Search results for: similarity searching

Authors: Paul Awolade, Parvesh Singh

Abstract:

The global threat of pathogenic resistance to available therapeutic agents has become a menace to clinical practice, public health and man’s existence inconsequential. This has therefore led to an exigency in the development of new molecular scaffolds with profound activity profiles. In this vein, a versatile synthetic tool for accessing new molecules by incorporating two or more pharmacophores into a single entity with the unique ability to be recognized by multiple receptors hence leading to an improved bioactivity, known as molecular hybridization, has been explored with tremendous success. Accordingly, aware of the similarity in pharmacological activity spectrum of quinoline and 1,2,3-triazole pharmacophores such as; anti-Alzheimer, anticancer, anti-HIV, antimalarial and antimicrobial to mention but a few, the present study sets out to synthesize hybrids of quinoline and 1,2,3-triazole. The hybrids were accessed via click chemistry using copper catalysed azide-alkyne 1,3-dipolar cycloaddition reaction. All synthesized compounds were evaluated for their pharmaco-potential in an antimicrobial assay out of which the 3-OH derivative emerged as the most active with MIC value of 4 μg/mL against Cryptococcus neoformans; a value superior to standard Fluconazole and comparable to Amphotericin B. Structures of synthesized hybrids were elucidated using appropriate spectroscopic techniques (1H, 13C and 2D NMR, FT-IR and HRMS).

Keywords: bioisostere, click chemistry, molecular hybridization, quinoline, 1, 2, 3-triazole

Procedia PDF Downloads 116

Authors: Halima Benazzouz

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It is often believed that British English and American English are the foremost varieties of the English Language serving as reference norms for other varieties;that is the reason why they have obviously been compared and contrasted.Meanwhile,the terms “British English” and “American English” are used differently by different people to refer to: 1) Two national varieties each subsuming regional and other sub-varieties standard and non-standard. 2) Two national standard varieties in which each one is only part of the range of English within its own state, but the most prestigious part. 3) Two international varieties, that is each is more than a national variety of the English Language. 4) Two international standard varieties that may or may not each subsume other standard varieties.Furthermore,each variety serves as a reference norm for users of the language elsewhere. Moreover, without a clear identification, as primarily belonging to one variety or the other, British English(Br.Eng) and American English (Am.Eng) are understood as national or international varieties. British English and American English are both “variants” and “varieties” of the English Language, more similar than different.In brief, the following may justify general categories of difference between Standard American English (S.Am.E) and Standard British English (S.Br.e) each having their own sociolectic value: A difference in pronunciation exists between the two foremost varieties, although it is the same spelling, by contrast, a divergence in spelling may be recognized, eventhough the same pronunciation. In such case, the same term is different but there is a similarity in spelling and pronunciation. Otherwise, grammar, syntax, and punctuation are distinctively used to distinguish the two varieties of the English Language. Beyond these differences, spelling is noted as one of the chief sources of variation.

Keywords: Greek, Latin, French pronunciation expert, varieties of English language

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Authors: Xu Rumin, Chen Jianyi, Yue Ti, Wang Yaan

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With the rapid development of subsea oil and gas exploitation, the demand for the related underwater process equipment is increasing fast. In order to reduce the energy consuming, people tend to separate the gas and oil phase directly on the seabed. Accordingly, an advanced separator is needed. In this paper, the pressure drop of a new type of separator named Gas Liquid Cylindrical Cyclone (GLCC) which is used in the subsea system is investigated by both experiments and numerical simulation. In the experiments, the single phase flow and gas-liquid two phase flow in GLCC were tested. For the simulation, the performance of GLCC under both laboratory and industrial conditions was calculated. The Eulerian model was implemented to describe the mixture flow field in the GLCC under experimental conditions and industrial oil-natural gas conditions. Furthermore, a relationship among Euler number (Eu), Reynolds number (Re), and Froude number (Fr) is generated according to similarity analysis and simulation data, which can present the GLCC separation performance of pressure drop. These results can give reference to the design and application of GLCC in deep sea.

Keywords: dimensionless analysis, gas-liquid cylindrical cyclone, numerical simulation, pressure drop

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Authors: Rana Gill, Chandandeep Kaur

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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.

Keywords: segmentation, edge detection, text, extracting characters

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Authors: Muhammedamin Hussen Saad, Geoffrey Haagler, Onno Omta, Gerben Van Der Velde

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Unfolding chaos and persistent disruptions pose threats to companies’ performance especially in vulnerable settings of SME’s particularly in developing countries. Attention for resilience research in the academic world has increased considerably during the last decade looking at the number of papers published. As we are interested in adding to the understanding of the foundation and development of the concept of resilience, we focus especially on structuring the literature of business resilience in those vulnerable settings. A well-structured systematic search & review procedure was deployed. First, we defined key search terms and applied these to multiple databases (Scopus, Web of Science, Google Scholar, Emerald, and Science Direct). To make our literature search more encompassing, we augmented with co-citation, reference checking including hand searching techniques. The paper offers (1) an overview of SMEs resilience literature from 2000 up to March 2017 comprising 88 articles, and (2) special attention, within that overview, to developing countries. This review concludes that resilience literature is very much diverse in definitions and its measurements, and is inconclusive about its influencing factors. Furthermore, resilience literature is based predominantly on research in the developed world. On the bases of how the concept resilience emerges from the literature we describe distinct features of resilience, give options to extend the theoretical bases of research into resilience and describe concrete ideas for further research.

Keywords: business resilience, systematic review, SMEs, developing countries

Procedia PDF Downloads 155

Authors: Moneesh Thakur, Hridayesh Prasad, Nikitasha Bora, Parimal Roy Choudhary, A. K. Samanta, Sanjeev Kumar

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Canine demodicosis is a common parasitic condition which involves dog skin. Demodicosis in dogs is due the prominent growth of Demodex. Out of various canine Demodex spp., Demodex canis is the most often involved species. Canine demodicosis can occur as either a localized or generalized form of demodicosis severely affect the dogs and in non-treated dogs may cause death. This study was planned with the aim to screen and characterize the 18S rRNA gene of isolated Demodex canis. A total of 1200 dogs were screened during this study period. The skin scrapings of all the suspected dogs were examined under a microscope at 100X magnification for the presence of Demodex canis. The skin scrapings positive for Demodex canis were examined using PCR for confirmation. A total of 35 dogs were confirmed a positive result for D. canis based on 18S rRNA gene amplification by PCR. Further, the 18S rRNA gene of isolated Demodex canis was cloned and sequenced for genome analysis. On the sequence analysis, it was found that isolated sequence (GenBank Accession No. MK177513) had close similarity (99.7%) to that of D. canis genotype of China (Accession No. MG372254).

Keywords: PCR, phylogenetic analysis, cloning and sequening, Demodex canis

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Authors: Rouhollah Radjabi, Mojtaba Zarei, Elham Sanatgar, Hossein Shouhani

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RAPD molecular markers in order to discrimination of the Iranian native Bombyx mori silkworm breeds were used. DNA extraction using phenol - chloroform was and the qualitative and quantitative measurements of extracted DNA and its dilution, the obtained bands on agarose gel 1.5 percent were marked and analyzed. Results showed that the bands are observed between 250-2500 bp and most bands have been observed as Gilani-orange, the lowest bands observed are Khorasani-lemon. Primer 3 with 100% polymorphism with the highest polymorphism and primer 2 with 61.5 polymorphism had the lowest percentage of polymorphism. Cluster analysis of races and placed them in three main groups, races Gilani - orange, Baghdad and Khorasani -pink if the first group, camel's thorn, Herati - yellow race was alone in the second group and Khorasani – lemon was alone in the third group. The greatest similarity between the races, between Khorasani- pink and Baghdad (0.64). RAPD markers have been determined different silkworm races based on various morphological or economic characteristics except geographic origin.

Keywords: silkworm, molecular marker, RAPD, Iran

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Jiya Mohammed, Ibrahim Ismail Giwa

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Unsteady squeezing flow of a viscous fluid between parallel plates is considered. The two plates are considered to be approaching each other symmetrically, causing the squeezing flow. Two-dimensional rectangular Cartesian coordinate is considered. The Navier-Stokes equation was reduced using similarity transformation to a single fourth order non-linear ordinary differential equation. The energy equation was transformed to a second order coupled differential equation. We obtained solution to the resulting ordinary differential equations via Homotopy Perturbation Method (HPM). HPM deforms a differential problem into a set of problem that are easier to solve and it produces analytic approximate expression in the form of an infinite power series by using only sixth and fifth terms for the velocity and temperature respectively. The results reveal that the proposed method is very effective and simple. Comparisons among present and existing solutions were provided and it is shown that the proposed method is in good agreement with Variation of Parameter Method (VPM). The effects of appropriate dimensionless parameters on the velocity profiles and temperature field are demonstrated with the aid of comprehensive graphs and tables.

Keywords: coupled differential equation, Homotopy Perturbation Method, plates, squeezing flow

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Authors: E. Tandis, E. Assareh

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Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

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Authors: Ritu Vashistha, Jitender Kumar

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Clustering using the K-means algorithm is a very common way to understand and analyze the obtained output data. When a similar object is grouped, this is called the basis of Clustering. There is K number of objects and C number of cluster in to single cluster in which k is always supposed to be less than C having each cluster to be its own centroid but the major problem is how is identify the cluster is correct based on the data. Formulation of the cluster is not a regular task for every tuple of row record or entity but it is done by an iterative process. Each and every record, tuple, entity is checked and examined and similarity dissimilarity is examined. So this iterative process seems to be very lengthy and unable to give optimal output for the cluster and time taken to find the cluster. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses the Euclidian distance formula as well melanosis to find the minimum distance between slots as technically we called clusters and the same approach we have also applied to Ant Colony Optimization(ACO) algorithm, which results in the production of two and multi-dimensional matrix.

Keywords: ant colony optimization, data clustering, centroids, data mining, k-means

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

Procedia PDF Downloads 388

Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: James Alberto Ortega Morales, Nelson Andrés Martínez Marín

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The bulding industry is one of the most important sectors all around the world in terms of contribution to index like GDP and labor. On the other hand, it is a major contributor to Greenhouse Gases (GHG) and waste generation contributing to global warming. In this sense, it is necessary to establish sustainable practices both from the strategic point of view to the operations point of view as well in all business and industries. Business models don’t scape to this reality attending it´s mediator role between strategy and operations. Business models can turn from the traditional practices searching economic benefits to sustainable bussines models that generate both economic value and value for society and the environment. Recent advances in the analysis of sustainable business models find different classifications that allow finding potential triple bottom line (economic, social and environmental) solutions applicable in every business sector. Into the metioned Advances have been identified, 11 groups and 45 patterns of sustainable business models have been identified; such patterns can be found either in the business models as a whole or found concurrently in their components. This article presents the analysis of a case study, seeking to identify the components and elements that are part of it, using the ECO CANVAS conceptual model. The case study allows showing the concurrent existence of different patterns of business models for sustainability empirically, serving as an example and inspiration for other Latin American companies interested in integrating sustainability into their new and existing business models.

Keywords: sustainable business models, business sustainability, business model patterns, case study, construction industry

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Authors: Hassan M. H. Mustafa

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This piece of research addresses an interesting comparative analytical study. Which considers two concepts of diverse algorithmic computational intelligence approaches related tightly with Neural and Non-Neural Systems. The first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Besides a mouse’s trial during its movement inside figure of eight (8) maze, to reach an optimal solution for reconstruction problem. Conversely, second algorithmic intelligent approach originated from observed activities’ results for Non-Neural Ant Colony System (ACS). These results obtained after reaching an optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced systems. Finally, performance of both intelligent learning paradigms shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

Keywords: artificial neural network modeling, animal learning, ant colony system, traveling salesman problem, computational biology

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Authors: Saeed Wahass

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Objective: Obesity is epidemic, affecting all societies and cultures. Most serious medical illnesses are attributed to obesity. For this reason, all healthcare systems worldwide have focused on obesity for both intervention and prevention. However, there is scientific evidence supporting that obesity is treatable through implementing different modalities of interventions. They include biological interventions like medications and bariatric surgeries and behavioral interventions. It seems healthcare professionals may suggest the quick and the easiest interventions for obesity like surgery, ignoring other modesties that might require efforts from their sides and patients as well. Searching on the onset, progression and prevention, behavior plays a major role. As a result, psychological interventions have become increasingly core for intervention and prevention of obesity. They are effective and cost effective in dealing with obesity. Methods: A questionnaire describing the role of behavior on obesity and the way it can be prevented and treated was distributed to a group of health professionals who are dealing with obesity e.g. bariatric surgeons, bariatric physicians, psychologists, health educators, nurses and social workers. Results: 88% of healthcare professionals believed that behavior plays a major role on the onset and progression of obesity, 95% of them recognized that obesity can be prevented with consideration for behavior factors. A major proportion (87%) of the respondents see that psychological interventions are effective and cost effective in treating obesity. Conclusions: It optimistically appears that the majority of healthcare professionals believe that behavior is a key component in understanding, preventing and treating obesity. This outcome may help in developing specific training courses for healthcare professionals, who are dealing with obesity concerning the way they can treat patients behaviorally and, moreover, educating the community.

Keywords: behavior, obesity, healthcare provider, psychological interventions

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

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Authors: Rupert G. S. Legg

Abstract:

The connection between poor health outcomes and living near contaminated land has long been understood. Less examined has been the impact of remediation on residents’ health. The cleaning process undoubtedly changes the local area in which it occurs, leading to the possibility that local housing and rental prices could increase resulting in the displacement of those least able to cope. Whether or not this potential displacement resulting from remediation has a considerable impact on health remains unknown. This review aims to determine how these health effects have been approached in the health geography literature. A systematic review of health geographies literature was conducted, searching for two-word clusters: ‘health’ and ‘remediation’ (100 articles); and ‘health’, ‘displacement’ and ‘gentrification’ (43 articles). 43 articles were selected for their relevance (7 from the first cluster, 20 from the second, and 16 from those cited within the reviewed articles). Several of the reviewed cases identified that potential displacement was a contributor to stress and worry in residents living near remediation projects. Likewise, the experience of displacement in other cases beyond remediation was linked with several mental health issues. However, no remediation cases followed-up on the ultimate effects of experiencing displacement on residents’ health. A reason identified for this was a tendency for reviewed studies to adopt a contextual or compositional approach, as opposed to a relational approach, which is more concerned with dimensions of mobility and temporality. Given that remediation and displacement both involve changing mobility and temporality, focussing solely on contextual or compositional factors is problematic. This review concludes by suggesting that more thorough, relational research is conducted into the extent to which potential displacement resulting from remediation affects health.

Keywords: contamination, displacement, health geography, remediation

Procedia PDF Downloads 154

Authors: Naoum Abderrahmane, Boumehed Meriem, Alshaqaqi Belal

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Background subtraction algorithm is a larger used technique for detecting moving objects in video surveillance to extract the foreground objects from a reference background image. There are many challenges to test a good background subtraction algorithm, like changes in illumination, dynamic background such as swinging leaves, rain, snow, and the changes in the background, for example, moving and stopping of vehicles. In this paper, we propose an efficient and accurate background subtraction method for moving object detection in video surveillance. The main idea is to use a developed fast-independent component analysis (ICA) algorithm to separate background, noise, and foreground masks from an image sequence in practical environments. The fast-ICA algorithm is adapted and adjusted with a matrix calculation and searching for an optimum non-quadratic function to be faster and more robust. Moreover, in order to estimate the de-mixing matrix and the denoising de-mixing matrix parameters, we propose to convert all images to YCrCb color space, where the luma component Y (brightness of the color) gives suitable results. The proposed technique has been verified on the publicly available datasets CD net 2012 and CD net 2014, and experimental results show that our algorithm can detect competently and accurately moving objects in challenging conditions compared to other methods in the literature in terms of quantitative and qualitative evaluations with real-time frame rate.

Keywords: background subtraction, moving object detection, fast-ICA, de-mixing matrix

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Authors: Mohamad Shariff Nurasikin, Paul Crawford, Nicola Wright

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People with any mental disorder can get benefit from the spiritual aspects of life for recovery, particularly in searching for the meaning of life and engaging in meaningful activities. However, little is known about such effects in the population of bipolar disorder. The concepts of spirituality are highly contestable, as they are too broad and removed from the original religious understanding. The concepts are more notable as encompassing multi-dimensional aspects of people’s lives such as social, emotional, and psychological. Viewing that Western or secular worldview dominates most of the literature in spirituality, it is time to explore the concept of spirituality from the Eastern and religious worldview, such as the Malaysian view. Thus, the aim of this study is to provide a conceptual understanding of people with bipolar disorder with a religious affiliation in Malaysia. This study employs a Grounded Theory and explores the narratives from the interviews of 25 participants. The narratives strongly suggest the salient resources or can be referred to as various forms of capital, as in the capital theory, namely, religious, social, psychological, and medicinal. More important is how these capitals are the enablers for recovery in mental health and well-being, where the participants in the sample engage in a more meaningful life and positive adaptations. This study also extends the Bourdieusian spiritual capital, in which the salient resources are termed as the capital bundle. More significant is how the capital bundles are working contiguously in building and accumulating the spiritual capital. This process is conducive to recovery within the social life of people with bipolar disorder or perhaps other mental disorders.

Keywords: bipolar, Bourdeau, recovery, spiritual

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Authors: Jinseon Song, Yongwan Park

Abstract:

In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction.

Keywords: positioning, distance, camera, features, SURF(Speed-Up Robust Features), database, estimation

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Authors: Irina Khutsishvili

Abstract:

In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations, since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

Keywords: In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

Procedia PDF Downloads 111

Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

Abstract:

Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

Procedia PDF Downloads 238

Authors: Yash M. Gupta, Kittisak Buddhachat, Surin Peyachoknagul, Somjit Homchan

Abstract:

The house cricket, Acheta domesticus is one of the commonly found species of field crickets. Although it is very commonly used as food and feed, the genomic information of house cricket is still missing for genetic investigation. DNA sequencing technology has evolved over the decades, and it has also revolutionized the molecular marker development for genetic analysis. In the present study, we have sequenced the whole genome of A. domesticus using illumina platform based HiSeq X Ten sequencing technology for searching simple sequence repeats (SSRs) in DNA to develop polymorphic microsatellite markers for population genetic analysis. A total of 112,157 SSRs with primer pairs were identified, 91 randomly selected SSRs used to check DNA amplification, of which nine primers were polymorphic. These microsatellite markers have shown cross-amplification with other three species of crickets which are Gryllus bimaculatus, Gryllus testaceus and Brachytrupes portentosus. These nine polymorphic microsatellite markers were used to check genetic variation for forty-five individuals of A. domesticus, Phitsanulok population, Thailand. For nine loci, the number of alleles was ranging from 5 to 15. The observed heterozygosity was ranged from 0.4091 to 0.7556. These microsatellite markers will facilitate population genetic analysis for future studies of A. domesticus populations. Moreover, the transferability of these SSR makers would also enable researchers to conduct genetic studies for other closely related species.

Keywords: cross-amplification, microsatellite markers, observed heterozygosity, population genetic, simple sequence repeats

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Authors: H. Niranjan, S. Sivasankaran

Abstract:

Heat and mass transfer near a steady stagnation point boundary layer flow of viscous incompressible fluid through porous media investigates along a vertical plate is thoroughly studied under the presence of magneto hydrodynamic (MHD) effects. The fluid flow is steady, laminar, incompressible and in two-dimensional. The nonlinear differential coupled parabolic partial differential equations of continuity, momentum, energy and specie diffusion are converted into the non-similar boundary layer equations using similarity transformation, which are then solved numerically using the Runge-Kutta method along with shooting method. The effects of the conjugate heat transfer parameter, the porous medium parameter, the permeability parameter, the mixed convection parameter, the magnetic parameter, and the thermal radiation on the velocity and temperature profiles as well as on the local skin friction and local heat transfer are presented and analyzed. The validity of the methodology and analysis is checked by comparing the results obtained for some specific cases with those available in the literature. The various parameters on local skin friction, heat and mass transfer rates are presented in tabular form.

Keywords: MHD, porous medium, slip, convective boundary condition, stagnation point

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Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

Abstract:

Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

Procedia PDF Downloads 363