Search results for: data comparison

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

Procedia PDF Downloads 374

Authors: Omid Amini

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Nowadays, technology plays the most important role in the life of human beings because people use technology to share data and to communicate with each other, but the challenge is the security of this data. For instance, as more people turn to technology in the world, more data is generated, and more hackers try to steal or infiltrate data. In addition, the data is under the control of the central authority, which can trigger the challenge of losing information and changing information; this can create widespread anxiety for different people in different communities. In this paper, we sought to investigate Blockchain technology that can guarantee information security and eliminate the challenge of central authority access to information. Now a day, people are suffering from the current voting system. This means that the lack of transparency in the voting system is a big problem for society and the government in most countries, but blockchain technology can be the best alternative to the previous voting system methods because it removes the most important challenge for voting. According to the results, this research can be a good start to getting acquainted with this new technology, especially on the security part and familiarity with how to use a voting system based on blockchain in the world. At the end of this research, it is concluded that the use of blockchain technology can solve the major security problem and lead to a secure and transparent election.

Keywords: blockchain, technology, security, information, voting system, transparency

Procedia PDF Downloads 132

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 167

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 159

Authors: K. W. Nam, B. J. Kim, S. J. Lee

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Along with the development of network infrastructures, such as high-speed Internet and mobile environment, the explosion of multimedia data is expanding the range of multimedia services beyond voice and data services. Amid this flow, research is actively being done on the creation, management, and transmission of metadata on digital content to provide different services to users. This paper proposes a system for the insertion, storage, and retrieval of metadata about digital content. The metadata server with Binary XML was implemented for efficient storage space and retrieval speeds, and the transport data size required for metadata retrieval was simplified. With the proposed system, the metadata could be inserted into the moving objects in the video, and the unnecessary overlap could be minimized by improving the storage structure of the metadata. The proposed system can assemble metadata into one relevant topic, even if it is expressed in different media or in different forms. It is expected that the proposed system will handle complex network types of data.

Keywords: video, multimedia, metadata, editing tool, XML

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Authors: Luís Pina

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The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

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Authors: Sinisa J. Vukicevic

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Edmonton is currently one of the youngest North American cities and has achieved significant growth over the past 40 years. Strong urban shift requires a new approach to how the city is envisioned, planned, and built. This approach is evidence-based scenario development, and an urban growth model was a key support tool in framing Edmonton development strategies, developing urban policies, and assessing policy implications. The urban growth model has been developed using the Metronamica software platform. The Metronamica land use model evaluated the dynamic of land use change under the influence of key development drivers (population and employment), zoning, land suitability, and land and activity accessibility. The model was designed following the Big City Moves ideas: become greener as we grow, develop a rebuildable city, ignite a community of communities, foster a healing city, and create a city of convergence. The Big City Moves were converted to three development scenarios: ‘Strong Central City’, ‘Node City’, and ‘Corridor City’. Each scenario has a narrative story that expressed scenario’s high level goal, scenario’s approach to residential and commercial activities, to transportation vision, and employment and environmental principles. Land use demand was calculated for each scenario according to specific density targets. Spatial policies were analyzed according to their level of importance within the policy set definition for the specific scenario, but also through the policy measures. The model was calibrated on the way to reproduce known historical land use pattern. For the calibration, we used 2006 and 2011 land use data. The validation is done independently, which means we used the data we did not use for the calibration. The model was validated with 2016 data. In general, the modeling process contain three main phases: ‘from qualitative storyline to quantitative modelling’, ‘model development and model run’, and ‘from quantitative modelling to qualitative storyline’. The model also incorporates five spatial indicators: distance from residential to work, distance from residential to recreation, distance to river valley, urban expansion and habitat fragmentation. The major finding of this research could be looked at from two perspectives: the planning perspective and technology perspective. The planning perspective evaluates the model as a tool for scenario development. Using the model, we explored the land use dynamic that is influenced by a different set of policies. The model enables a direct comparison between the three scenarios. We explored the similarities and differences of scenarios and their quantitative indicators: land use change, population change (and spatial allocation), job allocation, density (population, employment, and dwelling unit), habitat connectivity, proximity to objects of interest, etc. From the technology perspective, the model showed one very important characteristic: the model flexibility. The direction for policy testing changed many times during the consultation process and model flexibility in applying all these changes was highly appreciated. The model satisfied our needs as scenario development and evaluation tool, but also as a communication tool during the consultation process.

Keywords: urban growth model, scenario development, spatial indicators, Metronamica

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Ratna Farida Soenarto, Anas Alatas, Made Ryan Kharmayani

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Background: Conventional ultrafiltration (CUF) system was shown to be helpful in reducing anti-inflammatory mediators for patients who underwent open heart surgery. Additionally, modified ultrafiltration (MUF) has been shown to reduce anti-inflammatory mediators further while reducing interstitial fluid volume at the same time. However, there has been minimal data concerning the efficacy of combining both ultrafiltration methods. This study aims to compare inflammation marker, vascular resistance, and cardiac index on CUF+MUF patients with CUF only patients undergoing open heart surgery. Method: This is a single blind randomized controlled trial on patients undergoing open heart surgery between June 2021 - October 2021 in CiptoMangunkusumo National Referral Hospital and Jakarta Heart Hospital. Patients wererandomized using block randomization into modified ultrafiltration following conventional ultrafiltration (CUF+MUF) and conventional ultrafiltration (CUF) only. Outcome assessed in this study were 24-hoursinterleukin-6 levels, systemic vascular resistance (SVR), pulmonary vascular resistance (PVR), and cardiac index. Results: A total of 38patients were included (19 CUF+MUF and 19 CUF subjects). There was no difference in postoperative IL-6 level between groups (p > 0.05).No difference in PVR was observed between groups.Higher difference in SVR was observed in CUF+MUF group (-646 vs. -261dyn/s/cm-5, p < 0.05). Higher cardiac index was observed on CUF+MUF group (0.93 vs. 0.48, p < 0.05). Conclusion: Patients undergoing open heart surgery with modified ultrafiltration following conventional ultrafiltration had similar systemic inflammatory response and better cardiac response than those having conventional ultrafiltration.

Keywords: open-heart, CUF, MUF, SVR, PVR, IL-6

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Authors: Sujit K. Basak

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This paper presents a comparison of the reference management software between Zotero and Mendeley and the results were drawn by comparing the two software’s. The novelty of this paper is the comparative analysis of the software and it has shown that Mendeley can import more information from the Google Scholar for the researchers. This finding can help to know researchers to use the reference management software.

Keywords: analysis, comparative analysis, zotero, researchers, Mendeley

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Authors: Thanakorn Thanawat, Nomjit Nualnetr

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Background and Purpose: In Thailand, rice farmers have the most prevalence of low back pain when compared with other manual workers. Exercises have been suggested to be a principal part of treatment programs for low back pain. However, the programs should be tailored to an individual’s readiness to change categorized by a behavioral approach. This study aimed to evaluate a difference between the responses of rice farmers with chronic low back pain who received an exercise program integrated with the transtheoretical model of behavior change (TTM) and those of the comparison group regarding severity of pain and trunk muscle endurance. Materials and Methods: An 8-week exercise program was conducted to rice farmers with chronic low back pain who were randomized to either the TTM (n=62, 52 woman and 10 men, mean age ± SD 45.0±5.4 years) or non-TTM (n=64, 53 woman and 11 men, mean age ± SD 44.7±5.4 years) groups. All participants were tested for their severity of pain and trunk (abdominal and back) muscle endurance at baseline (week 0) and immediately after termination of the program (week 8). Data were analysed by using descriptive statistics and student’s t-tests. The results revealed that both TTM and non-TTM groups could decrease their severity of pain and improve trunk muscle endurance after participating in the 8-week exercise program. When compared with the non-TTM group, however, the TTM showed a significantly greater increase in abdominal muscle endurance than did the non-TTM (P=0.004, 95% CI -12.4 to -2.3). Conclusions and Clinical Relevance: An exercise program integrated with the TTM could provide benefits to rice farmers with chronic low back pain. Future studies with a longitudinal design and more outcome measures such as physical performance and quality of life are suggested to reveal further benefits of the program.

Keywords: chronic low back pain, transtheoretical model, rice farmers, exercise program

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Authors: M. Knezevic, V. Marecic, M. Ozanic, I. Kelava, M. Mihelcic, M. Santic

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Francisella tularensis is a highly infectious, gram-negative intracellular bacterium and the causative agent of tularemia. The bacterium has been isolated from more than 250 wild species, including protozoa cells. Since Francisella is very virulent and persists in the environment for years, the aim of this study was to investigate whether Acanthamoeba castellanii-grown F. novicida exhibits an alteration in the resistance to disinfectants. It has been shown by other intracellular pathogens, including Legionella pneumophila that bacteria grown in amoeba exhibit more resistance to disinfectants. However, there is no data showing Francisella viability behaviour after intracellular life cycle in A. castellani. In this study, the bacterial suspensions of A. castellanii-grown or in vitro-grown Francisella were treated with three different disinfectants, and the bacterial viability after disinfection treatment was determined by a colony-forming unit (CFU) counting method, transmission electron microscopy (TEM), fluorescence microscopy as well as the leakage of intracellular fluid. Our results have shown that didecyldimethylammonium chloride (DDAC) combined with isopropyl alcohol was the most effective in bacterial killing; all in vitro-grown and A. castellanii-grown F. novicida were killed after only 10s. Surprisingly, in comparison to in vitro-grown bacteria, A. castellanii-grown F. novicida was more sensitive to decontamination by the benzalkonium chloride combined with DDAC and formic acid and the polyhexamethylene biguanide (PHMB). We can conclude that the tested disinfectants exhibit antimicrobial activity by causing a loss of structural organization and integrity of the Francisella cell wall and membrane and the subsequent leakage of the intracellular contents. Finally, the results of this study clearly demonstrate that Francisella grown in A. castellanii had become more susceptible to many disinfectants.

Keywords: Acanthamoeba, disinfectant, Francisella, sensitivity

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Mohammadreza Ashouri, Majid Bayatian

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Analysis of fire data is a way for the implementation of any plan to improve the level of safety in cities. Such an analysis is able to reveal signs of changes in a given period and can be used as a measure of safety. The information of about 66,341 fires (from 2002 to 2012) released by Tehran Safety Services and Fire-Fighting Organization and data on the population and the number of households provided by Tehran Municipality and the Statistical Yearbook of Iran were extracted. Using the data, the fire changes, the rate of injuries, and mortality rate were determined and analyzed. The rate of injuries and mortality rate of fires per one million population of Tehran were 59.58% and 86.12%, respectively. During the study period, the number of fires and fire stations increased by 104.38% and 102.63%, respectively. Most fires (9.21%) happened in the 4th District of Tehran. The results showed that the recorded fire data have not been systematically planned for fire prevention since one of the ways to reduce injuries caused by fires is to develop a systematic plan for necessary actions in emergency situations. To determine a reliable source for fire prevention, the stages, definitions of working processes and the cause and effect chains should be considered. Therefore, a comprehensive statistical system should be developed for reported and recorded fire data.

Keywords: fire statistics, fire analysis, accident prevention, Tehran

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Authors: Christiana Ene Ogwuche, Joseph Amupitan, George Ndukwe, Rachael Ayo

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Maesobotrya barteri (Baill), belonging to the family Euphorbiaceae, is a medicinal plant growing widely in tropical Africa. The Aerial plant parts of Maesobotrya barteri (Baill) were collected fresh from Orokam, Ogbadibo local Government of Benue State, Nigeria in July 2013. Taxonomical identification was done by Mallam Musa Abdullahi at the Herbarium unit of Biological Sciences Department, ABU, Zaria, Nigeria. Pulverized aerial parts of Maesobotrya barteri (960g) was exhaustively extracted successively using petroleum ether, chloroform, ethyl acetate and methanol and concentrated in the rotary evaporator at 40°C. The Petroleum ether extract had the second highest activity against test microbes from preliminary crude microbial screenings. The Petroleum ether extract was subjected to phytochemical studies, antimicrobial analysis and column chromatography (CC). The column chromatography yielded fraction PE, which was further purified using preparative thin layer chromatography to give PE1. The structure of the isolated compound was established using 1-D NMR and 2-D NMR spectroscopic analysis and by direct comparison with data reported in literature was confirmed to be a mixture, an isomer of Betulinic acid and Oleanolic acid, both with the molecular weight (C₃₀H₄₈O₃). The bioactivity of this compound was carried out using some clinical pathogens and the activity compared with standard drugs, and this was found to be comparable with the standard drug.

Keywords: Maesobotrya barteri, medicinal plant, bioactivity, petroleum spirit extract, butellinic acid, oleanilic acid

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Authors: Osk Dagsdottir

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Background—Gait classifying allows clinicians to differentiate gait patterns into clinically important categories that help in clinical decision making. Reliable comparison of gait data between normal and patients requires knowledge of the gait parameters of normal children's specific age group. However, there is still a lack of the gait database for normal children of different ages. Objectives—This study aims to investigate the kinematics of the lower limb joints during gait for normal children in different age groups. Methods—Fifty-three normal children (34 boys, 19 girls) were recruited in this study. All the children were aged between 5 to 16 years old. Age groups were defined as three types: young child aged (5-7), child (8-11), and adolescent (12-16). When a participant agreed to take part in the project, their parents signed a consent form. Vicon® motion capture system was used to collect gait data. Participants were asked to walk at their comfortable speed along a 10-meter walkway. Each participant walked up to 20 trials. Three good trials were analyzed using the Vicon Plug-in-Gait model to obtain parameters of the gait, e.g., walking speed, cadence, stride length, and joint parameters, e.g., joint angle, force, moments, etc. Moreover, each gait cycle was divided into 8 phases. The range of motion (ROM) angle of pelvis, hip, knee, and ankle joints in three planes of both limbs were calculated using an in-house program. Results—The temporal-spatial variables of three age groups of normal children were compared between each other; it was found that there was a significant difference (p < 0.05) between the groups. The step length and walking speed were gradually increasing from young child to adolescent, while cadence was gradually decreasing from young child to adolescent group. The mean and standard deviation (SD) of the step length of young child, child and adolescent groups were 0.502 ± 0.067 m, 0.566 ± 0.061 m and 0.672 ± 0.053 m, respectively. The mean and SD of the cadence of the young child, child and adolescent groups were 140.11±15.79 step/min, 129±11.84 step/min, and a 115.96±6.47 step/min, respectively. Moreover, it was observed that there were significant differences in kinematic parameters, either whole gait cycle or each phase. For example, RoM of knee angle in the sagittal plane in the whole cycle of young child group is (65.03±0.52 deg) larger than child group (63.47±0.47 deg). Conclusion—Our result showed that there are significant differences between each age group in the gait phases and thus children walking performance changes with ages. Therefore, it is important for the clinician to consider the age group when analyzing the patients with lower limb disorders before any clinical treatment.

Keywords: action research, creative learning, mathematics education, professional development

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Authors: Nam Don Kim, Jungho Moon, Tae Yun Chung

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This paper proposes an Internet of Things (IoT) based virtualization platform for providing smart tourism services. The virtualization platform provides a consistent access interface to various types of data by naming IoT devices and legacy information systems as pathnames in a virtual file system. In the other words, the IoT virtualization platform functions as a middleware which uses the metadata for underlying collected data. The proposed platform makes it easy to provide customized tourism information by using tourist locations collected by IoT devices and additionally enables to create new interactive smart tourism services focused on the tourist locations. The proposed platform is very efficient so that the provided tourism services are isolated from changes in raw data and the services can be modified or expanded without changing the underlying data structure.

Keywords: internet of things (IoT), IoT platform, serviceplatform, virtual file system (VSF)

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Ahmed Noor Al-qayyim, Barlas Özden Çağlayan

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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures.

Keywords: damage detection, optimization, signals processing, structural health monitoring, two points–condensation

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Abiodun Afolabi, Rufus Adesoji Adedoyin

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This study investigated and compared the effects of gym-based exercise programme (GEBP) and audio-visual guided home-based exercise programme (AVGHBEP) on selected Anthropometric variables (Weight (W), Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Thigh Circumference (TC), Waist-Hip-Ratio (WHR), Waist-Height-Ratio (WHtR), Waist-Thigh-Ratio (WTR), Biceps Skinfold Thickness (BSFT), Triceps Skinfold Thickness (TSFT), Suprailliac Skinfold Thickness (SISFT), Subscapular Skinfold Thickness (SSSFT) and Percent Body Fat (PBF)); and Cardiovasular variables (Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and Heart Rate (HR)) of overweight and obese students of Federal College of Education (Special), Oyo, Oyo State, Nigeria, with a view to providing information and evidence for GBEP and AVGHBEP in reducing overweight and obesity for promoting cardiovascular fitness. Eighty overweight and obese students (BMI ≥ 25 Kg/m²) were involved in this pretest-posttest quasi experimental study. Participants were randomly assigned into GBEP (n = 40) and AVGBBEP (n = 40) groups. Anthropometric and cardiovascular variables were measured using a weighing scale, height meter, tape measure, skinfold caliper and electronic sphygmomanometer following standard protocols. GBEP and AVGHBEP were implemented following a circuit training (aerobic and resistance training) pattern with a duration of 40-60 minutes, thrice weekly for twelve weeks. GBEP consisted of gymnasium supervised exercise programme while AVGHBEP is a Visual Display guided exercise programme conducted at the home setting. Data were analyzed by Descriptive and Inferential Statistics. The mean ages of the participants were 22.55 ± 2.55 and 23.65 ± 2.89 years for the GBEP group and AVGHBEP group, respectively. Findings showed that in the GBEP group, there were significant reductions in anthropometric variables and adiposity measures of Weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHtR, and PBF at week 12 of the study. Similarly, in the AVGHBEP group, there were significant reductions in Weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHtR and PBF at the 12th week of intervention. Comparison of the effects of GEBP and AVGHBEP on anthropometric variables and measures of adiposity showed that there was no significant difference between the two groups in weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHR, WHtR, WTR and PBF between the two groups at week 12 of the study. Furthermore, findings on the effects of exercise on programmes on cardiovascular variables revealed that significant reductions occurred in SBP in GBEP group and AVGHBEP group respectively. Comparison of the effects of GBEP and AVGHBEP on cardiovascular variables showed that there was no significant difference in SBP, DBP and HR between the two groups at week 12 of the study. It was concluded that the Audio-Visual Guided Home-based Exercise Programme was as effective as the Gym-Based Exercise Programme in causing a significant reduction in anthropometric variables and body fat among college students who are overweight and obese over a period of twelve weeks. Both Gymnasium-Based Exercise Programme and Audio-Visual Guided Home-Based Exercise Programme led to significant reduction in Systolic Blood Pressure over a period of weeks. Audio-Visual Guided Home-Based Exercise Programme can, therefore, be used as an alternative therapy in the non-pharmacological management of people who are overweight and obese.

Keywords: gym-based exercises, audio-visual guided home-based exercises, anthropometric parameters, cardiovascular parameters, overweight students, obese students

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Authors: Ivan Balachin, Geanette Polanco, Jiang Xingliang, Hu Qin

Abstract:

The problem of global warming raised up interest towards renewable energy sources. To reduce cost of wind energy is a challenge. Before building of wind park conditions such as: average wind speed, direction, time for each wind, probability of icing, must be considered in the design phase. Operation values used on the setting of control systems also will depend on mentioned variables. Here it is proposed a procedure to be include in the evaluation of the performance of a wind turbine, based on the amplitude of wind changes, the number of changes and their duration. A generic study case based on actual data is presented. Data analysing techniques were applied to model the power required for yaw system based on amplitude and data amount of wind changes. A theoretical model between time, amplitude of wind changes and angular speed of nacelle rotation was identified.

Keywords: field data processing, regression determination, wind turbine performance, wind turbine placing, yaw system losses

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Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

Abstract:

The design in most counties should be inevitably carried out by their native code such as Iran. Since the Iranian concrete code does not exist in structural design software, most engineers in this country analyze the structures using commercial software but design the structural members manually. This point motivated us to make a communication between Iranian code and some other well-known ones to create facility for the engineers. Finally, this paper proposes the so-called interpretation charts which help specify the position of Iranian code in comparison of some worldwide ones.

Keywords: beam, concrete code, strength, interpretation charts

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Authors: Masoud Charkhabi

Abstract:

We examine trade theory with this motivation. The full set of World Trade Organization data are organized into country-year pairs, each treated as a different entity. Topological Data Analysis reveals that among the 16 region and 240 region-year pairs there exists in fact a distinguishable group of region-period pairs. The generally accepted periods of shifts from dissimilar-dissimilar to similar-similar trade in goods among regions are examined from this new perspective. The period breaks are treated as cumulative and are flexible. This type of all-subsets analysis is motivated from computer science and is made possible with Lossy Compression and Graph Theory. The results question many patterns in similar-similar to dissimilar-dissimilar trade. They also show indications of economic shifts that only later become evident in other economic metrics.

Keywords: econometrics, globalization, network science, topological data, analysis, trade theory, visualization, world trade

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Authors: Divya Vohora, Md. Jamir Anwar

Abstract:

Antiepileptic drugs (AEDs)-induced adverse consequences on bone are now well recognized. Despite this, there is limited data on the effect of anti-osteoporotic therapies on AEDs-induced bone loss. Both phenytoin (PHT) and sodium valproate (SVP) inhibit human aromatase enzyme and stimulate microsomal catabolism of oestrogens. Estrogen deficiency states are known to reduce the deposition of transforming growth factor-β (TGF-β3), a bone matrix protein, having anti-osteoclastic property. Thus, an attempt was made to investigate the effect of raloxifene, a selective oestrogen receptor modulator, in comparison with CVD supplementation, on PHT and SVP-induced alterations in bone in mice. Further, the effect of raloxifene on seizures and on the antiepileptic efficacy of AEDs was also investigated. Swiss strains of female mice were treated with PHT (35 mg/kg, p.o.) and SVP (300 mg/kg, p.o.) for 120 days to induce bone loss as evidenced by reduced bone mineral density (BMD) and altered bone turnover markers in lumbar bones (alkaline phosphatase, tartarate resistant acid phosphatase, hydroxyproline) and urine (calcium). The bone loss was accompanied by reduced serum estradiol levels and bone TGF-β3 content. Preventive and curative treatment with raloxifene ameliorated bony alterations and was more effective than CVD. Deprived estrogen levels (that in turn reduced lumbar TGF-β3 content) following PHT and SVP, thus, might represent one of the various mechanisms of AEDs-induced bone loss. Raloxifene preserved the bony changes without interfering with their antiepileptic efficacy, and hence raloxifene could be a potential therapeutic option in the management of PHT and SVP-induced bone disease if clinically approved.

Keywords: antiepileptic drugs, osteoporosis, raloxifene, TGF-β3

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Authors: Abdalla Mansour, Dan Brown, Adel Helmy, Rikin Trivedi, Mathew Guilfoyle

Abstract:

Introduction: Diagnosing delayed cerebral ischaemia (DCI) following aneurysmal subarachnoid haemorrhage (SAH) can be challenging, particularly in poor-grade patients. Objectives: This study sought to assess the value of routine CTP in identifying (or excluding) DCI and in guiding management. Methods: Eight-year retrospective neuroimaging study at a large UK neurosurgical centre. Subjects included a random sample of adult patients with confirmed aneurysmal SAH that had a CTP scan during their inpatient stay, over a 8-year period (May 2014 - May 2022). Data collected through electronic patient record and PACS. Variables included age, WFNS scale, aneurysm site, treatment, the timing of CTP, radiologist report, and DCI management. Results: Over eight years, 916 patients were treated for aneurysmal SAH; this study focused on 466 patients that were randomly selected. Of this sample, 181 (38.84%) had one or more CTP scans following brain aneurysm treatment (Total 318). The first CTP scan in each patient was performed at 1-20 days following ictus (median 4 days). There was radiological evidence of DCI in 83, and no reversible ischaemia was found in 80. Findings were equivocal in the remaining 18. Of the 103 patients treated with clipping, 49 had DCI radiological evidence, in comparison to 31 of 69 patients treated with endovascular embolization. The remaining 9 patients are either unsecured aneurysms or non-aneurysmal SAH. Of the patients with radiological evidence of DCI, 65 had a treatment change following the CTP directed at improving cerebral perfusion. In contrast, treatment was not changed for (61) patients without radiological evidence of DCI. Conclusion: CTP is a useful adjunct to clinical assessment in the diagnosis of DCI and is helpful in identifying patients that may benefit from intensive therapy and those in whom it is unlikely to be effective.

Keywords: SAH, vasospasm, aneurysm, delayed cerebral ischemia

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Authors: H. Assaedi, F. U. A. Shaikh, I. M. Low

Abstract:

This paper presents the effects of mixing procedures on mechanical properties of flyash-based geopolymer matrices containing nanosilica (NS) at 0.5%, 1.0%, 2.0%, and 3.0% by wt.. Comparison is made with conventional mechanical dry-mixing of NS with flyash and wet-mixing of NS in alkaline solutions. Physical and mechanical properties are investigated using X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM). Results show that generally the addition of NS particles enhanced the microstructure and improved flexural and compressive strengths of geopolymer nanocomposites. However, samples prepared using dry-mixing approach demonstrate better physical and mechanical properties than wet-mixing of NS.

Keywords: geopolymer, nano-silica, dry mixing, wet mixing, physical properties, mechanical properties

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

Abstract:

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

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Authors: Eric Kumar, Erica Cochran, Zhiang Zhang, Wei Liang, Ronak Mody

Abstract:

The objective of this article was to create a modelling framework that exposes the marginal costs of shifting workloads across geographically distributed data-centers. Geographical distribution of internet services helps to optimize their performance for localized end users with lowered communications times and increased availability. However, due to the geographical and temporal effects, the physical embodiments of a service's data center infrastructure can vary greatly. In this work, we first identify that the sources of variances in the physical infrastructure primarily stem from local weather conditions, specific user traffic profiles, energy sources, and the types of IT hardware available at the time of deployment. Second, we create a traffic simulator that indicates the IT load at each data-center in the set as an approximator for user traffic profiles. Third, we implement a framework that quantifies the global level energy demands using building energy models and the traffic profiles. The results of the model provide a time series of energy demands that can be used for further life cycle analysis of internet services.

Keywords: data-centers, energy, life cycle, network simulation

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

Abstract:

This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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