Search results for: data rate

Authors: Iftikhar Ahmed Satti, Wan Ismail Wan Yusoff

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The structural interpretation of a part of eastern Potwar (Missa Keswal) has been carried out with available seismological, seismic and well data. Seismological data contains both the source parameters and fault plane solution (FPS) parameters and seismic data contains ten seismic lines that were re-interpreted by using well data. Structural interpretation depicts two broad types of fault sets namely, thrust and back thrust faults. These faults together give rise to pop up structures in the study area and also responsible for many structural traps and seismicity. Seismic interpretation includes time and depth contour maps of Chorgali Formation while seismological interpretation includes focal mechanism solution (FMS), depth, frequency, magnitude bar graphs and renewal of Seismotectonic map. The Focal Mechanism Solutions (FMS) that surrounds the study area are correlated with the different geological and structural maps of the area for the determination of the nature of subsurface faults. Results of structural interpretation from both seismic and seismological data show good correlation. It is hoped that the present work will help in better understanding of the variations in the subsurface structure and can be a useful tool for earthquake prediction, planning of oil field and reservoir monitoring.

Keywords: Focal mechanism solution (FMS), Fault plane solution (FPS), Reservoir monitoring, earthquake prediction.

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Authors: Ratanaporn Leesing, Rattanaporn Baojungharn, Thidarat Papone

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Compared to oil production from microorganisms, little work has been performed for mixed culture of microalgae and yeast. In this article it is aimed to show high oil accumulation potential of mixed culture of microalgae Chlorella sp. KKU-S2 and oleaginous yeast Torulaspora maleeae Y30 using sugarcane molasses as substrate. The monoculture of T. maleeae Y30 grew faster than that of microalgae Chlorella sp. KKU-S2. In monoculture of yeast, a biomass of 6.4g/L with specific growth rate (m) of 0.265 (1/d) and lipid yield of 0.466g/L were obtained, while 2.53g/L of biomass with m of 0.133 (1/d) and lipid yield of 0.132g/L were obtained for monoculture of Chlorella sp. KKU-S2. The biomass concentration in the mixed culture of T. maleeae Y30 with Chlorella sp. KKU-S2 increased faster and was higher compared with that in the monoculture and mixed culture of microalgae. In mixed culture of microalgae Chlorella sp. KKU-S2 and C. vulgaris TISTR8580, a biomass of 3.47g/L and lipid yield of 0.123 g/L were obtained. In mixed culture of T. maleeae Y30 with Chlorella sp. KKU-S2, a maximum biomass of 7.33 g/L and lipid yield of 0.808g/L were obtained. Maximum cell yield coefficient (YX/S, 0.229g/L), specific yield of lipid (YP/X, 0.11g lipid/g cells) and volumetric lipid production rate (QP, 0.115 g/L/d) were obtained in mixed culture of yeast and microalgae. Clearly, T. maleeae Y30 and Chlorella sp. KKU-S2 use sugarcane molasses as organic nutrients efficiently in mixed culture under mixotrophic growth. The biomass productivity and lipid yield are notably enhanced in comparison with monoculture.

Keywords: Microbial oil, Chlorella sp. KKU-S2, Chlorella vulgaris, Torulaspora maleeae Y30, mixed culture, biodiesel.

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Authors: Asep Saepuloh, Katsuaki Koike

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Field mapping activity for an active volcano mainly in the Torrid Zone is usually hampered by several problems such as steep terrain and bad atmosphere conditions. In this paper we present a simple solution for such problem by a combination Synthetic Aperture Radar (SAR) and geostatistical methods. By this combination, we could reduce the speckle effect from the SAR data and then estimate roughness distribution of the pyroclastic flow deposits. The main purpose of this study is to detect spatial distribution of new pyroclastic flow deposits termed as P-zone accurately using the β°data from two RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and field observation were used for cross-validation of the SAR results. Mt. Merapi in central Java, Indonesia, was chosen as a study site and the eruptions in May-June 2006 were examined. The P-zones were found in the western and southern flanks. The area size and the longest flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The grain size variation of the P-zone was mapped in detail from fine to coarse deposits regarding the C-band wavelength of 5.6 cm.

Keywords: Geostatistical Method, Mt. Merapi, Pyroclastic, RADARSAT-1.

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Authors: Racholsan Raj Nirmal, B. S. V. Patnaik, R. Jayaganthan

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The deformation behaviour of additively manufactured AlSi10Mg alloy under low strains, high strain rates and elevated temperature conditions is essential to analyse and predict its response against dynamic loading such as impact and thermomechanical fatigue. The constitutive relation of Johnson-Cook is used to capture the strain rate sensitivity and thermal softening effect in AlSi10Mg alloy. Johnson-Cook failure model is widely used for exploring damage mechanics and predicting the fracture in many materials. In this present work, Johnson-Cook material and damage model parameters for additively manufactured AlSi10Mg alloy have been determined numerically from four types of uniaxial tensile test. Three different uniaxial tensile tests with dynamic strain rates (0.1, 1, 10, 50, and 100 s^-1) and elevated temperature tensile test with three different temperature conditions (450 K, 500 K and 550 K) were performed on 3D printed AlSi10Mg alloy in ABAQUS/Explicit. Hexahedral elements are used to discretize tensile specimens and fracture energy value of 43.6 kN/m was used for damage initiation. Levenberg Marquardt optimization method was used for the evaluation of Johnson-Cook model parameters. It was observed that additively manufactured AlSi10Mg alloy has shown relatively higher strain rate sensitivity and lower thermal stability as compared to the other Al alloys.

Keywords: ABAQUS, additive manufacturing, AlSi10Mg, Johnson-Cook model.

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Authors: Hsin-Chang Yang, Chung-Hong Lee, Kuo-Lung Ke

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The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

Keywords: Self-organizing map, topic identification, learning algorithm, text clustering.

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Authors: Vikas Tewari, Shivendra Tewari, K. R. Pardasani

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Calcium is a vital second messenger used in signal transduction. Calcium controls secretion, cell movement, muscular contraction, cell differentiation, ciliary beating and so on. Two theories have been used to simplify the system of reaction-diffusion equations of calcium into a single equation. One is excess buffer approximation (EBA) which assumes that mobile buffer is present in excess and cannot be saturated. The other is rapid buffer approximation (RBA), which assumes that calcium binding to buffer is rapid compared to calcium diffusion rate. In the present work, attempt has been made to develop a model for calcium diffusion under excess buffer approximation in neuron cells. This model incorporates the effect of [Na+] influx on [Ca2+] diffusion,variable calcium and sodium sources, sodium-calcium exchange protein, Sarcolemmal Calcium ATPase pump, sodium and calcium channels. The proposed mathematical model leads to a system of partial differential equations which have been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, association rate, calcium permeability.

Keywords: Excess buffer approximation, Na+ influx, sodium calcium exchange protein, sarcolemmal calcium atpase pump, forward time centred space.

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Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: Connected-car, data modeling, route planning, navigation system.

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Authors: Jing Zhou, Steven Su, Aihuang Guo

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COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.

Keywords: BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.

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Authors: S. Praveen, P. Sojan Lal

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Obfuscation is a low cost software protection methodology to avoid reverse engineering and re engineering of applications. Source code obfuscation aims in obscuring the source code to hide the functionality of the codes. This paper proposes an Array data transformation in order to obfuscate the source code which uses arrays. The applications using the proposed data structures force the programmer to obscure the logic manually. It makes the developed obscured codes hard to reverse engineer and also protects the functionality of the codes.

Keywords: Reverse Engineering, Source Code Obfuscation.

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Authors: Chien-Hua Wang, Wei-Hsuan Lee, Chin-Tzong Pang

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In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.

Keywords: Data mining, association rule, fuzzy frequent patterngrowth.

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: Donna R. Tabangin, Jacqueline C. Flores, Nelson F. Emperador

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Information is power. Geographical information is an emerging science that is advancing the development of knowledge to further help in the understanding of the relationship of “place" with other disciplines such as crime. The researchers used crime data for the years 2004 to 2007 from the Baguio City Police Office to determine the incidence and actual locations of crime hotspots. Combined qualitative and quantitative research methodology was employed through extensive fieldwork and observation, geographic visualization with Geographic Information Systems (GIS) and Global Positioning Systems (GPS), and data mining. The paper discusses emerging geographic visualization and data mining tools and methodologies that can be used to generate baseline data for environmental initiatives such as urban renewal and rejuvenation. The study was able to demonstrate that crime hotspots can be computed and were seen to be occurring to some select places in the Central Business District (CBD) of Baguio City. It was observed that some characteristics of the hotspot places- physical design and milieu may play an important role in creating opportunities for crime. A list of these environmental attributes was generated. This derived information may be used to guide the design or redesign of the urban environment of the City to be able to reduce crime and at the same time improve it physically.

Keywords: Crime mapping, data mining, environmental design, geographic visualization, GIS.

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Authors: Victor R. Thulari, John Akach, Haleden Chiririwa, Aoyi Ochieng

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Solar-light activated titanium dioxide photocatalysts were prepared by hydrolysis of titanium (IV) isopropoxide with thiourea, followed by calcinations at 450 °C. The experiments demonstrated that methyl orange in aqueous solutions were successfully degraded under solar light using doped TiO₂. The photocatalytic oxidation of a mono azo methyl-orange dye has been investigated in multi ion doped TiO₂ and solar light. Solutions were irradiated by solar-light until high removal was achieved. It was found that there was no degradation of methyl orange in the dark and in the absence of TiO₂. Varieties of laboratory prepared TiO₂ catalysts both un-doped and doped using titanium (IV) isopropoxide and thiourea as a dopant were tested in order to compare their photoreactivity. As a result, it was found that the efficiency of the process strongly depends on the working conditions. The highest degradation rate of methyl orange was obtained at optimum dosage using commercially produced TiO₂. Our work focused on laboratory synthesized catalyst and the maximum methyl orange removal was achieved at 81% with catalyst loading of 0.04 g/L, initial pH of 3 and methyl orange concentration of 0.005 g/L using multi-ion doped catalyst. The kinetics of photocatalytic methyl orange dye stuff degradation was found to follow a pseudo-first-order rate law. The presence of the multi-ion dopant (thiourea) enhanced the photoefficiency of the titanium dioxide catalyst.

Keywords: Degradation, kinetics, methyl orange, photocatalysis.

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Authors: Deng Liao, Dongyu Qiu, Ahmed K. Elhakeem

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A new code synchronization algorithm is proposed in this paper for the secondary cell-search stage in wideband CDMA systems. Rather than using the Cyclically Permutable (CP) code in the Secondary Synchronization Channel (S-SCH) to simultaneously determine the frame boundary and scrambling code group, the new synchronization algorithm implements the same function with less system complexity and less Mean Acquisition Time (MAT). The Secondary Synchronization Code (SSC) is redesigned by splitting into two sub-sequences. We treat the information of scrambling code group as data bits and use simple time diversity BCH coding for further reliability. It avoids involved and time-costly Reed-Solomon (RS) code computations and comparisons. Analysis and simulation results show that the Synchronization Error Rate (SER) yielded by the new algorithm in Rayleigh fading channels is close to that of the conventional algorithm in the standard. This new synchronization algorithm reduces system complexities, shortens the average cell-search time and can be implemented in the slot-based cell-search pipeline. By taking antenna diversity and pipelining correlation processes, the new algorithm also shows its flexible application in multiple antenna systems.

Keywords: WCDMA cell-search, synchronization algorithm, secondary synchronization channel, antenna diversity.

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Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

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This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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Authors: Ilyes Jenhani, Salem Benferhat, Zied Elouedi

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This paper investigates the issue of building decision trees from data with imprecise class values where imprecision is encoded in the form of possibility distributions. The Information Affinity similarity measure is introduced into the well-known gain ratio criterion in order to assess the homogeneity of a set of possibility distributions representing instances-s classes belonging to a given training partition. For the experimental study, we proposed an information affinity based performance criterion which we have used in order to show the performance of the approach on well-known benchmarks.

Keywords: Data mining from uncertain data, Decision Trees, Possibility Theory.

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Authors: Achitphol Chookaew, Paramee Thongsukhsai, Patamarerk Engsontia, Narongwit Nakwan, Pritsana Raugrut

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Lung cancer is one of the most common malignancies and primary cause of death due to cancer worldwide. Non-small cell lung cancer (NSCLC) is the main subtype in which majority of patients present with advanced-stage disease. Herein, we analyzed differentially expressed genes to find potential biomarkers for lung cancer diagnosis as well as prognostic markers. We used transcriptome data from our 2 NSCLC patients and public data (GSE81089) composing of 8 NSCLC and 10 normal lung tissues. Differentially expressed genes (DEGs) between NSCLC and normal tissue and between early-stage and late-stage NSCLC were analyzed by the DESeq2. Pairwise correlation was used to find the DEGs with false discovery rate (FDR) adjusted p-value £ 0.05 and |log2 fold change| ³ 4 for NSCLC versus normal and FDR adjusted p-value £ 0.05 with |log2 fold change| ³ 2 for early versus late-stage NSCLC. Bioinformatic tools were used for functional and pathway analysis. Moreover, the top ten genes in each comparison group were verified the expression and survival analysis via GEPIA. We found 150 up-regulated and 45 down-regulated genes in NSCLC compared to normal tissues. Many immnunoglobulin-related genes e.g., IGHV4-4, IGHV5-10-1, IGHV4-31, IGHV4-61, and IGHV1-69D were significantly up-regulated. 22 genes were up-regulated, and five genes were down-regulated in late-stage compared to early-stage NSCLC. The top five DEGs genes were KRT6B, SPRR1A, KRT13, KRT6A and KRT5. Keratin 6B (KRT6B) was the most significantly increased gene in the late-stage NSCLC. From GEPIA analysis, we concluded that IGHV4-31 and IGKV1-9 might be used as diagnostic biomarkers, while KRT6B and KRT6A might be used as prognostic biomarkers. However, further clinical validation is needed.

Keywords: Bioinformatics, differentially expressed genes, non-small cell lung cancer, transcriptomics.

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Authors: K. Benyounes, A. Benmounah

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Cement-based grouts has been used successfully to repair cracks in many concrete structures such as bridges, tunnels, buildings and to consolidate soils or rock foundations. In the present study the rheological characterization of cement grout with water/binder ratio (W/B) is fixed at 0.5. The effect of the replacement of cement by bentonite (2 to 10% wt) in presence of superplasticizer (0.5% wt) was investigated. Several rheological tests were carried out by using controlled-stress rheometer equipped with vane geometry in temperature of 20°C. To highlight the influence of bentonite and superplasticizer on the rheological behavior of grout cement, various flow tests in a range of shear rate from 0 to 200 s-1 were observed. Cement grout showed a non-Newtonian viscosity behavior at all concentrations of bentonite. Three parameter model Herschel- Bulkley was chosen for fitting of experimental data. Based on the values of correlation coefficients of the estimated parameters, The Herschel-Bulkley law model well described the rheological behavior of the grouts. Test results showed that the dosage of bentonite increases the viscosity and yield stress of the system and introduces more thixotropy. While the addition of both bentonite and superplasticizer with cement grout improve significantly the fluidity and reduced the yield stress due to the action of dispersion of SP.

Keywords: Cement grout, bentonite, superplasticizer, viscosity, yield stress.

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Authors: Akiko Kondo, Akio Kondo

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We are facing regional problems to low birth rate and longevity in Japan. Under this situation, there are some local municipalities which lose their vitality. The aims of this study are to clarify the present state of local public transportation services in local municipalities and relation between local public transportation services and population quantitatively. We conducted a questionnaire survey concerning regional agenda in all local municipalities in Japan. We obtained responses concerning the present state of convenience in use of public transportation and local public transportation services. Based on the data gathered from the survey, it is apparent that we should some sort of measures concerning public transportation services. Convenience in use of public transportation becomes an object of public concern in many rural regions. It is also clarified that some local municipalities introduce a demand bus for the purpose of promotion of administrative and financial efficiency. They also introduce a demand taxi in order to secure transportation to weak people in transportation and eliminate of blank area related to public transportation services. In addition, we construct a population model which includes explanatory variables of present states of local public transportation services. From this result, we can clarify the relation between public transportation services and population quantitatively.

Keywords: Public transportation, local municipality, regional analysis, regional issue.

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance empirical formula, typical SQL query tasks.

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Authors: Hamdi Melih Saraoğlu, Muhlis Yıldırım, Abdurrahman Özbeyaz, Feyzullah Temurtas

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In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.

Keywords: Palm perspiration, Diabetes, Support Vector Machine, Classification.

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Authors: Thanura Abeywardena

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This study recognized the effectiveness of cold-water immersion recovery post short-term exhaustive exercise. The purpose of this study was to understand if 16-20 °C of cold-water immersion would be beneficial in a tropical environment to achieve an optimal recovery in sprint swim performance in comparison to 10-15 °C of water immersion. Two 100 m-sprint swim performance times were measured along with blood lactate (BLa), heart rate (HR) and rate of perceived exertion (RPE) in a 25 m swimming pool with full body head out horizontal water immersions of 10-15 °C, 16-20 °C and 29-32 °C (pool temperature) for 10 minutes followed by 5 minutes of seated passive rest outside; in between the two swim performances. 10 well-trained adult swimmers (5 male and 5 female) within the top twenty in the Sri Lankan nationals swimming championships in 100m Butterfly and Freestyle in the years 2020 & 2021 volunteered for this study. One-way ANOVA analysis (p < 0.05) suggested performance time, BLa and HR had no significant differences between the three conditions after the second sprint, however RPE was significantly different with p = 0.034 between 10-15 °C and 16-20 °C immersion conditions. The study suggested that the recovery post the two cold-water immersion conditions were similar in terms of performance and physiological factors however the 16-20 °C temperature had a better “feel good” factor post sprint 2. Further study is recommended as there was participant bias with the swimmers not reaching optimal levels in sprint 1. Therefore, they might have been possibly fully recovered before sprint 2 invalidating the physiological effect of recovery.

Keywords: Hydrotherapy, blood lactate, fatigue, recovery, sprint-performance, sprint-swimming.

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, stereotypical motor movements, repetitive gesture, kinect, artificial neural network.

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: Building information modeling, elemental graph data model, geometric and topological data models, and graph theory.

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Authors: Minsoo Lee, Julee Choi, Sookyung Song

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The increasing interest on processing data created by sensor networks has evolved into approaches to implement sensor networks as databases. The aggregation operator, which calculates a value from a large group of data such as computing averages or sums, etc. is an essential function that needs to be provided when implementing such sensor network databases. This work proposes to add the DURING clause into TinySQL to calculate values during a specific long period and suggests a way to implement the aggregation service in sensor networks by applying materialized view and incremental view maintenance techniques that is used in data warehouses. In sensor networks, data values are passed from child nodes to parent nodes and an aggregation value is computed at the root node. As such root nodes need to be memory efficient and low powered, it becomes a problem to recompute aggregate values from all past and current data. Therefore, applying incremental view maintenance techniques can reduce the memory consumption and support fast computation of aggregate values.

Keywords: Aggregation, Incremental View Maintenance, Materialized view, Sensor Network.

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Authors: Samuel O. Alamu, Seong W. Lee, Blaise Kalmia, Marc J. Louise Caballes, Xuejun Qian

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The application of combustion technologies for thermal conversion of biomass and solid wastes to energy has been a major solution to the effective handling of wastes over a long period of time. Lab-scale biomass combustion systems have been observed to be economically viable and socially acceptable, but major concerns are the environmental impacts of the process and deviation of temperature distribution within the combustion chamber. Both high and low combustion chamber temperature may affect the overall combustion efficiency and gaseous emissions. Therefore, there is an urgent need to develop a control system which measures the deviations of chamber temperature from set target values, sends these deviations (which generates disturbances in the system) in the form of feedback signal (as input), and control operating conditions for correcting the errors. In this research study, major components of the feedback control system were determined, assembled, and tested. In addition, control algorithms were developed to actuate operating conditions (e.g., air velocity, fuel feeding rate) using ladder logic functions embedded in the Programmable Logic Controller (PLC). The developed control algorithm having chamber temperature as a feedback signal is integrated into the lab-scale swirling fluidized bed combustor (SFBC) to investigate the temperature distribution at different heights of the combustion chamber based on various operating conditions. The air blower rates and the fuel feeding rates obtained from automatic control operations were correlated with manual inputs. There was no observable difference in the correlated results, thus indicating that the written PLC program functions were adequate in designing the experimental study of the lab-scale SFBC. The experimental results were analyzed to study the effect of air velocity operating at 222-273 ft/min and fuel feeding rate of 60-90 rpm on the chamber temperature. The developed temperature-based feedback control system was shown to be adequate in controlling the airflow and the fuel feeding rate for the overall biomass combustion process as it helps to minimize the steady-state error.

Keywords: Air flow, biomass combustion, feedback control system, fuel feeding, ladder logic, programmable logic controller, temperature.

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Authors: Adil Loya, Ali Haider, Arslan A. Ghaffor, Abubaker Siddique

Abstract:

Simulation and modelling of Unmanned Aerial Vehicle (UAV) has gained wide popularity in front of aerospace community. The demand of designing and modelling optimized control system for UAV has increased ten folds since last decade, as next generation warfare is dependent on unmanned technologies. Therefore, this research focuses on the simulation of nonlinear UAV dynamics on Simulink and its integration with Flightgear. There has been lots of research on implementation of optimizing control using Simulink, however, there are fewer known techniques to simulate these dynamics over Flightgear and a tedious technique of acquiring data has been tackled in this research horizon. Sending data to Flightgear is easy but receiving it from Simulink is not that straight forward, i.e. we can only receive control data on the output. However, in this research we have managed to get the data out from the Flightgear by implementation of level 2 s-function block within Simulink. Moreover, the results captured from Flightgear over a Universal Datagram Protocol (UDP) communication are then compared with the attitude signal that were sent previously. This provide useful information regarding the difference in outputs attained from Simulink to Flightgear. It was found that values received on Simulink were in high agreement with that of the Flightgear output. And complete study has been conducted in a discrete way.

Keywords: aerospace, flight control, FlightGear, communication, Simulink

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Authors: Kadio Valere R. Angaman, Birahim Bouna Niang

Abstract:

Land degradation is a serious problem in developing countries including Cote d’Ivoire, which has its economy focused on agriculture. It occurs in all kinds of ecosystems over the world. However, the drivers of land degradation vary from one region to another, and from one ecosystem to another. Thus, identifying these drivers is an essential prerequisite to develop and implement appropriate policies to reverse the trend of land degradation in the country, especially in the trays ecosystem. Using the binary logistic model with primary data obtained through 780 farmers surveyed, we analyze and identify the drivers of land degradation in the trays ecosystem. The descriptive statistics show that 52% of farmers interviewed have stated facing land degradation in their farmland. This high rate shows the extent of land degradation in this ecosystem. Also, the results obtained from the binary logit regression reveal that land degradation is significantly influenced by a set of variables such as sex, education, slope, erosion, pesticide, agricultural activity, deforestation, and temperature. The drivers identified are mostly local, as a result, the government must implement some policies and strategies that facilitate and incentive the adoption of sustainable land management practices by farmers to reverse the negative trend of land degradation.

Keywords: Drivers, land degradation, trays ecosystem, sustainable land management.

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Authors: Ana Cázares

Abstract:

Aims for this study: first, to compare the expertise level in data analysis, communication and information technologies in undergraduate psychology students. Second, to verify the factor structure of E-ETICA (Escala de Experticia en Tecnologias de la Informacion, la Comunicacion y el Análisis or Data Analysis, Communication and Information'Expertise Scale) which had shown an excellent internal consistency (α= 0.92) as well as a simple factor structure. Three factors, Complex, Basic Information and Communications Technologies and E-Searching and Download Abilities, explains 63% of variance. In the present study, 260 students (119 juniors and 141 seniors) were asked to respond to ETICA (16 items Likert scale of five points 1: null domain to 5: total domain). The results show that both junior and senior students report having very similar expertise level; however, E-ETICA presents a different factor structure for juniors and four factors explained also 63% of variance: Information E-Searching, Download and Process; Data analysis; Organization; and Communication technologies.

Keywords: Data analysis, Information, Communications Technologies, Expertise'Levels.

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Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

Abstract:

Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities.

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