR protection offered by clothing materials, the ultraviolet protection factor (UPF) is commonly used. UV-protective fabric can be generated by application of a chemical finish using normal wet-processing methodologies. However, traditional wet-processing techniques are known to consume large quantities of water and energy and may lead to adverse alterations of the bulk properties of the substrate. Recently, usage of plasmas to generate physicochemical surface modifications of textile substrates has become an intriguing approach to replace or enhance conventional wet-processing techniques. In this research work the effect of plasma treatment on UV protection properties of fabrics was investigated. DC magnetron sputtering was used and the parameters of plasma such as gas type, electrodes, time of exposure, power and, etc. were studied. The morphological and chemical properties of samples were analyzed using Scanning Electron Microscope (SEM) and Furrier Transform Infrared Spectroscopy (FTIR), respectively. The transmittance and UPF values of the original and plasma-treated samples were measured using a Shimadzu UV3101 PC (UV–Vis–NIR scanning spectrophotometer, 190–2, 100 nm range). It was concluded that, plasma which is an echo-friendly, cost effective and dry technique is being used in different branches of the industries, and will conquer textile industry in the near future. Also it is promising method for preparation of UV protection textile.

Keywords: fabric, plasma, textile, UV protection

Procedia PDF Downloads 507

Authors: Rashmi Gupta

Abstract:

Rewarding stimuli capture attention involuntarily as a result of an association process that develops quickly during value learning, referred to as the reward or value-driven attentional capture. It is essential to compare reward with punishment processing to get a full picture of value-based modulation in visual attention processing. Hence, the present study manipulated both valence/value (reward as well as punishment) and motivational salience (probability of an outcome: high vs. low) together. Series of experiments were conducted, and there were two phases in each experiment. In phase 1, participants were required to learn to associate specific face stimuli with a high or low probability of winning or losing points. In the second phase, these conditioned stimuli then served as a distractor or prime in a speeded letter search task. Faces with high versus low outcome probability, regardless of valence, slowed the search for targets (specifically the left visual field target) and suggesting that the costs to performance on non-emotional cognitive tasks were only driven by motivational salience (high vs. loss) associated with the stimuli rather than the valence (gain vs. loss). It also suggests that the processing of motivationally salient stimuli is right-hemisphere biased. Together, results of these studies strengthen the notion that our visual attention system is more sensitive to affected by motivational saliency rather than valence, which termed here as motivational-driven attentional capture.

Keywords: attention, distractors, motivational salience, valence

Procedia PDF Downloads 209