Search results for: semantic similarity

Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Jinseon Song, Yongwan Park

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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

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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.

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

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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

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Authors: H. Niranjan, S. Sivasankaran

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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

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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

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Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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In this paper, a non-cooperative game method is formulated where all players compete to transmit at higher power. Every base station represents a player in the game. The game is solved by obtaining the Nash equilibrium (NE) where the game converges to optimality. The proposed method, named Power Efficient Handover Game Theoretic (PEHO-GT) approach, aims to control the handover in dense small cell networks. Players optimize their payoff by adjusting the transmission power to improve the performance in terms of throughput, handover, power consumption and load balancing. To select the desired transmission power for a player, the payoff function considers the gain of increasing the transmission power. Then, the cell selection takes place by deploying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A game theoretical method is implemented for heterogeneous networks to validate the improvement obtained. Results reveal that the proposed method gives a throughput improvement while reducing the power consumption and minimizing the frequent handover.

Keywords: energy efficiency, game theory, handover, HetNets, small cells

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Authors: Aditi Chauhan, Neethu S. Mohan

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In the modern era, the advancement and digitalization in technology have taken place during an investigation of crime scene. The rapid enhancement and investigative techniques have changed the mean of identification of suspect. Identification of the person is one of the significant aspects, and personal authentication is the key of security and reliability in society. Since early 90 s, people have relied on comparing handwriting through its class and individual characteristics. But in today’s 21st century we need more reliable means to identify individual through handwriting. An approach employing computer modalities have lately proved itself auspicious enough in exploration of hieroglyphics substantial in investigating the case. Various software’s such as FISH, WRITEON, and PIKASO, CEDAR-FOX SYSTEM identify and verify the associated quantitative measure of the similarity between two samples. The research till date has been confined to identify the authorship of the concerned samples. But prospects associated with the use of computational modalities might help to identify disguised writing, forged handwriting or say altered or modified writing. Considering the applications of such modal, similar work is sure to attract plethora of research in immediate future. It has a promising role in national security too. Documents exchanged among terrorist can also be brought under the radar of surveillance, bringing forth their source of existence.

Keywords: documents, identity, computational system, suspect

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Authors: Ole Michael Spaten

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This article presents similarities and differences between psychotherapy, coaching psychology and coaching, and hence discusses boundaries between these diverse fields of practice. The point of departure will be prevailing arguments and descriptions in the scientific community, and it shows both commonalities and major differences in relation to the application in daily practice. The results (the similarities and differences) are presented and discussed in the light of scientific research and different theoretical perspectives, including both classic and recent scholars. Some of the main differences presented are; the clinical/non-clinical perspective and the educational differences, including the different criteria and demands which professionals working in these three different professions, should undergo to obtain their certification. Further, one of the main similarities is presented: the importance of the relationship between the therapist/coach and the client/coachee. The goal and task oriented focus are also presented as a similarity between the three intervention forms – at least to some extent. Finally, some central concepts from the fields are presented in a table for a proposal of distinctions and interfaces. It is concluded that a comprehensive education in combination with an understanding of the differences and similarities between the three intervention forms is of significant importance for the professional working in either of the fields. Future studies should, however, include additional research on the similarities and differences and how to continue the educational progress in all three disciplines.

Keywords: boundaries, coaching, coaching psychology, interface, psychotherapy

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Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

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A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: ontologies, relational databases, SPARQL, web interface

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Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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Authors: Rana Bazaid, Debajyoti Pati

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Purpose: The aim of this research is to explore and discuss innovative workspaces, and how the design of the space has the potential to facilitate the work process and employees’ satisfaction which can lead to innovative results. Background: The relationship between the workforce and the work environment has a strong potential to enhance human capabilities associated with innovation outcomes. The need for innovation in workplaces can benefit employees’ satisfaction, health, and performance. To understand this complicated relationship, this research explores and comprehends innovative work environments. Methods: A review of 26 peer-reviewed articles, seven books, and 23 companies’ websites was conducted, along with analysis for five case studies on successful types of research and development fields to detect appropriate examples for the study. Results: The analysis of the five case studies showed the similarity-characteristics of innovation work environments among those five fields and observed what is unique about each field that makes them stand out in their industries. Conclusion: Understanding the psychological, cultural, physiological, and social needs of workers, physical workplaces, and issues found in the work environment may help enhance multifaceted innovation and productivity.

Keywords: innovation, productivity , work environment, workers satisfaction

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Authors: Srinivasacharya Darbhasayanam, Jagadeeshwar Pashikanti

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This paper analyzes the Soret and Dufour effects on mixed convection flow, heat and mass transfer from an exponentially stretching surface in a viscous fluid with Hall Effect. The governing partial differential equations are transformed into ordinary differential equations using similarity transformations. The nonlinear coupled ordinary differential equations are reduced to a system of linear differential equations using the successive linearization method and then solved the resulting linear system using the Chebyshev pseudo spectral method. The numerical results for the velocity components, temperature and concentration are presented graphically. The obtained results are compared with the previously published results, and are found to be in excellent agreement. It is observed from the present analysis that the primary and secondary velocities and concentration are found to be increasing, and temperature is decreasing with the increase in the values of the Soret parameter. An increase in the Dufour parameter increases both the primary and secondary velocities and temperature and decreases the concentration.

Keywords: Exponentially stretching sheet, Hall current, Heat and Mass transfer, Soret and Dufour Effects

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Authors: Lukas Stiburek, Jana Cesnekova, Josef Houstek, Jiri Zeman

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Cellular function depends on mitochondrial function and integrity that is therefore maintained by several classes of proteins possessing chaperone and/or proteolytic activities. In this work, we focused on characterization of LACE1 (lactation elevated 1) function in mitochondrial protein homeostasis maintenance. LACE1 is the human homologue of yeast mitochondrial Afg1 ATPase, a member of SEC18-NSF, PAS1, CDC48-VCP, TBP family. Yeast Afg1 was shown to be involved in mitochondrial complex IV biogenesis, and based on its similarity with CDC48 (p97/VCP) it was suggested to facilitate extraction of polytopic membrane proteins. Here we show that LACE1, which is a mitochondrial integral membrane protein, exists as part of three complexes of approx. 140, 400 and 500 kDa and is essential for maintenance of fused mitochondrial reticulum and lamellar cristae morphology. Using affinity purification of LACE1-FLAG expressed in LACE1 knockdown background we show that the protein physically interacts with mitochondrial inner membrane protease YME1L. We further show that human LACE1 exhibits significant pro-apoptotic activity and that the protein is required for normal function of the mitochondrial respiratory chain. Thus, our work establishes LACE1 as a novel factor with the crucial role in mitochondrial homeostasis maintenance.

Keywords: LACE1, mitochondria, apoptosis, protease

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Authors: K. Jafar, R. Nazar, A. Ishak, I. Pop

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The present analysis considers the steady stagnation point flow and heat transfer towards a permeable sheet in an upper-convected Maxwell (UCM) electrically conducting fluid, with a constant magnetic field applied in the transverse direction to flow, and a local heat generation within the boundary layer with a heat generation rate proportional to (T-T_inf)^p. Using a similarity transformation, the governing system of partial differential equations is first transformed into a system of ordinary differential equations, which is then solved numerically using a finite-difference scheme known as the Keller-box method. Numerical results are obtained for the flow and thermal fields for various values of the shrinking/stretching parameter lambda, the magnetic parameter M, the elastic parameter K, the Prandtl number Pr, the suction parameter s, the heat generation parameter Q, and the exponent p. The results indicate the existence of dual solutions for the shrinking sheet up to a critical value lambda_c whose value depends on the value of M, K, and s. In the presence of internal heat absorbtion (Q<0), the surface heat transfer rate decreases with increasing p but increases with parameter Q and s, when the sheet is either stretched or shrunk.

Keywords: magnetohydrodynamic (MHD), boundary layer flow, UCM fluid, stagnation point, shrinking sheet

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Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

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Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: massive open online course, MOOC, online learning, e-learning

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Authors: Mishu Gupta, Rama Gupta

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It has been elucidated here that non- autonomous discrete non-linear Schrödinger equation is associated with saturable non-linearity through photo-refractive media. We have investigated the localized solution of non-autonomous saturable discrete non-linear Schrödinger equations. The similarity transformation has been involved in converting non-autonomous saturable discrete non-linear Schrödinger equation to constant-coefficient saturable discrete non-linear Schrödinger equation (SDNLSE), whose exact solution is already known. By back substitution, the solution of the non-autonomous version has been obtained. We have analysed our solution for the hyperbolic and periodic form of gain/loss term, and interesting results have been obtained. The most important characteristic role is that it helps us to analyse the propagation of electromagnetic waves in glass fibres and other optical wave mediums. Also, the usage of SDNLSE has been seen in tight binding for Bose-Einstein condensates in optical mediums. Even the solutions are interrelated, and its properties are prominently used in various physical aspects like optical waveguides, Bose-Einstein (B-E) condensates in optical mediums, Non-linear optics in photonic crystals, and non-linear kerr–type non-linearity effect and photo refracting medium.

Keywords: B-E-Bose-Einstein, DNLSE-Discrete non linear schrodinger equation, NLSE-non linear schrodinger equation, SDNLSE - saturable discrete non linear Schrodinger equation

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Authors: Muhammad Zubair Akbar Qureshi, Abdul Bari Farooq

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The intention of the current study to analyze the significance of nano-layer in incompressible magneto-hydrodynamics (MHD) flow of a Newtonian nano-fluid consisting of carbon nano-materials has been considered through an absorbent channel with moving porous walls. Using applicable similarity transforms, the governing equations are converted into a system of nonlinear ordinary differential equations which are solved by using the 4th-order Runge-Kutta technique together with shooting methodology. The phenomena of nano-layer have also been modeled mathematically. The inspiration behind this segment is to reveal the behavior of involved parameters on velocity and temperature profiles. A detailed table is presented in which the effects of involved parameters on shear stress and heat transfer rate are discussed. Specially presented the impact of the thickness of the nano-layer and radius of the particle on the temperature profile. We observed that due to an increase in the thickness of the nano-layer, the heat transfer rate increases rapidly. The consequences of this research may be advantageous to the applications of biotechnology and industrial motive.

Keywords: carbon nano-tubes, magneto-hydrodynamics, nano-layer, thermal conductivity

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: M. J. Norashirene, Y. Zakiah, S. Nurdiana, I. Nur Hilwani, M. H. Siti Khairiyah, M. J. Muhamad Arif

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In this study, six bacterial isolates of a slightly thermophilic organism from the Sg. Klah hot spring, Malaysia were successfully isolated and designated as M7T55D1, M7T55D2, M7T55D3, M7T53D1, M7T53D2 and M7T53D3 respectively. The bacterial isolates were screened for their cellulose hydrolytic ability on Carboxymethlycellulose agar medium. The isolated bacterial strains were identified morphologically, biochemically and molecularly with the aid of 16S rDNA sequencing. All of the bacteria showed their optimum growth at a slightly alkaline pH of 7.5 with a temperature of 55°C. All strains were Gram-negative, non-spore forming type, strictly aerobic, catalase-positive and oxidase-positive with the ability to produce thermostable cellulase. Based on BLASTn results, bacterial isolates of M7T55D2 and M7T53D1 gave the highest homology (97%) with similarity to Tepidimonas ignava while isolates M7T55D1, M7T55D3, M7T53D2 and M7T53D3 showed their closest homology (97%-98%) with Tepidimonas thermarum. These cellulolytic thermophiles might have a commercial potential to produce valuable thermostable cellulase.

Keywords: cellulase, cellulolytic, thermophiles, 16S rDNA gene

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Authors: Srijoni Banerjee

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Sequences of some of the candidate genes (e.g., CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG) implicated in some of the complex disease, e.g. Type II diabetes in man has been compared with other species to investigate phylogenetic affinity. Based on mRNA sequence of these genes of 7 to 8 species, using bioinformatics tools Mega 5, Bioedit, Clustal W, distance matrix was obtained. Phylogenetic trees were obtained by NJ and UPGMA clustering methods. The results of the phylogenetic analyses show that of the species compared: Xenopus l., Danio r., Macaca m., Homo sapiens s., Rattus n., Mus m. and Gallus g., Bos taurus, both NJ and UPGMA clustering show close affinity between clustering of Homo sapiens s. (Man) with Rattus n. (Rat), Mus m. species for the candidate genes, except in case of Lipin1 gene. The results support the functional similarity of these genes in physiological and biochemical process involving man and mouse/rat. Therefore, in understanding the complex etiology and treatment of the complex disease mouse/rate model is the best laboratory choice for experimentation.

Keywords: phylogeny, candidate gene of type-2 diabetes, CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG

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Authors: Mohammed Abdullah Basahi

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The objective of the present study was to identify the phylogenetic relationships and determine genetic diversity among Suaeda genotypes growing in Saudi Arabia and to find out whether these could be a potential source for genetic diversity. A set of nineteen genotypes was analyzed using twenty-four ISSR primers. Clear amplified polymorphic DNA products were obtained from the screening of twenty-four ISSR primers on nineteen genotypes that allowed selection of ten primers and the results were reproducible. Nineteen genotypes were revealed a unique profile with ten ISSR primers and thus it can be used for the DNA fingerprinting. Different primers produced a different level of polymorphism among the nineteen genotypes. The number of polymorphic bands per primer varied from 5 to 14 with an average of 8 bands per primer. The results revealed that the genotypes differed for ISSR markers. The genetic similarity based on Nei and Li’s ranged from 0.450 to 0.930. Cluster analysis was conducted based on ISSR data to group the Suaeda genotypes and to construct a dendrogram. Four groups can be distinguished by truncating the dendrogram at GS value of 0.54. ISSR markers showed high level of polymorphism among the genotypes examined. The present study indicates that ISSR markers could be successfully used in genetic characterization and diversity in Suaeda.

Keywords: suaeda, DNA fingerprinting, ISSR, Saudi Arabia

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Authors: Muhammad Fathurridho

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Grammar as a rule used by every language to be understood by everyone is always related to syntax and morphology. Arabic grammar is different with another languages’ grammars. It has more rules and difficulties. This paper aims to investigate and describe the English grammatical errors of machine translation systems in translating Arabic sentences, including declarative, exclamation, imperative, and interrogative sentences, specifically in year 2018 which can be supported with artificial intelligence’s role. The Arabic sample sentences which are divided into two; verbal and nominal sentence of several Arabic published texts will be examined as the source language samples. The translated sentences done by several popular online machine translation systems, including Google Translate, Microsoft Bing, Babylon, Facebook, Hellotalk, Worldlingo, Yandex Translate, and Tradukka Translate are the material objects of this research. Descriptive method that will be taken to finish this research will show the grammatical errors of English target language, and classify them. The conclusion of this paper has showed that the grammatical errors of machine translation results are varied and generally classified into morphological, syntactical, and semantic errors in all type of Arabic words (Noun, Verb, and Particle), and it will be one of the evaluations for machine translation’s providers to correct them in order to improve their understandable results.

Keywords: Arabic, Arabic-English translation, machine translation, grammatical errors

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Authors: Kishor Bhagwat, Gayatri Vyas

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Construction industry is earmarked with complex processes depending on the nature and scope of the project. In recent years, developments in this sector are remarkable and have resulted in both positive and negative impacts on the environment and human being. Sustainable construction can be looked upon as one of the solution to overcome the negative impacts since sustainable construction is a vast concept, which includes many parameters, and sometimes the use of multi-criteria decision making [MCDM] tools becomes necessary. The main objective of this study is to determine the weightage of sustainable building parameters with the help of MCDM tools. Questionnaire survey was conducted to examine the perspective of respondents on the importance of weights of the criterion, and the respondents were architects, green building consultants, and civil engineers. This paper presents an overview of research related to Indian and international green building rating systems and MCDM. The results depict that economy, environmental health, and safety, site selection, climatic condition, etc., are important parameters in sustainable construction.

Keywords: green building, sustainability, multi-criteria decision making method [MCDM], analytical hierarchy process [AHP], technique for order preference by similarity to an ideal solution [TOPSIS], entropy

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Authors: Thanyaporn Senarai, Rapeepun Vanichviriyakit, Shinji Miyata, Chihiro Sato, Prapee Sretarugsa, Wattana Weerachatyanukul, Ken Kitajima

Abstract:

In this study, we characterized a region-specific 250 kDa protein that was secreted of MSD fluid, which is believed to play dual functions in forming a spermatophoric wall for sperm physical protection, and in sperm membrane modification as part of sperm maturation process. The partial amino acid sequence and N-terminal sequencing revealed that the MSD-specific 250 kDa protein showed a high similarity with a plasma-rich protein, α-2 macroglobulin (α2M), so termed pp-α2M. This protein was a large glycoprotein contained predominantly mannose and GlcNAc. The expression of pp-α2M mRNA was detected in spermatic duct (SD), androgenic gland (AG) and hematopoietic tissue, while the protein expression was rather specific to the apical cytoplasm of MSD epithelium. The secretory pp-α2M in MSD fluid was acquired onto the MSD sperm membrane and was also found within the matrix of the acrosome. Distally, pp-α2M was removed from spermathecal sperm membrane, while its level kept constant in the sperm AC. Together the results indicate that pp-α2M is a 250 kDa region-specific secretory protein which plays roles in sperm physical protection and also acts as maturation factor in the P. pelagicus sperm.

Keywords: alpha-2 macroglobulin, blue swimming crab, sperm maturation, spermatic duct

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Authors: Amirmehdi Dokhanchi, Babak Ziyae

Abstract:

Studying and prioritizing effective factors on productivity of human resources through TOPSIS method is the main aim of the present research study. For this reason, while reviewing concepts existing in productivity, effective factors were studied. Managers, supervisors, staff and personnel of Tabriz Tractor Manufacturing Company are considered subject of this study. Of total individuals, 160 of them were selected through the application of random sampling method as 'subject'. Two questionnaires were used for collecting data in this study. The factors, which had the highest effect on productivity, were recognized through the application of software packages. TOPSIS method was used for prioritizing recognized factors. For this reason, the second questionnaire was put available to statistics sample for studying effect of each of factors towards predetermined indicators. Therefore, decision-making matrix was obtained. The result of prioritizing factors shows that existence of accurate organizational strategy, high level of occupational skill, application of partnership and contribution system, on-the-job-training services, high quality of occupational life, dissemination of appropriate organizational culture, encouraging to creativity and innovation, and environmental factors are prioritized respectively.

Keywords: productivity of human resources, productivity indicators, TOPSIS, prioritizing factors

Procedia PDF Downloads 325

Authors: Levi Corallo

Abstract:

Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

Procedia PDF Downloads 190

Authors: Pranjali Avinash Yadav-Deshmukh

Abstract:

Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results.

Keywords: disaster management, extraction technique, k-means, multi-document summarization, NLP, ontology, sentence extraction

Procedia PDF Downloads 380

Authors: Yuya Tanigami, Naoaki Yamanaka, Satoru Okamoto

Abstract:

Economic losses due to delays in traffic congestion regarding urban transportation networks have become a more serious social problem as traffic volume increases. Platooning has recently been attracting attention from many researchers to alleviate traffic jams, especially on the highway. On highways, platooning can have positive effects, such as reducing inter-vehicular distance and reducing air resistance. However, the impacts of platooning on urban roads have not been addressed in detail since traffic lights may break the platoons. In this study, we propose a platooning method using L2 norm and cosine similarity to form a platoon with highly similar routes. Also, we investigate the sorting method within a platoon according to each vehicle’s straightness. Our proposed sorting platoon method, which uses two lanes, eliminates Head of Line Blocking at the intersection and improves throughput at intersections. This paper proposes a cyber-physical system (CPS) approach to collaborative urban platoon control. We conduct simulations using the traffic simulator SUMO and the road network, which imitates Manhattan Island. Results from the SUMO confirmed that our method shortens the average travel time by 10-20%. This paper shows the validity of forming a platoon based on destination vectors and sorting vehicles within a platoon.

Keywords: CPS, platooning, connected car, vector correlation

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Authors: E. Fathallahi Aghdam, V. Hosseini

Abstract:

Environmental pollution is not limited to a specific region or country; that is why sustainable development, as a necessary process for improvement, pays attention to issues such as destruction of natural resources, degradation of biological system, global pollution, and climate change in the world, especially in the developing countries. According to the World Health Organization, as a developing city, Tehran (capital of Iran) is one of the most polluted cities in the world in terms of air pollution. In this study, three pollutants including particulate matter less than 10 microns, nitrogen oxides, and sulfur dioxide were evaluated in Tehran using data mining techniques and through Crisp approach. The data from 21 air pollution measuring stations in different areas of Tehran were collected from 1999 to 2013. Commercial softwares Clementine was selected for this study. Tehran was divided into distinct clusters in terms of the mentioned pollutants using the software. As a data mining technique, clustering is usually used as a prologue for other analyses, therefore, the similarity of clusters was evaluated in this study through analyzing local conditions, traffic behavior, and industrial activities. In fact, the results of this research can support decision-making system, help managers improve the performance and decision making, and assist in urban studies.

Keywords: data mining, clustering, air pollution, crisp approach

Procedia PDF Downloads 424