Search results for: encrypted traffic classification

Authors: Eric Mensah

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The effects of rapid urbanisation of tropical sub-Saharan developing cities on local and global climate are of great concern due to the negative impacts of Urban Heat Island (UHI) effects. The importance of urban parks, vegetative cover and forest reserves in these tropical cities have been undervalued with a rapid degradation and loss of these vegetative covers to urban developments which continue to cause an increase in daily mean temperatures and changes to local climatic conditions. Using Landsat data of the same months and period intervals, the spatial variations of land cover changes, temperature, and vegetation were examined to determine how vegetation improves local temperature and the effects of urbanisation on daily mean temperatures over the past 12 years. The remote sensing techniques of maximum likelihood supervised classification, land surface temperature retrieval technique, and normalised differential vegetation index techniques were used to analyse and create the land use land cover (LULC), land surface temperature (LST), and vegetation and non-vegetation cover maps respectively. Results from the study showed an increase in daily mean temperature by 0.80 °C as a result of rapid increase in urban area by 46.13 sq. km and loss of vegetative cover by 46.24 sq. km between 2005 and 2017. The LST map also shows the existence of UHI within the urban areas of Accra, the potential mitigating effects offered by the existence of forest and vegetative cover as demonstrated by the existence of cool islands around the Achimota ecological forest and University of Ghana botanical gardens areas.

Keywords: land surface temperature, climate, remote sensing, urbanisation

Procedia PDF Downloads 316

Authors: Safitri Nur Wulandari, M. Ivan Adi Perdana, Prathisto L. Panuntun Unggul, R. Dary Wira Mahadika

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In road construction on the soft soil, we need a soil improvement method to improve the soil bearing capacity of the land base so that the soil can withstand the traffic loads. Most of the land in Indonesia has a soft soil, where soft soil is a type of clay that has the consistency of very soft to medium stiff, undrained shear strength, Cu <0:25 kg/cm2, or the estimated value of NSPT <5 blows/ft. This study focuses on the analysis of the effect on preloading load (embarkment) to the amount of settlement ratio on the under of embarkment that will impact on the building cracks around of embarkment. The method used in this research is a superposition method for embarkment distribution on 27 locations with undisturbed soil samples at some borehole point in Java and Kalimantan, Indonesia. Then correlating the results of settlement plate monitoring on the field with Asaoka method. The results of settlement plate monitoring taken from an embarkment of Ahmad Yani airport in Semarang on 32 points. Where the value of Cc (index compressible) soil data based on some laboratory test results, while the value of Cc is not tested obtained from empirical formula Ardhana and Mochtar, 1999. From this research, the results of the field monitoring showed almost the same results with an empirical formulation with the standard deviation of 4% where the formulation of the empirical results of this analysis obtained by linear formula. Value empirical linear formula is to determine the effect of compression heap area as high as 4,25 m is 3,1209x + y = 0.0026 for the slope of the embankment 1: 8 for the same analysis with an initial height of embankment on the field. Provided that at the edge of the embankment settlement worth is not equal to 0 but at a quarter of embankment has a settlement ratio average 0.951 and at the edge of embankment has a settlement ratio 0,049. The influence areas around of embankment are approximately 1 meter for slope 1:8 and 7 meters for slope 1:2. So, it can cause the building cracks, to build in sustainable development.

Keywords: building cracks, influence area, settlement plate, soft soil, empirical formula, embankment

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Seongho Han, Dongkwan Kim

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The focus of regional innovation policies is shifting from a central government to local governments. The central government demands that regions enforce autonomous and responsible regional innovation policies and that regional governments seek for innovation policies fit for regional characteristics. However, the central government and local governments have not arrived yet at a conclusion on what innovation policies are appropriate for regional circumstances. In particular, even if each local government is trying to find regional innovation strategies that are based on the needs of a region, its innovation strategies turn out to be similar with those of other regions. This leads to a consequence that is inefficient not only at a national level, but also at a regional level. Existing researches on regional innovation types point out that there are remarkable differences in the types or characteristics of innovation among the regions of a nation. In addition they imply that there would be no expected innovation output in cases in which policies are enforced with ignoring such differences. This means that it is undesirable to enforce regional innovation policies under a single standard. This research, given this problem, aims to find out the characteristics and differences in innovation types among the regions in Korea and suggests appropriate policy implications by classifying such characteristics and differences. This research, given these objectives, classified regions in consideration of the various indicators that comprise the innovation suggested by existing related researches and illustrated policies based on such characteristics and differences. This research used recent data, mainly from 2012, and as a methodology, clustering analysis based on multiple factor analysis was applied. Supplementary researches on dynamically analyzing stability in regional innovation types, establishing systematic indicators based on the regional innovation theory, and developing additional indicators are necessary in the future.

Keywords: regional innovation policy, regional innovation type, region-based innovation, multiple factor analysis, clustering analysis

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Authors: Wann-Ming Wey

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In recent years, in addition to face the external threats such as energy shortages and climate change, traffic congestion and environmental pollution have become anxious problems for many cities. Considering private automobile-oriented urban development had produced many negative environmental and social impacts, the transit-oriented development (TOD) has been considered as a sustainable urban model. TOD encourages public transport combined with friendly walking and cycling environment designs, however, non-motorized modes help improving human health, energy saving, and reducing carbon emissions. Due to environmental changes often affect the planners’ decision-making; this research applies dynamic network process (DNP) which includes the time dependent concept to promoting friendly walking and cycling environmental designs as an advanced planning support system for environment improvements. This research aims to discuss what kinds of design strategies can improve a friendly walking and cycling environment under TOD. First of all, we collate and analyze environment designing factors by reviewing the relevant literatures as well as divide into three aspects of “safety”, “convenience”, and “amenity” from fifteen environment designing factors. Furthermore, we utilize fuzzy Delphi Technique (FDT) expert questionnaire to filter out the more important designing criteria for the study case. Finally, we utilized DNP expert questionnaire to obtain the weights changes at different time points for each design criterion. Based on the changing trends of each criterion weight, we are able to develop appropriate designing strategies as the reference for planners to allocate resources in a dynamic environment. In order to illustrate the approach we propose in this research, Taipei city as one example has been used as an empirical study, and the results are in depth analyzed to explain the application of our proposed approach.

Keywords: environment planning support systems, walking and cycling, transit-oriented development (TOD), dynamic network process (DNP)

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Authors: Anatoly Krivinsh

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The results of dissertation research "Preventing and combating corruption in public procurement" are presented in this publication. The study was conducted 2011 till 2013 in a Member State of the European Union, in the Republic of Latvia. Goal of the thesis is to explore corruption prevention and combating issues in public procurement sphere, to identify the prevalence rates, determinants and contributing factors and prevention opportunities in Latvia. In the first chapter the author analyses theoretical aspects of understanding corruption in public procurement, with particular emphasis on corruption definition problem, its nature, causes and consequences. A separate section is dedicated to the public procurement concept, mechanism and legal framework. In the first part of this work the author presents cognitive methodology of corruption in public procurement field, based on which the author has carried out an analysis of corruption situation in public procurement in Republic of Latvia. In the second chapter of the thesis, the author analyzes the problem of corruption in public procurement, including its historical aspects, typology and classification of corruption subjects involved, corruption risk elements in public procurement and their identification. During the development of the second chapter author's practical experience in public procurements was widely used. The third and fourth chapter deals with issues related to the prevention and combating corruption in public procurement, namely the operation of the concept, principles, methods and techniques, subjects in Republic of Latvia, as well as an analysis of foreign experience in preventing and combating corruption. The fifth chapter is devoted to the corruption prevention and combating perspectives and their assessment. In this chapter the author has made the evaluation of corruption prevention and combating measures efficiency in Republic of Latvia, assessment of anti-corruption legislation development stage in public procurement field in Latvia.

Keywords: prevention of corruption, public purchases, good governance, human rights

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Yu Chen, Xiaoguang Yang, Lin Lin

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Traffic engineering construction is an important infrastructure for economic and social development. In the process of construction and operation, the ability to make a correct evaluation of the project's environmental impact appears to be crucial to the rational operation of existing transportation projects, the correct development of transportation engineering construction and the adoption of corresponding measures to scientifically carry out environmental protection work. Most of the existing research work on ecological and environmental impact assessment is limited to individual aspects of the environment and less to the overall evaluation of the environmental system; in terms of research conclusions, there are more qualitative analyses from the technical and policy levels, and there is a lack of quantitative research results and quantitative and operable evaluation models. In this paper, a comprehensive analysis of the ecological and environmental impacts of transportation construction projects is conducted, and factors such as the accessibility of data and the reliability of calculation results are comprehensively considered to extract indicators that can reflect the essence and characteristics. The qualitative evaluation indicators were screened using the expert review method, the qualitative indicators were measured using the fuzzy statistics method, the quantitative indicators were screened using the principal component analysis method, and the quantitative indicators were measured by both literature search and calculation. An environmental impact evaluation index system with the general objective layer, sub-objective layer and indicator layer was established, dividing the environmental impact of the transportation construction project into two periods: the construction period and the operation period. On the basis of the evaluation index system, the index weights are determined using the hierarchical analysis method, and the individual indicators to be evaluated are dimensionless, eliminating the influence of the original background and meaning of the indicators. Finally, the thesis uses the above research results, combined with the actual engineering practice, to verify the correctness and operability of the evaluation method.

Keywords: transportation construction projects, ecological and environmental impact, analysis and evaluation, indicator evaluation system

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: Siripen Yiamjanya, Kevin Wongleedee

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This research paper aims to identify travel motivation by push and pull factors that affected decision making of international tourists in selecting Thailand as their destination choice. A total of 200 international tourists who traveled to Thailand during January and February, 2014 were used as the sample in this study. A questionnaire was employed as a tool in collecting the data, conducted in Bangkok. The list consisted of 30 attributes representing both psychological factors as “push- based factors” and destination factors as “pull-based factors”. Mean and standard deviation were used in order to find the top ten travel motives that were important determinants in the respondents’ decision making process to select Thailand as their destination choice. The finding revealed the top ten travel motivations influencing international tourists to select Thailand as their destination choice included [i] getting experience in foreign land; [ii] Thai food; [iii] learning new culture; [iv] relaxing in foreign land; [v] wanting to learn new things; [vi] being interested in Thai culture, and traditional markets; [vii] escaping from same daily life; [viii] enjoying activities; [ix] adventure; and [x] good weather. Classification of push- based and pull- based motives suggested that getting experience in foreign land was the most important push motive for international tourists to travel, while Thai food portrayed its highest significance as pull motive. Discussion and suggestions were also made for tourism industry of Thailand.

Keywords: decision making, destination choice, international tourist, pull factor, push factor, Thailand, travel motivation

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Authors: Iftikhar A. Tayubi, Tabrej Khan, Mansoor M. Alsubei, Fahad A. Alsaferi

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LIZTOXD provides a single source of high-quality information about proteinaceous lizard toxins that will be an invaluable resource for pharmacologists, neuroscientists, toxicologists, medicinal chemists, ion channel scientists, clinicians, and structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to explore the detail information of Lizard and toxin proteins. It includes common name, scientific name, entry id, entry name, protein name and length of the protein sequence. The utility of this database is that it can provide a user-friendly interface for users to retrieve the information about Lizard, toxin and toxin protein of different Lizard species. These interfaces created in this database will satisfy the demands of the scientific community by providing in-depth knowledge about Lizard and its toxin. In the next phase of our project we will adopt methodology and by using A MySQL and Hypertext Preprocessor (PHP) which and for designing Smart Draw. A database is a wonderful piece of equipment for storing large quantities of data efficiently. The users can thus navigate from one section to another, depending on the field of interest of the user. This database contains a wealth of information on species, toxins, toxins, clinical data etc. LIZTOXD resource that provides comprehensive information about protein toxins from lizard toxins. The combination of specific classification schemes and a rich user interface allows researchers to easily locate and view information on the sequence, structure, and biological activity of these toxins. This manually curated database will be a valuable resource for both basic researchers as well as those interested in potential pharmaceutical and agricultural applications of lizard toxins.

Keywords: LIZTOXD, MySQL, PHP, smart draw

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: Abdelkader Mendas

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The integration of MultiCriteria Decision Making (MCDM) approaches in a Geographical Information System (GIS) provides a powerful spatial decision support system which offers the opportunity to efficiently produce the land suitability maps for agriculture. Indeed, GIS is a powerful tool for analyzing spatial data and establishing a process for decision support. Because of their spatial aggregation functions, MCDM methods can facilitate decision making in situations where several solutions are available, various criteria have to be taken into account and decision-makers are in conflict. The parameters and the classification system used in this work are inspired from the FAO (Food and Agriculture Organization) approach dedicated to a sustainable agriculture. A spatial decision support system has been developed for establishing the land suitability map for agriculture. It incorporates the multicriteria analysis method ELECTRE Tri (ELimitation Et Choix Traduisant la REalité) in a GIS within the GIS program package environment. The main purpose of this research is to propose a conceptual and methodological framework for the combination of GIS and multicriteria methods in a single coherent system that takes into account the whole process from the acquisition of spatially referenced data to decision-making. In this context, a spatial decision support system for developing land suitability maps for agriculture has been developed. The algorithm of ELECTRE Tri is incorporated into a GIS environment and added to the other analysis functions of GIS. This approach has been tested on an area in Algeria. A land suitability map for durum wheat has been produced. Through the obtained results, it appears that ELECTRE Tri method, integrated into a GIS, is better suited to the problem of land suitability for agriculture. The coherence of the obtained maps confirms the system effectiveness.

Keywords: multicriteria decision analysis, decision support system, geographical information system, land suitability for agriculture

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: L. V. S. S. Phaneendra Bolem, S. Shankar

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Low Volume Rural Roads (LVRRs) constitute an integral component of the road system in all countries. These encompass all aspects of the social and economic development of rural communities. It is known that on a worldwide basis the number of low traffic roads far exceeds the length of high volume roads. Across India, 90% of the roads are LVRRs, and they often form the most important link in terms of providing access to educational, medical, recreational and commercial activities in local and regional areas. In the recent past, Government of India (GoI), with the initiation of the ambitious programme namely 'Pradhan Mantri Gram Sadak Yojana' (PMGSY) gave greater importance to LVRRs realizing their role in economic development of rural communities. The vast expansion of the road network has brought connectivity to the rural areas of the country. Further, it is noticed that due to increasing axle loads and lack of timely maintenance, is accelerated the process of deterioration of LVRRs. In addition to this due to limited budget for maintenance of these roads systematic and scientific approach in utilizing the available resources has been necessitated. This would enable better prioritization and ranking for the maintenance and make ‘all-weather roads’. Taking this into account the present study has adopted a multi-criteria approach. The multi-criteria approach includes parameters such as social, economic, environmental and pavement condition as the main criterion and some sub-criteria to find the best suitable parameters and their weight. For this purpose the expert’s opinion survey was carried out using Delphi Technique (DT) considering Likert scale, pairwise comparison and ranking methods and entire data was analyzed. Finally, this study developed the maintenance criterion considering the socio-economic, environmental and pavement condition parameters for effective maintenance of low volume roads based on the engineering judgment.

Keywords: Delphi technique, experts opinion survey, low volume rural road maintenance, multi criteria analysis

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Authors: Mahsa Ghafouri, Guita Farivarsadri

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In urban open spaces, furniture plays a crucial role in meeting various needs of the users over time. Furniture consists of elements that not only can facilitate physical needs individually but also fulfill social, psychological, and cultural demands on an urban scale. Creating adjustable urban spaces and using flexible furniture can provide the possibility of using urban spaces for a wide range of uses and activities and allow the engagement of users with distinct abilities and limitations in these activities. Flexibility in urban furniture can be seen as designing a number of modular components that are movable, expandable, adjustable, and changeable to accommodate various functions. Although there is a great amount of research related to flexibility and its distinct insights into achieving spaces that can cope with changing demands, this fundamental issue is often neglected in the design of urban furniture. However, in the long term, to address changing public needs over time, it can be logical to bring this quality into the design process to make spaces that can be sustained for a long time. This study aims to first introduce diverse kinds of flexible furniture that can be designed for urban public spaces and then to realize how this flexible furniture can improve the quality of public open spaces and social interaction and make them more adaptable over time and, as a result, achieve social sustainability. This research is descriptive and is mainly based on an extensive literature review and the analysis and classification of existing examples around the world. This research tends to illustrate various kinds of approaches that can help designers create flexible furniture to enhance the sustainability and quality of urban open spaces and, in this way, act as a guide for urban designers in this respect.

Keywords: flexible furniture, flexible design, urban open spaces, adaptability, moveability, social sustainability

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Authors: Mohammad Maeiyat, Ali Maeiyat Ivatlou, Rasul Fani Khiavi, Abouzar Maeiyat Ivatlou, Parya Maeiyat

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Purpose: Considering the fact that visual impairment is still one of the countries health problem, this study was conducted to determine the causes of blindness and low vision in visually impaired membership of Ardabil Province welfare organization. Methods: The present study which was based on descriptive and national-census, that carried out in visually impaired population supported by welfare organization in all urban and rural areas of Ardabil Province in 2013 and Collection of samples lasted for 7 months. The subjects were inspected by optometrist to determine their visual status (blindness or low vision) and then referred to ophthalmologist in order to discover the main causes of visual impairment based on the international classification of diseases version 10. Statistical analysis of collected data was performed using SPSS software version 18. Results: Overall, 403 subjects with mean age of years participated in this study. 73.2% were blind, 26.8 % were low vision and according gender grouping 60.50 % of them were male, 39.50 % were female that divided into three groups with the age level of lower than 15 (11.2%) 15 to 49 (76.7%), and 50 and higher (12.1%). The age range was 1 to 78 years. The causes of blindness and low vision were in descending order: optic atrophy (18.4%), retinitis pigmentosa (16.8%), corneal diseases (12.4%), chorioretinal diseases (9.4%), cataract (8.9%), glaucoma (8.2%), phthisis bulbi (7.2%), degenerative myopia (6.9%), microphtalmos ( 4%), amblyopia (3.2%), albinism (2.5%) and nistagmus (2%). Conclusion: in this study the main causes of visual impairments were optic atrophy and retinitis pigmentosa, thus specific prevention plans can be effective in reducing the incidence of visual disabilities.

Keywords: blindness, low vision, welfare, ardabil

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Authors: Van Trieu, Shouhuai Xu, Yusheng Feng

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Tracking attack trajectories can be difficult, with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs) due to the current IDSs having some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event possibly cause the other event to happen. Because of this, it is important to investigate new methods capable of performing the tracking of attack trajectories task quickly with less attack information and dependency on IDSs, in order to prioritize actions during incident responses. This paper proposes a two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack events that can cause another event to occur in the system. Technically, given the time series of malicious events, the framework extracts events with useful features, such as attack time and port number, to apply to the conditional independent tests to detect the relationship between attack events. Using the academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs. It would cost expert human analysts a significant time if possible. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 85% of causal pairs have the average time difference between the causal and effect events in both computed and observed data within 5 minutes. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.24 seconds to 5 minutes, it is long enough to be used to design a prevention protocol to block those attacks.

Keywords: causality, multilevel graph, cyber-attacks, prediction

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Authors: Ivelise M. Strozberg, Juliana Sales Frota, Lucas de Oliveira Vale

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The state of Ceara, located in the northeast region of Brazil, is abundant in lateritic soil which has been usually discarded due to its lack of agricultural potential while materials of similar nature have been used as constituents of housing constructive elements in many parts of the world, such as India and Portugal, for decades. Since many of the semi-arid housing conditions in the state of Ceara fail to meet the minimum criteria regarding comfort and safety requirements, this research proposed to study the Ceara lateritic soil and the possibility of its use as a sustainable building block constituent for social housings, collaborating to the improvement of the region living conditions. In order to achieve this objective, soil samples were collected from five different locations within the specific region, three of which presented lateritic nature, being characterized according to the Unified Soil Classification System and the MCT methodology, which is a Brazilian methodology developed during the 80’s that aimed to better describe and approach tropical soils, its characterization and behavior. Two of these samples were used to build two different miniature block prototypes, which were manually molded, heated at low temperatures -( < 300 ºC) in order to save energy and lessen the CO₂ high emission rate common in traditional burning methods- and then submitted to load tests. Among the soils tested, the one with the highest degree of laterization and greater presence of fines constituted the block with the best performance in terms of flexural strength tensions, presenting resistance gains when heated at increasing temperatures, which can indicate that this type of soil has potential towards being used as constructing material.

Keywords: constructive blocks, lateritic soil, MCT methodology, sustainability

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Authors: Majeda Hammoud

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Background: Anemia during pregnancy increases the risk of delivery by cesarean section, low birth weight, preterm birth, perinatal mortality, stillbirth, and maternal mortality. In this study, we aimed to assess the prevalence of anemia in pregnant women and its associated factors in the Kuwait birth cohort study. Methods: The Kuwait birth cohort (N=1108) was a prospective cohort study in which pregnant women were recruited in the third trimester. Data were collected through personal interviews with mothers who attend antenatal care visits, including data on socio-economic status and lifestyle factors. Blood samples were taken after the recruitment to measure multiple laboratory indicators. Clinical data were extracted from the medical records by a clinician including data on comorbidities. Anemia was defined as having Hemoglobin (Hb) <110 g/L with further classification as mild (100-109 g/L), moderate (70-99 g/L), or severe (<70 g/L). Predictors of anemia were classified as underlying or direct factors, and logistic regression was used to investigate their association with anemia. Results: The mean Hb level in the study group was 115.21 g/L (95%CI: 114.56- 115.87 g/L), with significant differences between age groups (p=0.034). The prevalence of anemia was 28.16% (95%CI: 25.53-30.91%), with no significant difference by age group (p=0.164). Of all 1108 pregnant women, 8.75% had moderate anemia, and 19.40% had mild anemia, but no pregnant women had severe anemia. In multivariable analysis, getting pregnant while using contraception, adjusted odds ratio (AOR) 1.73(95%CI:1.01-2.96); p=0.046 and current use of supplements, AOR 0.50 (95%CI: 0.26-0.95); p=0.035 were significantly associated with anemia (underlying factors). From the direct factors group, only iron and ferritin levels were significantly associated with anemia (P<0.001). Conclusion: Although the severe form of anemia is low among pregnant women in Kuwait, mild and moderate anemia remains a significant health problem despite free access to antenatal care.

Keywords: anemia, pregnancy, hemoglobin, ferritin

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Authors: Danutė Jablonskytė-Raščė, Audronė MankevičIenė, Laura Masilionytė

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During the period 2009–2015, in Joniškėlis Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry, the effect of ecologic fertilizers Ekoplant, bio-activators Biokal 01 and Terra Sorb Foliar and their combinations on the formation of the productivity elements, grain yield and quality of winter wheat, spelt (Triticum spelta L.), and common wheat (Triticum aestivum L.) was analysed in ecological agro-system. The soil under FAO classification – Endocalcari-Endo-hypogleyic-Cambisol. In a clay loam soil, ecological fertilizer produced from sunflower hull ash and this fertilizer in combination with plant extracts and bio-humus exerted an influence on the grain yield of spelt and common wheat and their mixture (increased the grain yield by 10.0%, compared with the unfertilized crops). Spelt grain yield was by on average 16.9% lower than that of common wheat and by 11.7% lower than that of the mixture, but the role of spelt in organic production systems is important because with no mineral fertilization it produced grains with a higher (by 4%) gluten content and exhibited a greater ability to suppress weeds (by on average 61.9% lower weed weight) compared with the grain yield and weed suppressive ability of common wheat and mixture. Spelt cultivation in a mixture with common wheat significantly improved quality indicators of the mixture (its grain contained by 2.0% higher protein content and by 4.0% higher gluten content than common wheat grain), reduced disease incidence (by 2-8%), and weed infestation level (by 34-81%).

Keywords: common and spelt-wheat, ecological fertilizers, bio-activators, productivity elements, yield, quality

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Authors: Vladyslav Povoroznyuk, Anna Musiienko, Nataliia Zaverukha, Roksolana Povoroznyuk

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Along with the global aging of population, the number of people with somatic diseases is increasing, including such interrelated pathologies as obesity, osteoarthritis (OA) and osteoporosis (OP). The objective of the study is to examine the connection between body mass index (BMI), OA and bone mineral density (BMD) of lumbar spine, femoral neck and trabecular bone score (TBS) in postmenopausal women with OA. We have observed 359 postmenopausal women (50-89 years old) and divided them into four groups by age: 50-59 yrs, 60-69 yrs, 70-79 yrs and over 80 years old. In addition, according to the American College of Rheumatology (ACR) Clinical classification criteria for knee and hip OA, we divided them into 2 groups: group I – 117 females with symptomatic OA (including 89 patients with knee OA, 28 patients with hip OA) and group II –242 women with a normal functional activity of large joints. Analysis of data was performed taking into account their BMI, classified by World Health Organization (WHO). Diagnosis of obesity was established when BMI was above 30 kg/m². In woman with obesity, a symptomatic OA was detected in 44 postmenopausal women (41.1%), a normal functional activity of large joints - in 63 women (58.9%). However, in women with normal BMI – 73 women, who account for 29.0% of cases, a symptomatic OA was detected. According to a chi-squared (χ2) test, a significantly higher level of BMI was detected in postmenopausal women with OA (χ2 = 5.05, p = 0.02). Women with a symptomatic OA had a significantly higher BMD of lumbar spine compared with women who had a normal functional activity of large joints. No significant differences of BMD of femoral necks or TBS were detected in either the group with OA or with a normal functional activity of large joints.

Keywords: bone mineral density, body mass index, obesity, overweight, postmenopausal women, osteoarthritis

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Authors: Marcin Szlachetka, Mateusz Paszko, Grzegorz Baranski

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This paper discusses the AVL EXCITE Designer simulation research into loads applied to main and crank bearings in the compression-ignition two-stroke engine. There was created a model of engine lubrication system which covers the part of this system related to particular nodes of a bearing system, i.e. a connection of main bearings in an engine block with a crankshaft, a connection of crank pins with a connecting rod. The analysis focused on the load given as a distribution of hydrodynamic oil film pressure corresponding different values of radial internal clearance. There was also studied the impact of gas force on minimal oil film thickness in main and crank bearings versus crankshaft rotational speed. Our model calculates oil film parameters, an oil film pressure distribution, an oil temperature change and dimensions of bearings as well as an oil temperature distribution on surfaces of bearing seats. Accordingly, it was possible to select, for example, a correct clearance for each of the node bearings. The research was performed for several values of engine crankshaft speed ranging from 800 RPM to 4000 RPM. Bearing oil pressure was changed according to engine speed ranging between 1 bar and 5 bar and an oil temperature of 90°C. The main bearing clearances made initially for the calculation and research were: 0.015 mm, 0.025 mm, 0.035 mm, 0.05 mm, 0.1 mm. The oil used for the research corresponded the SAE 5W-40 classification. The paper presents the selected research results referring to certain specific operating points and bearing radial internal clearances. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK ‘PZL-KALISZ’ S.A. and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: crank bearings, diesel engine, oil film, two-stroke engine

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

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Authors: Omer Altaf, Liam Wotherspoon, Rolando Orense

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The Wairau Plains are located in the north-east of the South Island of New Zealand in the region of Marlborough. The region is cut by many active crustal faults such as the Wairau, Awatere, and Clarence faults, which give rise to frequent seismic events. This paper presents the preliminary results of the overall project in which liquefaction-induced ground damage maps are developed in the Wairau Plains based on the Ministry of Business, Innovation and Employment NZ guidance. A suite of maps has been developed in relation to the level of details that was available to inform the liquefaction hazard mapping. Maps at the coarsest level of detail make use of regional geologic information, applying semi-quantitative criteria based on geological age, design peak ground accelerations and depth to the water table. The next level of detail incorporates higher resolution surface geomorphologic characteristics to better delineate potentially liquefiable and non-liquefiable deposits across the region. The most detailed assessment utilised CPT sounding data to develop ground damage response curves for areas across the region and provide a finer level of categorisation of liquefaction vulnerability. Linking these with design level earthquakes defined through NZGS guidelines will enable detailed classification to be carried out at CPT investigation locations, from very low through to high liquefaction vulnerability. To update classifications to these detailed levels, CPT investigations in geomorphic regions are grouped together to provide an indication of the representative performance of the soils in these areas making use of the geomorphic mapping outlined above.

Keywords: hazard, liquefaction, mapping, seismicity

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Authors: Ashrafosadat Mousavi Laer, Elaheh Moravej

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The art of binding was simple and elementary at the beginning of Islam. This art thrived gradually and continued its development as an independent art. Identification of the binding techniques and used materials in covers and investigation of the arrays give us indexes for the better identification of different doctrines and methods of that time. The catalogers of the manuscripts usually pay attention to four items: gender, color, art elegances, injury, and exquisiteness of the cover. The criterion for classification of the covers is their art nature and gender. 15th century AD (9th century AH) was the period of the binding art development in which the most beautiful covers were produced by the so-called method of ‘burning’. At 16th century AD (10th century AH), in Safavid era, art changed completely and a fundamental evolution occurred in the technique and method of binding. The greatest change in this art was the extensive use of stamp that was made mostly of steel and copper. Theses stamps were presses against leather. These covers were called ‘beat’. In this paper, writing and bookbinding of about 32 Shahnamehs of Safavid era available in the Iranian libraries and museums are studied. An analytical-statistical study shows that four methods have been used including beat, burning, mosaic, and oily. 69 percent of the covers of these copies are cardboards with a leathery coating (goatskin) and have been produced by burning and beat methods. Its reasons are that these two methods have been common methods in Safavid era and performing them was only feasible on leather and the most desirable and commonly used leather of that time was goatskin which was the best option for cover legend durability and preserving the book and it was more durable because it had been made of goat skin. In addition, it had prepared a suitable opportunity for the binding artist’s creativity and innovation.

Keywords: Shahnameh, Safavid era, bookbinding, beat cover, burning cover

Procedia PDF Downloads 233

Authors: Jonah Kenei, Elisha Opiyo

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Electronic health records, as a repository of patient information, is nowadays the most commonly used technology to record, store and review patient clinical records and perform other clinical tasks. However, the accurate identification and retrieval of relevant information from clinical records is a difficult task due to the unstructured nature of clinical documents, characterized in particular by a lack of clear structure. Therefore, medical practice is facing a challenge thanks to the rapid growth of health information in electronic health records (EHRs), mostly in narrative text form. As a result, it's becoming important to effectively manage the growing amount of data for a single patient. As a result, there is currently a requirement to visualize electronic health records (EHRs) in a way that aids physicians in clinical tasks and medical decision-making. Leveraging text visualization techniques to unstructured clinical narrative texts is a new area of research that aims to provide better information extraction and retrieval to support clinical decision support in scenarios where data generated continues to grow. Clinical datasets in electronic health records (EHR) offer a lot of potential for training accurate statistical models to classify facets of information which can then be used to improve patient care and outcomes. However, in many clinical note datasets, the unstructured nature of clinical texts is a common problem. This paper examines the very issue of getting raw clinical texts and mapping them into meaningful structures that can support healthcare professionals utilizing narrative texts. Our work is the result of a collaborative design process that was aided by empirical data collected through formal usability testing.

Keywords: classification, electronic health records, narrative texts, visualization

Procedia PDF Downloads 115