Search results for: statistical correlations

Authors: Abdel Rodríguez, Isis Bonet, Ricardo Grau, María M. García

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In this paper we designed and implemented a new ensemble of classifiers based on a sequence of classifiers which were specialized in regions of the training dataset where errors of its trained homologous are concentrated. In order to separate this regions, and to determine the aptitude of each classifier to properly respond to a new case, it was used another set of classifiers built hierarchically. We explored a selection based variant to combine the base classifiers. We validated this model with different base classifiers using 37 training datasets. It was carried out a statistical comparison of these models with the well known Bagging and Boosting, obtaining significantly superior results with the hierarchical ensemble using Multilayer Perceptron as base classifier. Therefore, we demonstrated the efficacy of the proposed ensemble, as well as its applicability to general problems.

Keywords: classifiers, delegation, ensemble

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Authors: Oleg Demchenko

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The aim of the present study is to analyze empirical researches on the social resources dimension of occupational status attainment process and relate them to the rational choice approach. The analysis suggests that the existing data on the strength of ties aspect of social resources is insufficient and does not allow any implication concerning rational actor-s behavior. However, the results concerning work relation aspect are more encouraging.

Keywords: Social resources, status attainment, rational choice, weak ties, work-related ties.

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Authors: Archit Yajnik

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Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: Hidden Markov model, Viterbi algorithm, POS tagging, natural language processing.

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Authors: Ie-jung Choi

Abstract:

Since the feasibility study of R&D programs have been initiated for efficient public R&D investments, year 2008, feasibility studies have improved in terms of precision. Although experience related to these studies of R&D programs have increased to a certain point, still methodological improvement is required. The feasibility studies of R&D programs are consisted of various viewpoints, such as technology, policy, and economics. This research is to provide improvement methods to the economic perspective; especially the cost estimation process of R&D activities. First of all, the fundamental concept of cost estimation is reviewed. After the review, a statistical and econometric analysis method is applied as empirical analysis. Conclusively, limitations and further research directions are provided.

Keywords: Cost Estimation, R&D Program, Feasibility AnalysisStudy.

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Authors: M. Giannopoulou, I. Roukounis, A. Roukouni.

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Geographical Information Systems are an integral part of planning in modern technical systems. Nowadays referred to as Spatial Decision Support Systems, as they allow synergy database management systems and models within a single user interface machine and they are important tools in spatial design for evaluating policies and programs at all levels of administration. This work refers to the creation of a Geographical Information System in the context of a broader research in the area of influence of an under construction station of the new metro in the Greek city of Thessaloniki, which included statistical and multivariate data analysis and diagrammatic representation, mapping and interpretation of the results.

Keywords: Databases, Geographical information systems (GIS), Land-use planning, Metro stations

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Authors: H. W. Ker

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Longitudinal data typically have the characteristics of changes over time, nonlinear growth patterns, between-subjects variability, and the within errors exhibiting heteroscedasticity and dependence. The data exploration is more complicated than that of cross-sectional data. The purpose of this paper is to organize/integrate of various visual-graphical techniques to explore longitudinal data. From the application of the proposed methods, investigators can answer the research questions include characterizing or describing the growth patterns at both group and individual level, identifying the time points where important changes occur and unusual subjects, selecting suitable statistical models, and suggesting possible within-error variance.

Keywords: Data exploration, exploratory analysis, HLMs/LMEs, longitudinal data, visual-graphical methods.

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Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: Forecasting, Gaussian process, modeling, wind power.

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Authors: Dipankaj G Medhi, Juri Dakua

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Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Keywords: Collaborative Filtering, Content Based Filtering, Intelligent Agent, Level of Interest, Recommendation System.

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Authors: M.Rajasimman, S.Subathra

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In this work, statistical experimental design was applied for the optimization of medium constituents for Gentamicin production by Micromsonospora echinospora subs pallida (MTCC 708) in a batch reactor and the results are compared with the ANN predicted values. By central composite design, 50 experiments are carried out for five test variables: Starch, Soya bean meal, K2HPO4, CaCO3 and FeSO4. The optimum condition was found to be: Starch (8.9,g/L), Soya bean meal (3.3 g/L), K2HPO4 (0.8 g/L), CaCO3 (4 g/L) and FeSO4 (0.03 g/L). At these optimized conditions, the yield of gentamicin was found to be 1020 mg/L. The R2 values were found to be 1 for ANN training set, 0.9953 for ANN test set, and 0.9286 for RSM.

Keywords: Gentamicin, optimization, Micromonospora echinospora, ANN, RSM

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

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: Y. Bentoutou, A.M. Si Mohammed

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This paper presents a review of an 8-year study on radiation effects in commercial memory devices operating within the main on-board computer system OBC386 of the Algerian microsatellite Alsat-1. A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in these commercial memories shows that the typical SEU rate at alsat-1's orbit is 4.04 × 10-7 SEU/bit/day, where 98.6% of these SEUs cause single-bit errors, 1.22% cause double-byte errors, and the remaining SEUs result in multiple-bit and severe errors.

Keywords: Radiation effects, error detection and correction, satellite computer, small satellite mission.

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Authors: Yuan He, Yupin Luo, Dongcheng Hu

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In this paper, we propose a novel variational method for unsupervised texture segmentation. We use a Gabor filter bank to extract texture features. Some of the filtered channels form a multidimensional Gaborian feature space. To avoid deforming contours directly in a vector-valued space we use a Gaussian mixture model to describe the statistical distribution of this space and get the boundary and region probabilities. Then a framework of geodesic active regions is applied based on them. In the end, experimental results are presented, and show that this method can obtain satisfied boundaries between different texture regions.

Keywords: Texture segmentation, Gabor filter, snakes, Geodesicactive regions

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Authors: Eduardo Romero, Marcelo Costamagna, Gabriela Peretti, Carlos Marqués

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This work evaluates the ability of OBT for detecting parametric faults in continuous-time filters. To this end, we adopt two filters with quite different topologies as cases of study and a previously reported statistical fault model. In addition, we explore the behavior of the test schemes when a particular test condition is changed. The new data reported here, obtained from a fault simulation process, reveal a lower performance of OBT not observed in previous work using single-deviation faults, even under the change in the test condition.

Keywords: Testing, analog fault simulation, analog filter test, oscillation based test.

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Authors: P. Bountris, E. Farantatos, N. Apostolou

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Autofluorescence (AF) bronchoscopy is an established method to detect dysplasia and carcinoma in situ (CIS). For this reason the “Sotiria" Hospital uses the Karl Storz D-light system. However, in early tumor stages the visualization is not that obvious. With the help of a PC, we analyzed the color images we captured by developing certain tools in Matlab®. We used statistical methods based on texture analysis, signal processing methods based on Gabor models and conversion algorithms between devicedependent color spaces. Our belief is that we reduced the error made by the naked eye. The tools we implemented improve the quality of patients' life.

Keywords: Bronchoscopy, digital image processing, lung cancer, texture analysis.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya

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In the present world, predicting rainfall is considered to be an essential and also a challenging task. Normally, the climate and rainfall are presumed to have non-linear as well as intricate phenomena. For predicting accurate rainfall, we necessitate advanced computer modeling and simulation. When there is an enhanced understanding of the spatial and temporal distribution of precipitation then it becomes enrichment to applications such as hydrologic, climatic and ecological. Conversely, there may be some kind of challenges occur in the community due to some application which results in the absence of consistent precipitation observation in remote and also emerging region. This survey paper provides a multifarious collection of methodologies which are epitomized by various researchers for predicting the rainfall. It also gives information about some technique to forecast rainfall, which is appropriate to all methods like numerical, traditional and statistical.

Keywords: Satellite Image, Segmentation, Feature Extraction, Classification, Clustering, Precipitation Estimation.

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Authors: Y. Bentoutou

Abstract:

Memory Errors Detection and Correction aim to secure the transaction of data between the central processing unit of a satellite onboard computer and its local memory. In this paper, the application of a double-bit error detection and correction method is described and implemented in Field Programmable Gate Array (FPGA) technology. The performance of the proposed EDAC method is measured and compared with two different EDAC devices, using the same FPGA technology. Statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard the first Algerian microsatellite Alsat-1 is given.

Keywords: Error Detection and Correction, On-board computer, small satellite missions.

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Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou

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Emotion in speech is an issue that has been attracting the interest of the speech community for many years, both in the context of speech synthesis as well as in automatic speech recognition (ASR). In spite of the remarkable recent progress in Large Vocabulary Recognition (LVR), it is still far behind the ultimate goal of recognising free conversational speech uttered by any speaker in any environment. Current experimental tests prove that using state of the art large vocabulary recognition systems the error rate increases substantially when applied to spontaneous/emotional speech. This paper shows that recognition rate for emotionally coloured speech can be improved by using a language model based on increased representation of emotional utterances.

Keywords: Statistical language model, N-grams, emotionallycoloured speech

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Authors: Arkadiusz Biernacki

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In this paper we present a statistical analysis of Voice over IP (VoIP) packet streams produced by the G.711 voice coder with voice activity detection (VAD). During telephone conversation, depending whether the interlocutor speaks (ON) or remains silent (OFF), packets are produced or not by a voice coder. As index of dispersion for both ON and OFF times distribution was greater than one, we used hyperexponential distribution for approximation of streams duration. For each stage of the hyperexponential distribution, we tested goodness of our fits using graphical methods, we calculated estimation errors, and performed Kolmogorov-Smirnov test. Obtained results showed that the precise VoIP source model can be based on the five-state Markov process.

Keywords: VoIP source modelling, distribution approximation, hyperexponential distribution.

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Authors: Shuimiao Wan, Chao Yuan, Baoguang Tian

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In this paper, a linear mixed model which has two random effects is broken up into two models. This thesis gets the parameter estimation of the original model and an estimation’s statistical qualities based on these two models. Then many important properties are given by comparing this estimation with other general estimations. At the same time, this paper proves the analysis of variance estimate (ANOVAE) about σ2 of the original model is equal to the least-squares estimation (LSE) about σ2 of these two models. Finally, it also proves that this estimation is better than ANOVAE under Stein function and special condition in some degree.

Keywords: Linear mixed model, Random effects, Parameter estimation, Stein function.

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Authors: W. Fethallah, L. Chraibi, N. Sefiani

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The present paper is concerned with a statistical approach involving latent and manifest variables applied in order to assess the organization's social responsibility performance. The main idea is to develop an assessment tool and a measurement of the Social Responsibility Performance, enabling the company to characterize her performance regarding to the ISO 26000 standard's seven core subjects. For this, we conceptualize a structural equation modeling (SEM) which describes various causal connections between the Social Responsibility’s components. The SEM’s resolution is based on the Partial Least squares (PLS) method and the implementation is running in the XLSTAT software.

Keywords: Corporate social responsibility, latent and manifest variable, partial least squares, structural equation model.

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Authors: Rajeev R. Sahay

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Rivers have transient storage or dead zones where injected pollutants or solutes are entrapped for considerable period of time, known as residence time, before being released into the main flowing zones of rivers. In this study, a new empirical expression for residence time, implementing genetic programming on published dispersion data, has been derived. The proposed expression uses few hydraulic and geometric characteristics of rivers which are normally known to the authorities. When compared with some reported expressions, based on various statistical indices, it can be concluded that the proposed expression predicts the residence time of pollutants in natural rivers more accurately.

Keywords: Parameter estimation, pollutant transport, residence time, rivers, transient storage.

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Authors: Ana Horvat, Maja Krsmanovic, Mladen Djuric

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Rapid growth of distance learning resulted in importance to conduct research on students- satisfaction with distance learning because differences in students- satisfaction might influence educational opportunities for learning in a relevant Web-based environment. In line with this, this paper deals with satisfaction of students with distance module at Faculty of organizational sciences (FOS) in Serbia as well as some factors affecting differences in their satisfaction . We have conducted a research on a population of 68 first-year students of distance learning studies at FOS. Using statistical techniques, we have found out that there is no significant difference in students- satisfaction with distance learning module between men and women. In the same way, we also concluded that there is a difference in satisfaction with distance learning module regarding to student-s perception of opportunity to gain knowledge as the classic students.

Keywords: distance learning, students' satisfaction

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Authors: M. Safaei, M. Seifert, K. D. Thoben

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Supply network management adopts a systematic and integrative approach to managing the operations and relationships of various parties in a supply network. The objective of the manufactures in their supply network is to reduce inventory costs and increase customer satisfaction levels. One way of doing that is to synchronize delivery performance. A supply network can be described by nodes representing the companies and the links (relationships) between these nodes. Uncertainty in delivery time depends on type of network relationship between suppliers. The problem is to understand how the individual uncertainties influence the total uncertainty of the network and identify those parts of the network, which has the highest potential for improving the total delivery time uncertainty.

Keywords: Delivery time uncertainty, Distribution function, Statistical method, Supply Network.

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Authors: S. Chumkiew, W. Srisang, M. Jaroensutasinee, K. Jaroensutasinee

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This study investigated the climatic factors associated with Influenza incidence in Nakhon Si Thammarat, Southern Thailand. Climatic factors comprised of the amount of rainfall, percent of rainy days, relative humidity, wind speed, maximum, minimum temperatures and temperature difference. A multiple stepwise regression technique was used to fit the statistical model. The result showed that the temperature difference and percent of rainy days were positively associated with Influenza incidence in Nakhon Si Thammarat.

Keywords: Influenza, Climatic Factor, Relative Humidity, Rainy day, Wind Speed.

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Authors: Rajasekhar Mamilla, Janardhana G., Anjan Babu G.

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The present research study analyses the students’ satisfaction with university performance regarding the reliability dimension, ability of professors and staff to perform the promised services with quality to students in the post-graduate courses offered by Sri Venkateswara University in India. The research is done with the notion that the student compares the perceived performance with prior expectations. Customer satisfaction is seen as the outcome of this comparison. The sample respondents were administered with schedule based on stratified random technique for this study. Statistical techniques such as factor analysis, t-test and correlation analysis were used to accomplish the respective objectives of the study.

Keywords: Satisfaction, Reliability, Service Quality.

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Authors: Hongmin Deng, Qionghua Wang

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A modified two dimensional (2D) logistic map based on cross feedback control is proposed. This 2D map exhibits more random chaotic dynamical properties than the classic one dimensional (1D) logistic map in the statistical characteristics analysis. So it is utilized as the pseudo-random (PN) sequence generator, where the obtained real-valued PN sequence is quantized at first, then applied to radio frequency identification (RFID) communication system in this paper. This system is experimentally validated on a cortex-M₀ development board, which shows the effectiveness in key generation, the size of key space and security. At last, further cryptanalysis is studied through the test suite in the National Institute of Standards and Technology (NIST).

Keywords: Chaos encryption, logistic map, pseudo-random sequence, RFID.

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Authors: Fayez M. Idris, Nehal I. AlZubaidi

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Breast cancer detection techniques have been reported to aid radiologists in analyzing mammograms. We note that most techniques are performed on uncompressed digital mammograms. Mammogram images are huge in size necessitating the use of compression to reduce storage/transmission requirements. In this paper, we present an algorithm for the detection of microcalcifications in the JPEG2000 domain. The algorithm is based on the statistical properties of the wavelet transform that the JPEG2000 coder employs. Simulation results were carried out at different compression ratios. The sensitivity of this algorithm ranges from 92% with a false positive rate of 4.7 down to 66% with a false positive rate of 2.1 using lossless compression and lossy compression at a compression ratio of 100:1, respectively.

Keywords: Breast cancer, JPEG2000, mammography, microcalcifications.

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Authors: F. Castillo, J. Arellano, S. Sánchez

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In this paper an alternative analysis in the time domain is described and the results of the interpolation process are presented by means of functions that are based on the rule of conditional mathematical expectation and the covariance function. A comparison between the interpolation error caused by low order filters and the classic sinc(t) truncated function is also presented. When fewer samples are used, low-order filters have less error. If the number of samples increases, the sinc(t) type functions are a better alternative. Generally speaking there is an optimal filter for each input signal which depends on the filter length and covariance function of the signal. A novel scheme of work for adaptive interpolation filters is also presented.

Keywords: Interpolation, basis function, over-sampling.

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Authors: D. Woradechjumroen, H. Li, Y. Yu

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Supermarkets are the most electricity-intensive type of commercial buildings. The unsuitable indoor environment of a supermarket provided by abnormal HVAC operations incurs waste energy consumption in refrigeration systems. This current study briefly describes significantly solid backgrounds and proposes easyto- use analysis terminology for investigating the impact of HVAC operations on refrigeration power consumption using the field-test data obtained from building automation system (BAS). With solid backgrounds and prior knowledge, expected energy interactions between HVAC and refrigeration systems are proposed through Pearson’s correlation analysis (R value) by considering correlations between equipment power consumption and dominantly independent variables (driving force conditions).The R value can be conveniently utilized to evaluate how strong relations between equipment operations and driving force parameters are. The calculated R values obtained from field data are compared to expected ranges of R values computed by energy interaction methodology. The comparisons can separate the operational conditions of equipment into faulty and normal conditions. This analysis can simply investigate the condition of equipment operations or building sensors because equipment could be abnormal conditions due to routine operations or faulty commissioning processes in field tests. With systematically solid and easy-to-use backgrounds of interactions provided in the present article, the procedures can be utilized as a tool to evaluate the proper commissioning and routine operations of HVAC and refrigeration systems to detect simple faults (e.g. sensors and driving force environment of refrigeration systems and equipment set-point) and optimize power consumption in supermarket buildings. Moreover, the analysis will be used to further study the FDD research for supermarkets in future.

Keywords: Energy interaction, HVAC, R-value, Supermarket buildings.

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Authors: Ana Mafalda Gonçalves, Neuza Pedro

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The evolution of ICT has changed all sections of society and these changes have been creating an irreversible impact on higher education institutions, which are expected to adopt innovative technologies in their teaching practices. As theorical framework this study select Rogers theory of innovation diffusion which is widely used to illustrate how technologies move from a localized invented to a widespread evolution on organizational practices. Based on descriptive statistical data collected in a European higher education institution three years longitudinal study was conducted for analyzing and discussion the different stages of a LMS adoption process. Results show that ICT integration in higher education is not progressively successful and a linear process and multiple aspects must be taken into account.

Keywords: e-learning, higher education, LMS, innovation, technologies

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