Search results for: raw complex data

Authors: Bahadir Aydin

Abstract:

Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Malraj Bharatha Kiriella

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The purpose of this paper is to investigate the determinants of tourism foreign direct investment (TFDI) to selected developing countries during 1978-2017. The study used pooled panel data to estimate an econometric model. The findings show that market size and institutional barriers are determining factors for TFDI in countries, while other variables of positive country conditions, FDI-related government policy, tourism-related infrastructure and labor conditions are insignificant. The result shows that institutional effects are positive, while market size negatively affects TFDI inflows. The research is limited to eight developing countries. The results can be used to support government policy on TFDI. The paper makes the following contributions: First, it provides important insight and understanding into the TFDI decision-making process in developing countries. Second, both TFDI theory and evidence are minimal, and an econometric model developed on the basis of available literature has been empirically tested.

Keywords: determinants, developing countries, FDI in tourism, panel data

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Authors: Junling Li, Fang Meng, Yichun Zhang

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Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection.

Keywords: visual saliency, background probability, boundary knowledge, background priors

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Authors: Eid Alharbi, Yaser Miaji, Saeed Alzahrani

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The importance of fruit quality and freshness is potential in today’s life. Most recent studies show and automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic convener belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300 nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950 nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950 nm region the online sorting system was constructed.

Keywords: mechatronics design, NIR, fruit quality, spectroscopic technology

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Authors: A. Orlov, V. Rachkov

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The future of Nuclear Energetics is seen in fast reactors with inherent safety working in the closed nuclear fuel cycle. The concept of inherent safety, which lies in deterministic elimination of the most severe accidents due to inherent properties of the reactor rather than through building up engineered barriers, is a cornerstone of success in ensuring safety and economic efficiency of future Nuclear Energetics. The focus of this paper is one of the key elements of inherent safety - the lead coolant of a nuclear reactor. Advantages of lead coolant for reactor application, influence on safety are reviewed. BREST-OD-300 fast reactor, currently being developed in Russia withing the “Proryv” Project utilizes lead coolant and a special set of measures and devices, called technology of lead coolant that ensures safe operation in a wide range of temperatures. Here these technological elements are reviewed, and current progress in their development is discussed.

Keywords: BREST-OD-300. , fast reactor, inherent safety, lead coolant

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Authors: Hsiang-Wen Tang, Cheng-Ying Lo

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In this study, the static behavior of super elliptical Winkler plate is analyzed by applying the double side approach method. The lack of information about super elliptical Winkler plates is the motivation of this study and we use the double side approach method to solve this problem because of its superior ability on efficiently treating problems with complex boundary shape. The double side approach method has the advantages of high accuracy, easy calculation procedure and less calculation load required. Most important of all, it can give the error bound of the approximate solution. The numerical results not only show that the double side approach method works well on this problem but also provide us the knowledge of static behavior of super elliptical Winkler plate in practical use.

Keywords: super elliptical winkler plate, double side approach method, error bound, mechanic

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Authors: Xiang Li, Kui Chen, Bin Wu, Min Zhou

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Sclareolide is made from sclareol by oxidiative synthesis and subsequent crystallization, while the crystallization mother liquor still contains 15%~30%wt of sclareolide to be reclaimed. With the reaction material of sclareol is provided as plant extract, many sorts of complex impurities exist in the mother liquor. Due to the difficulty in recycling sclareolide after solvent recovery, it is common practice for the factories to discard the mother liquor, which not only results in loss of sclareolide, but also contributes extra environmental burden. In this paper, a process based on adsorption and elution has been presented for recycling of sclareolide from mother liquor. After pretreatment of the crystallization mother liquor by HZ-845 resin to remove parts of impurities, sclareolide is adsorbed by HZ-816 resin. The HZ-816 resin loaded with sclareolide is then eluted by elution solvent. Finally, the eluent containing sclareolide is concentrated and fed into the crystallization step in the process. By adoption of the recycle from mother liquor, total yield of sclareolide increases from 86% to 90% with a stable purity of the final sclareolide products maintained.

Keywords: sclareolide, resin, adsorption, chromatography

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Konrad Steyn, Jacques Laubscher

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South African urban design solutions are mostly aligned with European and North American models that are often not appropriate in addressing some of this country’s challenges such as multiculturalism and decaying urban areas. Sustainable urban redevelopment in South Africa should be comprehensive in nature, sensitive in its manifestation, and should be robust and inclusive in order to achieve social relevance. This paper argues that the success of an urban design intervention is largely dependent on the public’s perceptions and expectations, and the way people participate in shaping their environments. The concept of sustainable urbanism is thus more comprehensive than – yet should undoubtedly include – methods of construction, material usage and climate control principles. The case study is a central element of this research paper. 725 Arcadia Street in Pretoria, was originally commissioned as a food market structure. A starkly contrasting existing modernist adjacent building forms the morphological background. Built in 1969, it is a valuable part of Pretoria’s modernist fabric. It was realised early on that the project should not be a mere localised architectural intervention, but rather an occasion to revitalise the neighbourhood through urban regeneration. Because of the complex and comprehensive nature of the site and rich cultural diversity of the area, a multi-faceted approach seemed the most appropriate response. The methodology for collating data consisted of a combination of literature reviews (regarding the historic original fauna and flora and current plants, observation (frequent site visits) and physical surveying on the neighbourhood level (physical location, connectivity to surrounding landmarks as well as movement systems and pedestrian flows). This was followed by an exploratory design phase, culminating in the present redevelopment proposal. Since built environment interventions are increasingly based on generalised normative guidelines, an approach focusing of urban acupuncture could serve as an alternative. Celebrating the specific urban condition, urban acupuncture offers an opportunity to influence the surrounding urban fabric and achieve urban renewal through physical, social and cultural mediation.

Keywords: neighbourhood, urban renewal, South African urban design solutions, sustainable urban redevelopment

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Ludwig Conistabile, Martina Falappi

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This study was born out of the necessity to delve into and understand the intricate world of tattoos, a millenary art that lays its foundation, although sometimes complex, for effective non-verbal communication. What is most astonishing about a phenomenon such as irreversibly branding the skin, which at first glance may appear superficial, especially in the eyes of those who do not fully understand its meaning, is the fact that it is actually one of the most enduring forms of communication: body modification through tattoos or ornamental dyes indicated the wearer's membership in a particular ethnic group, social class or religious community. Even within crime-based aggregations, tattoos had (and still have) the purpose of conveying one's affiliation to a particular group, thus making one's criminal identity known. The profession of a language and cultural mediator teaches that it is essential to be able to read between the lines to grasp all those aspects that words alone do not convey. Tattooing, therefore, becomes a tool of analysis and a language that is not secondary to be taken into account for an in-depth reading of the reality that surrounds us and of cultures different from our own.

Keywords: tattoo, cultural tattoos, tattooing, non verbal communication

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Authors: Thamer Mahmmoud Muhammad Al Jbarat

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Building Performance Simulation plays a crucial role in optimizing energy efficiency, comfort, and sustainability in buildings. This paper explores the integration of Artificial Intelligence techniques into Building Performance Simulation to enhance accuracy, efficiency, and adaptability. The synthesis of Artificial Intelligence and Building Performance Simulation offers promising avenues for addressing complex building dynamics, optimizing energy consumption, and improving occupants' comfort. This paper examines various Artificial Intelligence methodologies and their applications in Building Performance Simulation, highlighting their potential benefits and challenges. Through a comprehensive review of existing literature and case studies, this paper presents insights into the current state, future directions, and implications of Artificial Intelligence driven Building Performance Simulation on the built environment

Keywords: artificial intelligence, building performance, energy efficiency, building performance simulation, buildings sustainability, built environment.

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Authors: Hantian Wu, Bo Huang, Yuan Zeng

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Divisions between wealthy and poor, private and public landscapes are propagated by the increasing economic inequality of cities. While these are the spatial reflections of larger social issues and problems, urban design can at least employ spatial techniques that promote more inclusive rather than exclusive, overlapping rather than segregated, interlinked rather than disconnected landscapes. Indeed, the type of edge or border between urban landscapes plays a critical role in the way the environment is perceived. China experiences rapid urbanization, which poses unpredictable environmental challenges. The urban green cover and water body are under changes, which highly relevant to resident wealth and happiness. However, very limited knowledge and data on their rapid changes are available. In this regard, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understating the driving forces of urban landscape changes can be a significant contribution for urban planning and studying. High-resolution remote sensing data has been widely applied to urban management in China. The map of urban land use map for the entire China of 2018 with 10 meters resolution has been published. However, this research focuses on the large-scale and high-resolution remote sensing land use but does not precisely focus on the seasonal change of urban covers. High-resolution remote sensing data has a long-operation cycle (e.g., Landsat 8 required 16 days for the same location), which is unable to satisfy the requirement of monitoring urban-landscape changes. On the other hand, aerial-remote or unmanned aerial vehicle (UAV) sensing are limited by the aviation-regulation and cost was hardly widely applied in the mega-cities. Moreover, those data are limited by the climate and weather conditions (e.g., cloud, fog), and those problems make capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Particularly, during the rainy season, no data are available even for Sentinel Satellite data with 5 days interval. Many natural events and/or human activities drive the changes of urban covers. In this case, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understanding the mechanism of urban landscape changes can be a significant contribution for urban planning and studying. This project aims to use the high spatiotemporal fusion of remote sensing data to create short-cycle, high-resolution remote sensing data sets for exploring the high-frequently urban cover changes. This research will enhance the long-term monitoring applicability of high spatiotemporal fusion of remote sensing data for the urban landscape for optimizing the urban management of landscape border to promoting the inclusive of the urban landscape to all communities.

Keywords: urban land cover changes, remote sensing, high spatiotemporal fusion, urban management

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Khoshnaw Khalid Hama Saleh, Ergun Ercelebi

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Increasingly complex modern power systems require stability, especially for transient and small disturbances. Transient stability plays a major role in stability during fault and large disturbance. This paper compares a power system stabilizer (PSS) and static Var compensator (SVC) to improve damping oscillation and enhance transient stability. The effectiveness of a PSS connected to the exciter and/or governor in damping electromechanical oscillations of isolated synchronous generator was tested. The SVC device is a member of the shunt FACTS (flexible alternating current transmission system) family, utilized in power transmission systems. The designed model was tested with a multi-machine system consisting of four machines six bus, using MATLAB/SIMULINK software. The results obtained indicate that SVC solutions are better than PSS.

Keywords: FACTS, MATLAB/SIMULINK, multi-machine system, PSS, SVC, transient stability

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: Lin Ji

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The 1923 HSBC Building is one of the landmarks of Shanghai's Bund complex and is described as "one of the finest buildings from the Suez Canal to the Bering Strait". Mr George Leopold Wilson of Palmer&Turner and his design team combine the latest British design taste with Chinese elements and bring the high standard of London manufacturing to Shanghai. This paper reviews the establishment background and construction process of the Hongkong and Shanghai Bank Corporation in Shanghai, and analyzes the characteristics of various styles and ornament of HSBC. At the same time, using the research method of iconography, compared with Britain's exploration of modern design mode in the early 20th century, we can deeply understand how this "monument of world commerce and prosperity" realizes the identity construction of its financial empire in the Far East in the perfect combination of practicality and artistry.

Keywords: early 20-century Shanghai, the bund, the HSBC building, classical styles, ornament, identity construction

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Authors: Aliyah Dosani, Kerri Alderson

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Two different simulations using standardized patients were developed to reinforce content and foster undergraduate nursing students’ practice and development of interpersonal skills in difficult clinical situations in the classroom. The live actor simulations focused on fostering interpersonal skills, traditionally considered by students to be simple and easy. However, seemingly straightforward interactions can be very stressful, particularly in women’s complex social/emotional situations. Supporting patients in these contexts is fraught with complexity and high emotion, requiring skillful support, assessment and intervention by a registered nurse. In this presentation, the personal and professional perspectives of the development, incorporation, and execution of the live actor simulations will be discussed, as well as the inclusion of student perceptions, and the learning gained by the involved faculty.

Keywords: adult learning, interpersonal skill development, simulation learning, teaching and learning

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Authors: S. Vanaja, P. Panneerselvam, S. Santhanakarthikeyan

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Cloud Computing is a latest trend in Libraries. Entering in to cloud services, Librarians can suit the present information handling and they are able to satisfy needs of the knowledge society. Libraries are now in the platform of universalizing all its information to users and they focus towards clouds which gives easiest access to data and application. Cloud computing is a highly scalable platform promising quick access to hardware and software over the internet, in addition to easy management and access by non-expert users. In this paper, we discuss the cloud’s features and its potential applications in the library and information centers, how cloud computing actually works is illustrated in this communication and how it will be implemented. It discuss about what are the needs to move to cloud, process of migration to cloud. In addition to that this paper assessed the practical problems during migration in libraries, advantages of migration process and what are the measures that Libraries should follow during migration in to cloud. This paper highlights the benefits and some concerns regarding data ownership and data security on the cloud computing.

Keywords: cloud computing, cloud-service, cloud based-ILS, cloud-providers, discovery service, IaaS, PaaS, SaaS, virtualization, Web scale access

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

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Authors: Khalid A. Al-Afandy, El-Sayyed El-Rabaie, Osama Salah, Ahmed El-Mhalaway

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This paper presents a high secure data hiding technique using image cropping and Least Significant Bit (LSB) steganography. The predefined certain secret coordinate crops will be extracted from the cover image. The secret text message will be divided into sections. These sections quantity is equal the image crops quantity. Each section from the secret text message will embed into an image crop with a secret sequence using LSB technique. The embedding is done using the cover image color channels. Stego image is given by reassembling the image and the stego crops. The results of the technique will be compared to the other state of art techniques. Evaluation is based on visualization to detect any degradation of stego image, the difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio of stego image (PSNR), and the embedding algorithm CPU time. Experimental results ensure that the proposed technique is more secure compared with the other traditional techniques.

Keywords: steganography, stego, LSB, crop

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Authors: Natalya G. Filimonova, Mariya G. Ozerova, Irina N. Ermakova

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The long-term strategy of the economic development of Russia up to 2030 is based on the concept of sustainable growth. The determining factor of such development is complex changes in the economic system which may be achieved by making progressive changes in its structure. The structural changes determine the character and the direction of economic development, as well as they include all elements of this system without exception, and their regulated character ensures the most rapid aim achievement. This article has discussed the industrial structure of the agriculture in Russia. With the use of the system of indexes, the article has determined the directions, intensity, and speed of structural shifts. The influence of structural changes on agricultural production development has been found out. It is noticed that the changes in the industrial structure are synchronized with the changes in the organisation and economic structure. Efficiency assessment of structural changes allowed to trace the efficiency of structural changes and elaborate the main directions for agricultural policy improvement.

Keywords: Russian agricultural sectors, sectoral structure, organizational and economic structure, structural changes

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Authors: Elke Mackensen

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Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: Bulelani Ngamntwini, Liezel Cilliers

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South Africa has one of the highest prevalence of obese people on the African continent. Forty-six percent of the adults in South Africa are physically inactive. Fitness applications can be used to increase physical inactivity. However, the uptake of mobile fitness applications in South Africa has been found to be poor due to usability challenges with the technology. The study developed a usability framework to influence the intention to use mobile fitness applications in South Africa. The study made use of a positivistic approach to collect data. A questionnaire was used to collect quantitative data from 377 respondents that have used mobile fitness applications in the past. A response rate of 80.90% was recorded. To analyse the data, the Pearson correlation was used to determine the relationships between the various hypotheses. There are four usability factors, efficiency, effectiveness, satisfaction, and learnability, which contribute to the intention of users to make use of mobile fitness applications. The study, therefore, recommends that for a mobile fitness application to be successful, these four factors must be considered and incorporated by developers when designing the applications.

Keywords: obese, overweight, physical inactivity, mobile fitness application, usability factors

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Authors: Yiding Qu, Svetlana V. Komarova

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Chemokines acting through their cognate chemokine receptors guide the directional migration of the cell along the chemokine gradient. Several chemokine receptors were recently identified as non-signaling (decoy), based on their ability to bind the chemokine but produce no measurable signal in the cell. The function of these decoy receptors is not well understood. We examined the expression of a decoy receptor CCRL1 and a signaling receptor that binds to the same ligands, CCR7, in prostate cancer using publically available microarray data (www.oncomine.org). The expression of both CCRL1 and CCR7 increased in an approximately half of prostate carcinoma samples and the majority of metastatic cancer samples compared to normal prostate. Moreover, the expression of CCRL1 positively correlated with the expression of CCR7. These data suggest that CCR7 and CCRL1 can be used as clinical markers for the early detection of transformation from carcinoma to metastatic cancer. In addition, these data support our hypothesis that the non-signaling chemokine receptors actively stimulate cell migration.

Keywords: bioinformatics, cell migration, decoy receptor, meta-analysis, prostate cancer

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Authors: Stefan Bradu, Daniel Siegel, Jameson Loyal, Andrea Leaf, Alana Kurtti, Usha Alapati, Jared Jagdeo

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Compared with all other variants of nodular melanoma, patients with polypoid nodular melanoma have the lowest 5-year survival rate. The pathophysiology and management of polypoid melanoma are scarcely reported in the literature. Although surgical excision is the cornerstone of melanoma management, treatment of polypoid melanoma is complicated by several negative prognostic factors, including early metastasis. This report demonstrates the successful treatment of a rapidly developing red nodular polypoid melanoma with metastasis using surgery and adjuvant nivolumab in a SARS-CoV-2-positive patient who delayed seeking care due to the COVID-19 pandemic. In addition to detailing the successful treatment approach, the immunosuppressive effects of SARS-2-CoV and its possible contribution to the rapid progression of polypoid melanoma are discussed. This case highlights the complex challenges of melanoma diagnosis and management during the COVID-19 pandemic.

Keywords: covid-19, dermatology, immunotherapy, melanoma, nivolumab

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Authors: Jaechun No

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This paper focuses on I/O optimizations of N-hybrid (New-Form of hybrid), which provides a hybrid file system space constructed on SSD and HDD. Although the promising potentials of SSD, such as the absence of mechanical moving overhead and high random I/O throughput, have drawn a lot of attentions from IT enterprises, its high ratio of cost/capacity makes it less desirable to build a large-scale data storage subsystem composed of only SSDs. In this paper, we present N-hybrid that attempts to integrate the strengths of SSD and HDD, to offer a single, large hybrid file system space. Several experiments were conducted to verify the performance of N-hybrid.

Keywords: SSD, data section, I/O optimizations, hybrid system

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

Abstract:

Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

Procedia PDF Downloads 194