Search results for: filtering and estimation

Authors: Yanling Li, Linying Ji, Zita Oravecz, Timothy R. Brick, Michael D. Hunter, Sy-Miin Chow

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Assessing several individuals intensively over time yields intensive longitudinal data (ILD). Even though ILD provide rich information, they also bring other data analytic challenges. One of these is the increased occurrence of missingness with increased study length, possibly under non-ignorable missingness scenarios. Multiple imputation (MI) handles missing data by creating several imputed data sets, and pooling the estimation results across imputed data sets to yield final estimates for inferential purposes. In this article, we introduce dynr.mi(), a function in the R package, Dynamic Modeling in R (dynr). The package dynr provides a suite of fast and accessible functions for estimating and visualizing the results from fitting linear and nonlinear dynamic systems models in discrete as well as continuous time. By integrating the estimation functions in dynr and the MI procedures available from the R package, Multivariate Imputation by Chained Equations (MICE), the dynr.mi() routine is designed to handle possibly non-ignorable missingness in the dependent variables and/or covariates in a user-specified dynamic systems model via MI, with convergence diagnostic check. We utilized dynr.mi() to examine, in the context of a vector autoregressive model, the relationships among individuals’ ambulatory physiological measures, and self-report affect valence and arousal. The results from MI were compared to those from listwise deletion of entries with missingness in the covariates. When we determined the number of iterations based on the convergence diagnostics available from dynr.mi(), differences in the statistical significance of the covariate parameters were observed between the listwise deletion and MI approaches. These results underscore the importance of considering diagnostic information in the implementation of MI procedures.

Keywords: dynamic modeling, missing data, mobility, multiple imputation

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Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Abderrahmane Diaf, Assia Cherfa, Lamia Karadaniz

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Multiple tray solar distillation offers an interesting alternative for small-scale desalination and production high quality distilled water at a competitive cost using solar energy. In this work, we present indoor/outdoor trial performance data of our multiple tray solar distillation as well as the results of cost estimation analysis.

Keywords: solar desalination, tray distillation, multi-étages solaire, solar distillation

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Authors: Ratul Das

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The present experimental study insights the decontamination of instantaneous velocity fluctuations captured by Acoustic Doppler Velocimeter (ADV) in gravel-bed streams to ascertain near-bed turbulence for low Reynolds number. The interference between incidental and reflected pulses produce spikes in the ADV data especially in the near-bed flow zone and therefore filtering the data are very essential. Nortek’s Vectrino four-receiver ADV probe was used to capture the instantaneous three-dimensional velocity fluctuations over a non-cohesive bed. A spike removal algorithm based on the acceleration threshold method was applied to note the bed roughness and its influence on velocity fluctuations and velocity power spectra in the carrier fluid. The velocity power spectra of despiked signals with a best combination of velocity threshold (VT) and acceleration threshold (AT) are proposed which ascertained velocity power spectra a satisfactory fit with the Kolmogorov “–5/3 scaling-law” in the inertial sub-range. Also, velocity distributions below the roughness crest level fairly follows a third-degree polynomial series.

Keywords: acoustic doppler velocimeter, gravel-bed, spike removal, reynolds shear stress, near-bed turbulence, velocity power spectra

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Konstantinos G. Kostinakis, Manthos K. Papadopoulos, Asimina M. Athanatopoulou

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An important area of research in seismic risk analysis is the evaluation of expected seismic damage of structures under a specific earthquake ground motion. Several conventional intensity measures of ground motion have been used to estimate their damage potential to structures. Yet, none of them was proved to be able to predict adequately the seismic damage of any structural system. Therefore, alternative advanced intensity measures which take into account not only ground motion characteristics but also structural information have been proposed. The adequacy of a number of advanced earthquake intensity measures in prediction of structural damage of 3D R/C buildings under seismic excitation which attacks the building with arbitrary incident angle is investigated in the present paper. To achieve this purpose, a symmetric in plan and an asymmetric 5-story R/C building are studied. The two buildings are subjected to 20 bidirectional earthquake ground motions. The two horizontal accelerograms of each ground motion are applied along horizontal orthogonal axes forming 72 different angles with the structural axes. The response is computed by non-linear time history analysis. The structural damage is expressed in terms of the maximum interstory drift as well as the overall structural damage index. The values of the aforementioned seismic damage measures determined for incident angle 0° as well as their maximum values over all seismic incident angles are correlated with 9 structure-specific ground motion intensity measures. The research identified certain intensity measures which exhibited strong correlation with the seismic damage of the two buildings. However, their adequacy for estimation of the structural damage depends on the response parameter adopted. Furthermore, it was confirmed that the widely used spectral acceleration at the fundamental period of the structure is a good indicator of the expected earthquake damage level.

Keywords: damage indices, non-linear response, seismic excitation angle, structure-specific intensity measures

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Authors: Dibakar Sahoo, Sridevi Gummadi

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The focus of the study is to empirically analyse the climatic impacts on foodgrain yield and its variability across seasons and altitudes in Odisha, one of the most vulnerable states in India. The study uses Just-Pope Stochastic Production function by using two-step Feasible Generalized Least Square (FGLS): mean equation estimation and variance equation estimation. The study uses the panel data on foodgrain yield, rainfall and temperature for 13 districts during the period 1984-2013. The study considers four seasons: winter (December-February), summer (March-May), Rainy (June-September) and autumn (October-November). The districts under consideration have been categorized under three altitude regions such as low (< 70 masl), middle (153-305 masl) and high (>305 masl) altitudes. The results show that an increase in the standard deviations of monthly rainfall during rainy and autumn seasons have an adversely significant impact on the mean yield of foodgrains in Odisha. The summer temperature has beneficial effects by significantly increasing mean yield as the summer season is associated with harvesting stage of Rabi crops. The changing pattern of temperature has increasing effect on the yield variability of foodgrains during the summer season, whereas it has a decreasing effect on yield variability of foodgrains during the Rainy season. Moreover, the positive expected signs of trend variable in both mean and variance equation suggests that foodgrain yield and its variability increases with time. On the other hand, a change in mean levels of rainfall and temperature during different seasons has heterogeneous impacts either harmful or beneficial depending on the altitudes. These findings imply that adaptation strategies should be tailor-made to minimize the adverse impacts of climate change and variability for sustainable development across seasons and altitudes in Odisha agriculture.

Keywords: altitude, adaptation strategies, climate change, foodgrain

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Authors: Chuen-arom Janthimachai-amorn, Chirawadee Harnrittha

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This study aims to identify the factors facilitating the knowledge management to develop the graduate study programs to achieve success and to identify the approaches in developing the graduate study programs in the Rajbhat Suansunantha University. The 10 respondents were the administrators, the faculty, and the personnel of its Graduate School. The research methodology was based on Pla-too Model of the Knowledge Management Institute (KMI) by allocating the knowledge indicators, the knowledge creation and search, knowledge systematization, knowledge processing and filtering, knowledge access, knowledge sharing and exchanges and learning. The results revealed that major success factors were knowledge indicators, evident knowledge management planning, knowledge exchange and strong solidarity of the team and systematic and tenacious access of knowledge. The approaches allowing the researchers to critically develop the graduate study programs were the environmental data analyses, the local needs and general situations, data analyses of the previous programs, cost analyses of the resources, and the identification of the structure and the purposes to develop the new programs.

Keywords: program development, knowledge management, graduate study programs, Rajbhat Suansunantha University

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Authors: M. A. Moussa, M. H. Abdel Rehim, G.M. Turky

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Polyaniline composites with carbon nitride, to overcome compatibility restriction with graphene, were prepared with the solution method. FTIR and Uv-vis spectra were used for structural conformation. While XRD and XPS confirmed the structures in addition to estimation of nitrogen atom surroundings, the pore sizes and the active surface area were determined from BET adsorption isotherm. The electrical and dielectric parameters were measured and calculated with BDS .

Keywords: carbon nitride, dynamic relaxation, electrical conductivity, polyaniline

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Authors: Myung Ho Lee, Hee Seung Lim, Young Jae Maeng, Chang Hoon Lee

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This study presents an automatic sequential extraction device for Pu and Am isotopes in radioactive waste samples from the nuclear power plant with anion exchange resin and TRU resin. After radionuclides were leached from the radioactive waste samples with concentrated HCl and HNO₃, the sample was allowed to evaporate to dryness after filtering the leaching solution with 0.45 micron filter. The Pu isotopes were separated in HNO₃ medium with anion exchange resin. For leaching solution passed through the anion exchange column, the Am isotopes were sequentially separated with TRU resin. Automatic sequential extraction device built-in software information of separation for Pu and Am isotopes was developed. The purified Pu and Am isotopes were measured by alpha spectrometer, respectively, after the micro-precipitation of neodymium. The data of Pu and Am isotopes in radioactive waste with an automatic sequential extraction device developed in this study were validated with the ICP-MS system.

Keywords: automatic sequential extraction device, Pu isotopes, Am isotopes, alpha spectrometer, radioactive waste samples, ICP-MS system

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Authors: Xiao-Yan Li, Takashi Hashimoto, Guanhong Li, Shuo Yang

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In Japanese, onomatopoeia is an important element in the lively expression of feelings and experiences. It is very difficult for students of Japanese to acquire onomatopoeia, especially, its nuances. In this paper, based on traditional L2 learning theories, we propose a new method to improve the efficiency of teaching the nuances – both explicit and tacit - to non-native speakers of Japanese. The method for teaching the tacit nuances of onomatopoeia consists of three elements. First is to teach the formal rules representing the explicit nuances of onomatopoeic words. Second is to have the students create new onomatopoeic words by utilizing those formal rules. The last element is to provide feedback by evaluating the onomatopoeias created. Our previous study used five-grade relative estimation. However students were confused about the five-grade system, because they could not understand the evaluation criteria only based on a figure. In this new system, then, we built an evaluation database through native speakers’ narrative interpretation. We asked Japanese native speakers to describe their awareness of the nuances of onomatopoeia in writing. Then they voted on site and defined priorities for showing to learners on the system. To verify the effectiveness of the proposed method and the learning system, we conducted a preliminary experiment involving two groups of subjects. While Group A got feedback about the appropriateness of their onomatopoeic constructions from the native speakers’ narrative interpretation, Group B got feedback just in the form of the five-grade relative estimation. A questionnaire survey administered to all of the learners clarified our learning system availability and also identified areas that should be improved. Repetitive learning of word-formation rules, creating new onomatopoeias and gaining new awareness from narrative interpretation is the total process used to teach the explicit and tacit nuances of onomatopoeia.

Keywords: onomatopoeia, tacit nuance, narrative interpretation, e-learning system, second language teaching

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Abdel-Monem Sayed Mohamed

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Egypt rapidly growing development is accompanied by increasing levels of standard living particular in its urban areas. However, there is a limited experience in quantifying the sources of risk management in Egypt and in designing efficient strategies to keep away serious impacts of earthquakes. From the historical point of view and recent instrumental records, there are some seismo-active regions in Egypt, where some significant earthquakes had occurred in different places. The special tectonic features in Egypt: Aswan, Greater Cairo, Red Sea and Sinai Peninsula regions are the territories of a high seismic risk, which have to be monitored by up-to date technologies. The investigations of the seismic events and interpretations led to evaluate the seismic hazard for disaster prevention and for the safety of the dense populated regions and the vital national projects as the High Dam. In addition to the monitoring of the recent crustal movements, the most powerful technique of satellite geodesy GPS are used where geodetic networks are covering such seismo-active regions. The results from the data sets are compared and combined in order to determine the main characteristics of the deformation and hazard estimation for specified regions. The final compiled output from the seismological and geodetic analysis threw lights upon the geodynamical regime of these seismo-active regions and put Aswan and Greater Cairo under the lowest class according to horizontal crustal strains classifications. This work will serve a basis for the development of so-called catastrophic models and can be further used for catastrophic risk management. Also, this work is trying to evaluate risk of large catastrophic losses within the important regions including the High Dam, strategic buildings and archeological sites. Studies on possible scenarios of earthquakes and losses are a critical issue for decision making in insurance as a part of mitigation measures.

Keywords: b-value, Gumbel distribution, seismic and GPS data, strain parameters

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Authors: Liwei Liu, Shuguo Pan, Wang Gao

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In order to take full advantages of the BDS-3 penta-frequency signals in the long-baseline RTK positioning, a long-baseline RTK positioning method based on the BDS-3 penta-frequency ionospheric-reduced (IR) combinations is proposed. First, the low noise and weak ionospheric delay characteristics of the multi-frequency combined observations of BDS-3is analyzed. Second, the multi-frequency extra-wide-lane (EWL)/ wide-lane (WL) combinations with long-wavelengths are constructed. Third, the fixed IR EWL combinations are used to constrain the IR WL, then constrain narrow-lane (NL)ambiguityies and start multi-epoch filtering. There is no need to consider the influence of ionospheric parameters in the third step. Compared with the estimated ionospheric model, the proposed method reduces the number of parameters by half, so it is suitable for the use of multi-frequency and multi-system real-time RTK. The results using real data show that the stepwise fixed model of the IR EWL/WL/NL combinations can realize long-baseline instantaneous cimeter-level positioning.

Keywords: penta-frequency, ionospheric-reduced (IR), RTK positioning, long-baseline

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Authors: Yousif Mohamed Y. Abdallah, Razan Manofely, Rajab M. Ben Yousef

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X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.

Keywords: enhancement, x-rays, pixel intensity values, MatLab

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Authors: Jang-Hwan Jo

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The supply management of ecosystem services of local forests is an essential issue as it is linked to the ecological welfare of local residents. This study aims to estimate the supply, trade-offs, and synergies of local forest regulating ecosystem services using a land cover classification map (LCCM) and a forest types map (FTM). Rigorous literature reviews and Expert Delphi analysis were conducted using the detailed variables of 1:5,000 LCCM and FTM. Land-use scoring method and Getis-Ord Gi* Analysis were utilized on detailed variables to propose a method for estimating supply, trade-offs, and synergies of the local forest regulating ecosystem services. The analysis revealed that the rank order (1st to 5th) of supply of regulating ecosystem services was Erosion prevention, Air quality regulation, Heat island mitigation, Water quality regulation, and Carbon storage. When analyzing the correlation between defined services of the entire city, almost all services showed a synergistic effect. However, when analyzing locally, trade-off effects (Heat island mitigation – Air quality regulation, Water quality regulation – Air quality regulation) appeared in the eastern and northwestern forest areas. This suggests the need to consider not only the synergy and trade-offs of the entire forest between specific ecosystem services but also the synergy and trade-offs of local areas in managing the regulating ecosystem services of local forests. The study result can provide primary data for the stakeholders to determine the initial conditions of the planning stage when discussing the establishment of policies related to the adjustment of the supply of regulating ecosystem services of the forests with limited access. Moreover, the study result can also help refine the estimation of the supply of the regulating ecosystem services with the availability of other forms of data.

Keywords: ecosystem service, getis ord gi* analysis, land use scoring method, regional forest, regulating service, synergies, trade-offs

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Authors: Zukhrof Romadhon, Nani Kurniati

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Availability of Spare parts is critical to support maintenance tasks and the production system. Managing spare part inventory deals with some parameters and objective functions, as well as the tradeoff between inventory costs and spare parts availability. Several mathematical models and methods have been developed to optimize the spare part policy. Many researchers who proposed optimization models need to be considered to identify other potential models. This work presents a review of several pertinent literature on spare part inventory optimization and analyzes the gaps for future research. Initial investigation on scholars and many journal database systems under specific keywords related to spare parts found about 17K papers. Filtering was conducted based on five main aspects, i.e., replenishment policy, objective function, echelon network, lead time, model solving, and additional aspects of part classification. Future topics could be identified based on the number of papers that haven’t addressed specific aspects, including joint optimization of spare part inventory and maintenance.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenance

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Authors: C. Patrascioiu, V. Matei, N. Nicolae

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The paper describes the experiments and the kinetic parameters calculus of the gasoil hydrofining. They are presented experimental results of gasoil hidrofining using Mo and promoted with Ni on aluminum support catalyst. The authors have adapted a kinetic model gasoil hydrofining. Using this proposed kinetic model and the experimental data they have calculated the parameters of the model. The numerical calculus is based on minimizing the difference between the experimental sulf concentration and kinetic model estimation.

Keywords: hydrofining, kinetic, modeling, optimization

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Authors: Levan Tsulukidze

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The paper considers debris flow phenomena and forecasting of them in the corridor of ‘TRACECA’ on the example of river Naokhrevistkali, as well as the debris flow -type channel passing between the villages of Vale-2 and Naokhrevi. As a result of expeditionary and reconnaissance investigations, as well as using empiric dependencies, the debris flow expenditure has been estimated in case of different debris flow provisions.

Keywords: debris flow, Traceca corridor, forecasting, river Naokhrevistkali

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Authors: Artem Kim, Clara Savary, Christele Dubourg, Wilfrid Carre, Houda Hamdi-Roze, Valerie Dupé, Sylvie Odent, Marie De Tayrac, Veronique David

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Holoprosencephaly (HPE) is a rare congenital brain malformation resulting from the incomplete separation of the two cerebral hemispheres. It is characterized by a wide phenotypic spectrum and a high degree of locus heterogeneity. Genetic defects in 16 genes have already been implicated in HPE, but account for only 30% of cases, suggesting that a large part of genetic factors remains to be discovered. HPE has been recently redefined as a complex multigenic disorder, requiring the joint effect of multiple mutational events in genes belonging to one or several developmental pathways. The onset of HPE may result from accumulation of the effects of multiple rare variants in functionally-related genes, each conferring a moderate increase in the risk of HPE onset. In order to decipher the genetic basis of HPE, unconventional patterns of inheritance involving multiple genetic factors need to be considered. The primary objective of this study was to uncover possible disease causing combinations of multiple rare variants underlying HPE by performing trio-based Whole Exome Sequencing (WES) of familial cases where no molecular diagnosis could be established. 39 families were selected with no fully-penetrant causal mutation in known HPE gene, no chromosomic aberrations/copy number variants and without any implication of environmental factors. As the main challenge was to identify disease-related variants among a large number of nonpathogenic polymorphisms detected by WES classical scheme, a novel variant prioritization approach was established. It combined WES filtering with complementary gene-level approaches: transcriptome-driven (RNA-Seq data) and clinically-driven (public clinical data) strategies. Briefly, a filtering approach was performed to select variants compatible with disease segregation, population frequency and pathogenicity prediction to identify an exhaustive list of rare deleterious variants. The exome search space was then reduced by restricting the analysis to candidate genes identified by either transcriptome-driven strategy (genes sharing highly similar expression patterns with known HPE genes during cerebral development) or clinically-driven strategy (genes associated to phenotypes of interest overlapping with HPE). Deeper analyses of candidate variants were then performed on a family-by-family basis. These included the exploration of clinical information, expression studies, variant characteristics, recurrence of mutated genes and available biological knowledge. A novel bioinformatics pipeline was designed. Applied to the 39 families, this final integrated workflow identified an average of 11 candidate variants per family. Most of candidate variants were inherited from asymptomatic parents suggesting a multigenic inheritance pattern requiring the association of multiple mutational events. The manual analysis highlighted 5 new strong HPE candidate genes showing recurrences in distinct families. Functional validations of these genes are foreseen.

Keywords: complex genetic disorder, holoprosencephaly, multiple rare variants, whole exome sequencing

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Authors: Naod Mekonnen Anega

Abstract:

The very objective of this paper was to empirically identify technology tailored determinants to the adoption and adoption intensity (extent of use) of agricultural water management technologies in Meda Welabu Woreda, Oromia regional state, Ethiopia. Meda Welabu Woreda which is one of the administrative Woredas of the Oromia regional state was selected purposively as this Woreda is one of the Woredas in the region where small scale irrigation practices and the use of agricultural water management technologies can be found among smallholders. Using the existence water management practices (use of water management technologies) and land use pattern as a criterion Genale Mekchira Kebele is selected to undergo the study. A total of 200 smallholders were selected from the Kebele using the technique developed by Krejeie and Morgan. The study employed the Logit and Tobit models to estimate and identify the economic, social, geographical, household, institutional, psychological, technological factors that determine adoption and adoption intensity of water management technologies. The study revealed that while 55 of the sampled households are adopters of agricultural water management technology the rest 140 were non adopters of the technologies. Among the adopters included in the sample 97% are using river diversion technology (traditional) with traditional canal while the rest 7% percent are using pond with treadle pump technology. The Logit estimation reveled that while adoption of river diversion is positively and significantly affected by membership to local institutions, active labor force, income, access to credit and land ownership, adoption of treadle pump technology is positively and significantly affected by family size, education level, access to credit, extension contact, income, access to market, and slope. The Logit estimation also revealed that whereas, group action requirement, distance to farm, and size of active labor force negative and significantly influenced adoption of river diversion, age and perception has negatively and significantly influenced adoption decision of treadle pump technology. On the other hand, the Tobit estimation reveled that while adoption intensity (extent of use) of agricultural water management is positively and significantly affected by education, credit, and extension contact, access to credit, access to market and income. This study revealed that technology tailored study on adoption of Agricultural water management technologies (AWMTs) should be considered to indentify and scale up best agricultural water management practices. In fact, in countries like Ethiopia, where there is difference in social, economic, cultural, environmental and agro ecological conditions even within the same Kebele technology tailored study that fit the condition of each Kebele would help to identify and scale up best practices in agricultural water management.

Keywords: water management technology, adoption, adoption intensity, smallholders, technology tailored approach

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Authors: Mauro Callejas-Cuervo, Andrea C. Alarcon-Aldana, Andres F. Ruiz-Olaya, Juan C. Alvarez

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Motion capture (MC) is the construction of a precise and accurate digital representation of a real motion. Systems have been used in the last years in a wide range of applications, from films special effects and animation, interactive entertainment, medicine, to high competitive sport where a maximum performance and low injury risk during training and competition is seeking. This paper presents an inertial and magnetic sensor based technological platform, intended for particular amplitude monitoring and telerehabilitation processes considering an efficient cost/technical considerations compromise. Our platform particularities offer high social impact possibilities by making telerehabilitation accessible to large population sectors in marginal socio-economic sector, especially in underdeveloped countries that in opposition to developed countries specialist are scarce, and high technology is not available or inexistent. This platform integrates high-resolution low-cost inertial and magnetic sensors with adequate user interfaces and communication protocols to perform a web or other communication networks available diagnosis service. The amplitude information is generated by sensors then transferred to a computing device with adequate interfaces to make it accessible to inexperienced personnel, providing a high social value. Amplitude measurements of the platform virtual system presented a good fit to its respective reference system. Analyzing the robotic arm results (estimation error RMSE 1=2.12° and estimation error RMSE 2=2.28°), it can be observed that during arm motion in any sense, the estimation error is negligible; in fact, error appears only during sense inversion what can easily be explained by the nature of inertial sensors and its relation to acceleration. Inertial sensors present a time constant delay which acts as a first order filter attenuating signals at large acceleration values as is the case for a change of sense in motion. It can be seen a damped response of platform virtual in other images where error analysis show that at maximum amplitude an underestimation of amplitude is present whereas at minimum amplitude estimations an overestimation of amplitude is observed. This work presents and describes the platform virtual as a motion capture system suitable for telerehabilitation with the cost - quality and precision - accessibility relations optimized. These particular characteristics achieved by efficiently using the state of the art of accessible generic technology in sensors and hardware, and adequate software for capture, transmission analysis and visualization, provides the capacity to offer good telerehabilitation services, reaching large more or less marginal populations where technologies and specialists are not available but accessible with basic communication networks.

Keywords: inertial sensors, joint amplitude measurement, MEMS, telerehabilitation

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Authors: Mrinal Kanti Bhowmik, Debanjana Debnath Jr., Debotosh Bhattacharjee

Abstract:

A facial expression is undeniably the human manners. It is a significant channel for human communication and can be applied to extract emotional features accurately. People in pain often show variations in facial expressions that are readily observable to others. A core of actions is likely to occur or to increase in intensity when people are in pain. To illustrate the changes in the facial appearance, a system known as Facial Action Coding System (FACS) is pioneered by Ekman and Friesen for human observers. According to Prkachin and Solomon, a set of such actions carries the bulk of information about pain. Thus, the Prkachin and Solomon pain intensity (PSPI) metric is defined. So, it is very important to notice that facial expressions, being a behavioral source in communication media, provide an important opening into the issues of non-verbal communication in pain. People express their pain in many ways, and this pain behavior is the basis on which most inferences about pain are drawn in clinical and research settings. Hence, to understand the roles of different pain behaviors, it is essential to study the properties. For the past several years, the studies are concentrated on the properties of one specific form of pain behavior i.e. facial expression. This paper represents a comprehensive study on pain assessment that can model and estimate the intensity of pain that the patient is suffering. It also reviews the historical background of different pain assessment techniques in the context of painful expressions. Different approaches incorporate FACS from psychological views and a pain intensity score using the PSPI metric in pain estimation. This paper investigates in depth analysis of different approaches used in pain estimation and presents different observations found from each technique. It also offers a brief study on different distinguishing features of real and fake pain. Therefore, the necessity of the study lies in the emerging fields of painful face assessment in clinical settings.

Keywords: facial action coding system (FACS), pain, pain behavior, Prkachin and Solomon pain intensity (PSPI)

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Authors: Marriette Sakah, Samuel Gyamfi, Morkporkpor Delight Sedzro, Christoph Kuhn

Abstract:

Ghana is experiencing rapid economic development and its cities command an increasingly dominant role as centers of both production and consumption. Cities run on energy and are extremely vulnerable to energy scarcity, energy price escalations and health impacts of very poor air quality. The overriding concern in Ghana and other West African states is bridging the gap between energy demand and supply. Energy efficiency presents a cost-effective solution for supply challenges by enabling more coverage with current power supply levels and reducing the need for investment in additional generation capacity and grid infrastructure. In Ghana, major issues for energy policy formulation in residential applications include lack of disaggregated electrical energy consumption data and lack of thorough understanding with regards to socio-economic influences on energy efficiency investment. This study uses a bottom up approach to estimate baseline electricity end-use as well as the energy consumption of best available technologies to enable estimation of energy-efficiency resource in terms of relative reduction in total energy use for Tema city, Ghana. A ground survey was conducted to assess the probable consumer behavior in response to energy efficiency initiatives to enable estimation of the amount of savings that would occur in response to specific policy interventions with regards to funding and incentives provision targeted at households. Results show that 16% - 54% reduction in annual electricity consumption is reasonably achievable depending on the level of incentives provision. The saved energy could supply 10000 - 34000 additional households if the added households use only best available technology. Political support and consumer awareness are necessary to translate energy efficiency resources into real energy savings.

Keywords: achievable energy savings, energy efficiency, Ghana, household appliances

Procedia PDF Downloads 190

Authors: Tobias Horstmann, Thomas Le Garrec, Daniel-Ciprian Mincu, Emmanuel Lévêque

Abstract:

Despite the efficiency and low dissipation of the stream-collide formulation of the Lattice Boltzmann (LB) algorithm, which is nowadays implemented in many commercial LBM solvers, there are certain situations, e.g. mesh transition, in which a classical finite-volume or finite-difference formulation of the LB algorithm still bear advantages. In this paper, we present an algorithm that combines the node-based streaming of the distribution functions with a second-order finite volume discretization of the advection term of the BGK-LB equation on a uniform D2Q9 lattice. It is shown that such a coupling is possible for a multi-domain approach as long as the overlap, or buffer zone, between two domains, is achieved on at least 2Δx. This also implies that a direct coupling (without buffer zone) of a stream-collide and finite-volume LB algorithm on a single grid is not stable. The critical parameter in the coupling is the CFL number equal to 1 that is imposed by the stream-collide algorithm. Nevertheless, an explicit filtering step on the finite-volume domain can stabilize the solution. In a further investigation, we demonstrate how such a coupling can be used for mesh transition, resulting in an intrinsic conservation of mass over the interface.

Keywords: algorithm coupling, finite volume formulation, grid refinement, Lattice Boltzmann method

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Authors: Mimeche Leila, Debabeche Mahmoud

Abstract:

It is question to study and to value the possibility of settling the process of purification by plants (constructed wetland) to treat the domestic waste water of Biskra, city in a semi-arid environment with grave problems of. According to the bibliography, the process of treatment by plants is considered as more advantageous than the classic techniques. It is the use of beds with macrophytes where the purification is made by the combined action of plants and micro-organisms in a filtering bed. The micro-organisms which are aerobic bacteria and\or anaerobic have for main function to degrade the polluting materials. Plants in the macrophytes beds have for function to serve as support in the development of bacteria and to favour also their development. In this study, we present a preliminary experimental analysis of the potentialities of treatment of some macrpohytes plants, implanted in basins filled of gravel. Analyses physico chemical and bacteriological of the waste water indicate a good elimination of the polluting materials, and put in evidence the purifier power of these plants, in association with bacteria. The obtained results seem to be interesting and encourage deepening the study for other types of plants in other conditions.

Keywords: constructed wetlands, macrophytes, sewage treatment, wastewater

Procedia PDF Downloads 376

Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

Abstract:

In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

Procedia PDF Downloads 490