Search results for: positive count data.

Authors: Ireneusz Majsterek, Anna Merecz, Agnieszka Sliwinska, Marcin Kosmalski, Jacek Kasznicki, Jozef Drzewoski

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Oxidative stress is considered to be the cause for onset and the progression of type 2 diabetes mellitus (T2DM) and complications including neuropathy. It is a deleterious process that can be an important mediator of damage to cell structures: protein, lipids and DNA. Data suggest that in patients with diabetes and diabetic neuropathy DNA repair is impaired, which prevents effective removal of lesions. Objective: The aim of our study was to evaluate the association of the hOGG1 (326 Ser/Cys) and XRCC1 (194 Arg/Trp, 399 Arg/Gln) gene polymorphisms whose protein is involved in the BER pathway with DNA repair efficiency in patients with diabetes type 2 and diabetic neuropathy compared to the healthy subjects. Genotypes were determined by PCR-RFLP analysis in 385 subjects, including 117 with type 2 diabetes, 56 with diabetic neuropathy and 212 with normal glucose metabolism. The polymorphisms studied include codon 326 of hOGG1 and 194, 399 of XRCC1 in the base excision repair (BER) genes. Comet assay was carried out using peripheral blood lymphocytes from the patients and controls. This test enabled the evaluation of DNA damage in cells exposed to hydrogen peroxide alone and in the combination with the endonuclease III (Nth). The results of the analysis of polymorphism were statistically examination by calculating the odds ratio (OR) and their 95% confidence intervals (95% CI) using the ¤ç2-tests. Our data indicate that patients with diabetes mellitus type 2 (including those with neuropathy) had higher frequencies of the XRCC1 399Arg/Gln polymorphism in homozygote (GG) (OR: 1.85 [95% CI: 1.07-3.22], P=0.3) and also increased frequency of 399Gln (G) allele (OR: 1.38 [95% CI: 1.03-1.83], P=0.3). No relation to other polymorphisms with increased risk of diabetes or diabetic neuropathy. In T2DM patients complicated by neuropathy, there was less efficient repair of oxidative DNA damage induced by hydrogen peroxide in both the presence and absence of the Nth enzyme. The results of our study suggest that the XRCC1 399 Arg/Gln polymorphism is a significant risk factor of T2DM in Polish population. Obtained data suggest a decreased efficiency of DNA repair in cells from patients with diabetes and neuropathy may be associated with oxidative stress. Additionally, patients with neuropathy are characterized by even greater sensitivity to oxidative damage than patients with diabetes, which suggests participation of free radicals in the pathogenesis of neuropathy.

Keywords: Diabetic neuropathy, oxidative stress, gene polymorphisms, oxidative DNA damage.

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Authors: Moneeb Gohar, Seok Joo Koh, Tae-Won Um, Hyun-Woo Lee

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This paper proposes a fast tree join scheme to provide seamless multicast handover in the mobile networks based on the Fast Mobile IPv6 (FMIPv6). In the existing FMIPv6-based multicast handover scheme, the bi-directional tunnelling or the remote subscription is employed with the packet forwarding from the previous access router (AR) to the new AR. In general, the remote subscription approach is preferred to the bi-directional tunnelling one, since in the remote subscription scheme we can exploit an optimized multicast path from a multicast source to many mobile receivers. However, in the remote subscription scheme, if the tree joining operation takes a long time, the amount of data packets to be forwarded and buffered for multicast handover will increase, and thus the corresponding buffer may overflow, which results in severe packet losses. In order to reduce these costs associated with packet forwarding and buffering, this paper proposes the fast join to multicast tree, in which the new AR will join the multicast tree as fast as possible, so that the new multicast data packets can also arrive at the new AR, by which the packet forwarding and buffering costs can be reduced. From numerical analysis, it is shown that the proposed scheme can give better performance than the existing FMIPv6-based multicast handover schemes in terms of the multicast packet delivery costs.

Keywords: Mobile Multicast, FMIPv6, Seamless Handover, Fast Tree Join.

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Authors: M. T. Tsepav, Y. Adamu, M. A. Umar

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A geoelectric survey was carried out in some parts of Angwan Gwari, an outskirt of Lapai Local Government Area on Niger State which belongs to the Nigerian Basement Complex, with the aim of evaluating the soil corrosivity, aquifer transmissivity and protective capacity of the area from which aquifer characterisation was made. The G41 Resistivity Meter was employed to obtain fifteen Schlumberger Vertical Electrical Sounding data along profiles in a square grid network. The data were processed using interpex 1-D sounding inversion software, which gives vertical electrical sounding curves with layered model comprising of the apparent resistivities, overburden thicknesses, and depth. This information was used to evaluate longitudinal conductance and transmissivities of the layers. The results show generally low resistivities across the survey area and an average longitudinal conductance variation from 0.0237Siemens in VES 6 to 0.1261Siemens in VES 15 with almost the entire area giving values less than 1.0 Siemens. The average transmissivity values range from 96.45 Ω.m2 in VES 4 to 299070 Ω.m2 in VES 1. All but VES 4 and VES14 had an average overburden greater than 400 Ω.m2, these results suggest that the aquifers are highly permeable to fluid movement within, leading to the possibility of enhanced migration and circulation of contaminants in the groundwater system and that the area is generally corrosive.

Keywords: Geoelectric survey, corrosivity, protective capacity, transmissivity.

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Authors: Gholamreza Habibi

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In order to study seed yield and seed yield components in bean under reduced irrigation condition and assessment drought tolerance of genotypes, 15 lines of White beans were evaluated in two separate RCB design with 3 replications under stress and non stress conditions. Analysis of variance showed that there were significant differences among varieties in terms of traits under study, indicating the existence of genetic variation among varieties. The results indicate that drought stress reduced seed yield, number of seed per plant, biological yield and number of pod in White been. In non stress condition, yield was highly correlated with the biological yield, whereas in stress condition it was highly correlated with harvest index. Results of stepwise regression showed that, selection can we done based on, biological yield, harvest index, number of seed per pod, seed length, 100 seed weight. Result of path analysis showed that the highest direct effect, being positive, was related to biological yield in non stress and to harvest index in stress conditions. Factor analysis were accomplished in stress and nonstress condition a, there were 4 factors that explained more than 76 percent of total variations. We used several selection indices such as Stress Susceptibility Index ( SSI ), Geometric Mean Productivity ( GMP ), Mean Productivity ( MP ), Stress Tolerance Index ( STI ) and Tolerance Index ( TOL ) to study drought tolerance of genotypes, we found that the best Stress Index for selection tolerance genotypes were STI, GMP and MP were the greatest correlations between these Indices and seed yield under stress and non stress conditions. In classification of genotypes base on phenotypic characteristics, using cluster analysis ( UPGMA ), all allels classified in 5 separate groups in stress and non stress conditions.

Keywords: Cluster analysis, factor analysis, path analysis, selection index, White bean

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Authors: E. Seidi

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Offset Double-Disk Opener (DDO) is a popular furrow opener in conservation tillage. It has some limitations such as negative suction to penetrate in the soil, hair pinning and mixing seed and fertilizer in the slot. Because of importance of separation of seed and fertilizer in the slot, by adding two horizontal mini disks to DDO a modified opener was made (MDO) which placed the fertilizer between and under two rows of seed. To consider performance of novel opener an indoor comparison test between DDO and MDO was performed at soil bin. The experiment was conducted with three working speeds (3, 6 and 8 km h-1), two bulk densities of soil (1.1 and 1.4 Mg m-3) and two levels of residues (1 and 2 ton ha-1). The experimental design consisted in a (3×2×2) complete randomized factorial with three replicates for each test. Moisture of seed furrow, separation of seed and fertilizer, hair pinning and resultant forces acting on the openers were used as assessing indexes. There was no significant difference between soil moisture content in slots created by DDO and MDO at 0-4 cm depth, but at 4-8 cm the in the slot created by MDO moisture content was higher about 9%. Horizontal force for both openers increased with increasing speed and soil bulk density. Vertical force for DDO was negative so it needed additional weight for penetrating in the soil, but vertical force for MDO was positive and, which can solve the challenge of penetration in the soil in DDO. In soft soil with heavy residues some trash was pushed by DDO into seed furrow (hair pinning) but at MDO seed were placed at clean groove. Lateral and vertical separation of seed and fertilizer was performed effectively by MDO (4.5 and 5 cm, respectively) while DDO put seed and fertilizer close to each other. Overall, the Modified Offset Double-disks (MDO) had better performance. So by adapting this opener with no-tillage drillers it would possible to have higher yield in conservation tillage where the most appropriate opener is disk type.

Keywords: Seed Slot, opener's geometry, physical properties.

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Authors: A. I. Augie, Y. Alhassan, U. Z. Magawata

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Geophysical investigation was carried out at wacot rice factory Argungu north-western Nigeria, using the 2D electrical resistivity method. The area falls between latitude 12˚44′23ʺN to 12˚44′50ʺN and longitude 4032′18′′E to 4032′39′′E covering a total area of about 1.85 km. Two profiles were carried out with Wenner configuration using resistivity meter (Ohmega). The data obtained from the study area were modeled using RES2DIVN software which gave an automatic interpretation of the apparent resistivity data. The inverse resistivity models of the profiles show the high resistivity values ranging from 208 Ωm to 651 Ωm. These high resistivity values in the overburden were due to dryness and compactness of the strata that lead to consolidation, which is an indication that the area is free from leachate contaminations. However, from the inverse model, there are regions of low resistivity values (1 Ωm to 18 Ωm), these zones were observed and identified as clayey and the most contaminated zones. The regions of low resistivity thereby indicated the leachate plume or the highly leachate concentrated zones due to similar resistivity values in both clayey and leachate. The regions of leachate are mainly from the factory into the surrounding area and its groundwater. The maximum leachate infiltration was found at depths 1 m to 15.9 m (P1) and 6 m to 15.9 m (P2) vertically, as well as distance along the profiles from 67 m to 75 m (P1), 155 m to 180 m (P1), and 115 m to 192 m (P2) laterally.

Keywords: Contaminant, leachate, soil, groundwater, 2D, electrical, resistivity, Argungu.

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Authors: Rami N. Khushaba, Adel Al-Jumaily

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The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Josephine S. Larkings, Patricia M. Brown

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Mental health professionals views about mental illness is an important issue which has not received enough attention. The negative stigma associated with mental illness can have many negative consequences. Unfortunately, health professionals working with the mentally ill can also exhibit stigma. It has been suggested that causal explanations or beliefs around the causes of mental illness may influence stigma. This study aims to gain a greater insight into stigma through examining stigma among potential mental health professionals. Firstly, results found that potential mental health professionals had relatively low social distance t(205) = -3.62, p <.001. Secondly, an ANOVA indicated that the participants endorsed some causal beliefs more than others, F(1.82, 311.55) = 88.47, p < .001, partial n2 = .34. Moreover, participants endorsed the biological causal explanation the most. Thirdly, results indicated that combined contact (quality and quantity) and causal beliefs (biological, psychological, and environmental) explained a significant proportion of the variance in stigma, R2 = .35, adjusted R2 = .33, F(5, 153) = 16.66, p < .001. Quality of contact was the strongest predictor, with greater quality of contact associated with lower desired social distance. Also, quantity of contact, psychological and environmental causal explanations were also significant predictors of stigma. Greater quantity of contact and higher levels of environmental causal beliefs were associated with lower levels of stigma while psychological causal explanations were associated with higher levels of stigma. A series of multiple regression analyses were conducted that showed the three causal beliefs had different impacts on four factors of stigma (Authoritarian, Benevolence, Social restrictiveness, and Community mental health ideology). These analyses showed that psychological causal beliefs had the most positive impact. More research is required on this topic as it has important implications to the treatment and recovery for people suffering from mental illness.

Keywords: Causal beliefs, contact, health professionals, mental illness stigma, social distance.

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Authors: Phichak Phutrakhul

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The purposes of this research were to study concepts and strategies of human resource development in the automotive manufacturers and to articulate the proposals against the government about the human resource development for automotive industry. In the present study, qualitative study was an in-depth interview in which the qualitative data were collected from the executive or the executive of human resource division from five automotive companies - Toyota Motor (Thailand) Co., Ltd., Nissan Motor (Thailand) Co., Ltd., Mitsubishi Motors (Thailand) Co., Ltd., Honda Automobile (Thailand) Co., Ltd., and Suzuki Motor (Thailand) Co., Ltd. Qualitative data analysis was performed by using inter-coder agreement technique. The research findings were as follows: The external factors included the current conditions of the automotive industry, government’s policy related to the automotive industry, technology, labor market and human resource development systems of the country. The internal factors included management, productive management, organizational strategies, leadership, organizational culture and philosophy of human resource development. These factors were affected to the different concept of human resources development -the traditional human resource development and the strategies of human resource development. The organization focuses on human resources as intellectual capital and uses the strategies of human resource development in all development processes. The strategies of human resource development will enhance the ability of human resources in the organization and the country.

Keywords: Human Resource Development Strategy, Automotive industry, Eco-Cars, ASEAN.

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Authors: Mai. M. Toughan, Ahmed A. A. Sallam, Ashraf O. Abd El-Latif

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Termites are eusocial insects that are found on all continents except Antarctica. Termites have serious destructive impact, damaging local huts and crops of poor subsistence. The annual cost of termite damage and its control is determined in the billions globally. In Egypt, most of these damages are due to the subterranean termite species especially the sand termite, P. hypostoma. Pyrethroids became the primary weapon for subterranean termite control, after the use of chlorpyrifos as a soil termiticide was banned. Despite the important role of pyrethroids in termite control, its extensive use in pest control led to the eventual rise of insecticide resistance which may make many of the pyrethroids ineffective. The ability to diagnose the precise mechanism of pyrethroid resistance in any insect species would be the key component of its management at specified location for a specific population. In the present study, detailed toxicological and biochemical studies was conducted on the mechanism of pyrethroid resistance in P. hypostoma. The susceptibility of field populations of P. hypostoma against deltamethrin, α-cypermethrin and ƛ-cyhalothrin was evaluated. The obtained results revealed that the workers of P. hypostoma have developed high resistance level against the tested pyrethroids. Studies carried out through estimation of detoxification enzyme activity indicated that enhanced esterase and cytochrome P450 activities were probably important mechanisms for pyrethroid resistance in field populations. Elevated esterase activity and also additional esterase isozyme were observed in the pyrethroid-resistant populations compared to the susceptible populations. Strong positive correlation between cytochrome P450 activity and pyrethroid resistance was also reported. |Deltamethrin could be recommended as a resistance-breaking pyrethroid that is active against resistant populations of P. hypostoma.

Keywords: Psammotermes hypostoma, pyrethroid resistance, esterase, cytochrome P450.

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Authors: Man Young Kim

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Catalytic combustion is generally accepted as an environmentally preferred alternative for the generation of heat and power from fossil fuels mainly due to its advantages related to the stable combustion under very lean conditions with low emissions of NOx, CO, and UHC at temperatures lower than those occurred in conventional flame combustion. Despite these advantages, the commercial application of catalytic combustion has been delayed because of complicated reaction processes and the difficulty in developing appropriate catalysts with the required stability and durability. To develop the catalytic combustors, detailed studies on the combustion characteristics of catalytic combustion should be conducted. To the end, in current research, quantitative studies on the combustion characteristics of the catalytic combustors, with a Pd-based catalyst for MCFC power generation systems, relying on numerical simulations have been conducted. In addition, data from experimental studies of variations in outlet temperatures and fuel conversion, taken after operating conditions have been used to validate the present numerical approach. After introducing the governing equations for mass, momentum, and energy equations as well as a description of catalytic combustion kinetics, the effects of the excess air ratio, space velocity, and inlet gas temperature on the catalytic combustion characteristics are extensively investigated. Quantitative comparisons are also conducted with previous experimental data. Finally, some concluding remarks are presented.

Keywords: Catalytic combustion, Methane, BOP, MCFC power generation system, Inlet temperature, Excess air ratio, Space velocity.

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Authors: Luedech Girdwichai, Witthaya Mekhum

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This research intended to develop the model of community management for sustainable utilization by investigating on 2 groups of population, the family heads and the community management team. The population of the former group consisted of family heads from 511 families in 12 areas to complete the questionnaires which were returned at 479 sets. The latter group consisted of the community management team of 12 areas with 1 representative from each area to give the interview. The questionnaires for the family heads consisted of 2 main parts; general information such as occupations, etc. in the form of checklist. The second part dealt with the data on self reliance community development based on 4P Framework, i.e., People (human resource) development, Place (area) development, Product (economic and income source) development, and Plan (community plan) development in the form of rating scales. Data in the 1^st part were calculated to find frequency and percentage while those in the 2^nd part were analyzed to find arithmetic mean and SD. Data from the 2^nd group of population or the community management team were derived from focus group to find factors influencing successful management together with the in depth interview which were analyzed by descriptive statistics. The results showed that 479 family heads reported that the aspect on the implementation of community plan to self reliance community activities based on Sufficient Economy Philosophy and the 4P was at the average of 3.28 or moderate level. When considering in details, it was found that the 1^st aspect was on the area development with the mean of 3.71 or high level followed by human resource development with the mean of 3.44 or moderate level, then, economic and source of income development with the mean of 3.09 or moderate level. The last aspect was community plan development with the mean of 2.89. The results from the small group discussion revealed some factors and guidelines for successful community management as follows: 1) on the People (human resource) development aspect, there was a project to support and develop community leaders. 2) On the aspect of Place (area) development, there was a development on conservative tourism areas. 3) On the aspect of Product (economic and source of income) development, the community leaders promoted the setting of occupational group, saving group, and product processing group. 4) On the aspect of Plan (community plan) development, there was a prioritization through public hearing.

Keywords: Model of community management, sustainable utilization.

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Authors: Jagadish H. Pujar

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Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).

Keywords: Fuzzy Logic Control (FLC), Data DependantFactors(DDF), Model Dependent Factors(MDF), StatisticalError(SE), Short Term Load Forecasting (STLF), MiscellaneousError(ME).

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Authors: Balgaisha G. Mukanova, Yelbek B. Utepov, Aida G. Nazarova, Alisher Z. Imanov

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The problem of identifying local noise sources on a construction site using a sensor system is considered. Mathematical modeling of detected signals on sensors was carried out, considering signal decay and signal delay time between the source and detector. Recordings of noises produced by construction tools were used as a dependence of noise on time. Synthetic sensor data was constructed based on these data, and a model of the propagation of acoustic waves from a point source in the three-dimensional space was applied. All sensors and sources are assumed to be located in the same plane. A source localization method is checked based on the signal time delay between two adjacent detectors and plotting the direction of the source. Based on the two direct lines' crossline, the noise source's position is determined. Cases of one dominant source and the case of two sources in the presence of several other sources of lower intensity are considered. The number of detectors varies from three to eight detectors. The intensity of the noise field in the assessed area is plotted. The signal of a two-second duration is considered. The source is located for subsequent parts of the signal with a duration above 0.04 sec; the final result is obtained by computing the average value.

Keywords: Acoustic model, direction of arrival, inverse source problem, sound localization, urban noises.

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: Curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering.

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted the actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 489 male undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 489 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 489 students, 378 males emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: Physical activity, motivation, the level of physical activity, types of physical activities.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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This paper introduces new algorithms (Fuzzy relative of the CLARANS algorithm FCLARANS and Fuzzy c Medoids based on randomized search FCMRANS) for fuzzy clustering of relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd) in which the within cluster dissimilarity of each cluster is minimized in each iteration by recomputing new medoids given current memberships, FCLARANS minimizes the same objective function minimized by FCMdd by changing current medoids in such away that that the sum of the within cluster dissimilarities is minimized. Computing new medoids may be effected by noise because outliers may join the computation of medoids while the choice of medoids in FCLARANS is dictated by the location of a predominant fraction of points inside a cluster and, therefore, it is less sensitive to the presence of outliers. In FCMRANS the step of computing new medoids in FCMdd is modified to be based on randomized search. Furthermore, a new initialization procedure is developed that add randomness to the initialization procedure used with FCMdd. Both FCLARANS and FCMRANS are compared with the robust and linearized version of fuzzy c-medoids (RFCMdd). Experimental results with different samples of the Reuter-21578, Newsgroups (20NG) and generated datasets with noise show that FCLARANS is more robust than both RFCMdd and FCMRANS. Finally, both FCMRANS and FCLARANS are more efficient and their outputs are almost the same as that of RFCMdd in terms of classification rate.

Keywords: Data Mining, Fuzzy Clustering, Relational Clustering, Medoid-Based Clustering, Cluster Analysis, Unsupervised Learning.

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Authors: Veruska C. Dutra, Mary L. G. S. Senna, Afonso R. Aquino

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Tourists bring many benefits to a local community, encouraging it to be involved in that activity; however, it may also have detrimental effects like garbage, noise, violence, external culture and the damaging of the natural environment among others, which may promote community dissatisfaction. The contact between the tourist and the local community is a concern, especially when the community is located near protected areas. In this case, the community must know the tourist destination well, so it can collaborate in the tourism development without harming the environment. In this context, the present article aims to demonstrate the results of a research study conducted as part of a doctorate program in Sciences from the University of Sao Paulo, Brazil. It had as an objective to elaborate a methodology proposal to measure the local community satisfaction indicator, with applicability on a case study in the Mateiros community located in the surrounding area of the Parque Estadual do Jalapão –PEJ conservation unit in the state of Tocantins, Brazil. This is a study of an interdisciplinary nature that had the deductive method as its guide. The indicator result is going to be presented in this study. It pointed out as negative factors: there is no involvement between the local community and the tourism sector, and there is also dissatisfaction with regard to the town’s basic services. The study showed as positive the local community knowledge about the various attractions in the surrounding area and that the group recognizes the importance of the tourism for the town and life. Concerning the methodology that was used, the results showed that it can collaborate in seeking actions of improvement and involvement of the community in the planning and development of the local tourism. It comes out as an efficient analysis tool, thus enabling the perceiving of the local community point of view.

Keywords: Satisfaction indicator, tourism, community, Jalapão.

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Authors: Justine Jun

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This study aims to reveal migrant women English instructors' workplace learning experiences in Canadian post-secondary institutions in Ontario. Migrant women English instructors in higher education are an understudied group of teachers. This study employs a qualitative research paradigm. Mezirow’s Transformative Learning Theory is an essential lens for the researcher to explain, analyze, and interpret the research data. It is a collaborative research project. The researcher and participants cooperatively create photographic or other artwork data responding to the research questions. Photovoice and arts-informed data collection methodology are the main methods. Research participants engage in the study as co-researchers and inquire about their own workplace learning experiences, actively utilizing their critical self-reflective and dialogic skills. Co-researchers individually select the forms of artwork they prefer to engage with to represent their transformative workplace learning experiences about the Canadian workplace cultures that they underwent while working with colleagues and administrators in the workplace. Once the co-researchers generate their cultural artifacts as research data, they collaboratively interpret their artworks with the researcher and other volunteer co-researchers. Co-researchers jointly investigate the themes emerging from the artworks. They also interpret the meanings of their own and others’ workplace learning experiences embedded in the artworks through interactive one-on-one or group interviews. The following are the research questions that the migrant women English instructor participants examine and answer: (1) What have they learned about their workplace culture and how do they explain their learning experiences? (2) How transformative have their learning experiences been at work? (3) How have their colleagues and administrators influenced their transformative learning? (4) What kind of support have they received? What supports have been valuable to them and what changes would they like to see? (5) What have their learning experiences transformed? (6) What has this arts-informed research process transformed? The study findings implicate English language instructor support currently practiced in post-secondary English language programs in Ontario, Canada, especially for migrant women English instructors. This research is a doctoral empirical study in progress. This study has the urgency to address the research problem that few studies have investigated migrant English instructors’ professional learning and support issues in the workplace, precisely that of English instructors working with adult learners in Canada. While appropriate social and professional support for migrant English instructors is required throughout the country, the present workplace realities in Ontario's English language programs need to be heard soon. For that purpose, the conceptualization of this study is crucial. It makes the investigation of under-represented instructors’ under-researched social phenomena, workplace learning and support, viable and rigorous. This paper demonstrates the robust theorization of English instructors’ workplace experiences using Mezirow’s Transformative Learning Theory in the English language teacher education field. 

Keywords: English teacher education, professional learning, transformative learning theory, workplace learning.

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

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Authors: Jana Kaděrová, Miroslav Vořechovský

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The presented paper copes with an experimental evaluation of a model based on modified Weibull size effect theory. Classical statistical Weibull theory was modified by introducing a new parameter (correlation length lp) representing the spatial autocorrelation of a random mechanical properties of material. This size effect modification was observed on two different materials used in civil engineering: unreinforced (plain) concrete and multi-filament yarns made of alkaliresistant (AR) glass which are used for textile-reinforced concrete. The behavior under flexural, resp. tensile loading was investigated by laboratory experiments. A high number of specimens of different sizes was tested to obtain statistically significant data which were subsequently corrected and statistically processed. Due to a distortion of the measured displacements caused by the unstiff experiment device, only the maximal load values were statistically evaluated. Results of the experiments showed a decreasing strength with an increasing sample length. Size effect curves were obtained and the correlation length was fitted according to measured data. Results did not exclude the existence of the proposed new parameter lp.

Keywords: Statistical size effect, concrete, multi filaments yarns, experiment, autocorrelation length.

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Authors: Maha Salah Abdullah Ismail, Mahmoud M. Alsagheir, Mohammed Salah Abd Allah

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Acute respiratory distress syndrome (ARDS) is characterized by permeability pulmonary edema and refractory hypoxemia. Lung-protective ventilation is still the key of better outcome in ARDS. Prone position reduces the trans-pulmonary pressure gradient, recruiting collapsed regions of the lung without increasing airway pressure or hyperinflation. Prone ventilation showed improved oxygenation and improved outcomes in severe hypoxemic patients with ARDS. This study evaluates the effect of prone positioning on mechanically ventilated patients with ARDS. A quasi-experimental design was carried out at Critical Care Units, on 60 patients. Two tools were utilized to collect data; Socio demographic, medical and clinical outcomes data sheet. Results of the present study indicated that prone position improves oxygenation in patients with severe respiratory distress syndrome. The study recommended that use prone position in patients with severe ARDS, as early as possible and for long sessions. Also, replication of this study on larger probability sample at the different geographical location is highly recommended.

Keywords: Acute respiratory distress syndrome, Critical care, Mechanical ventilation and Prone position.

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Authors: Ola A. Nassr, Ali M. Abd Alridha, Rua A. Naser, Rasha S. Abbas

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Background: Excessive and inappropriate use of antimicrobial agents among hospitalized patients remains an important patient safety and public health issue worldwide. Not only does this behavior incur unnecessary cost but it is also associated with increased morbidity and mortality. The objective of this study is to obtain an insight into the prescribing patterns of antibiotics in surgical and medical wards, to help identify a scope for improvement in service delivery. Method: A simple point prevalence survey included a convenience sample of 200 patients admitted to medical and surgical wards in a government teaching hospital in Baghdad between October 2017 and April 2018. Data were collected by a trained pharmacy intern using a standardized form. Patient’s demographics and details of the prescribed antibiotics, including dose, frequency of dosing and route of administration, were reported. Patients were included if they had been admitted at least 24 hours before the survey. Patients under 18 years of age, having a diagnosis of cancer or shock, or being admitted to the intensive care unit, were excluded. Data were checked and entered by the authors into Excel and were subjected to frequency analysis, which was carried out on anonymized data to protect patient confidentiality. Results: Overall, 88.5% of patients (n=177) received 293 antibiotics during their hospital admission, with a small variation between wards (80%-97%). The average number of antibiotics prescribed per patient was 1.65, ranging from 1.3 for medical patients to 1.95 for surgical patients. Parenteral third-generation cephalosporins were the most commonly prescribed at a rate of 54.3% (n=159) followed by nitroimidazole 29.4% (n=86), quinolones 7.5% (n=22) and macrolides 4.4% (n=13), while carbapenems and aminoglycosides were the least prescribed together accounting for only 4.4% (n=13). The intravenous route was the most common route of administration, used for 96.6% of patients (n=171). Indications were reported in only 63.8% of cases. Culture to identify pathogenic organisms was employed in only 0.5% of cases. Conclusion: Broad-spectrum antibiotics are prescribed at an alarming rate. This practice may provoke antibiotic resistance and adversely affect the patient outcome. Implementation of an antibiotic stewardship program is warranted to enhance the efficacy, safety and cost-effectiveness of antimicrobial agents.

Keywords: Acute care, antibiotic misuse, Iraq, prescribing.

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: Suraiya Jabin, Kamal K. Bharadwaj

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This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system.

Keywords: Hierarchical Production Rule, Data Mining, Learning Classifier System, Fuzzy Subsumption Relation, Subsumption matrix, Reinforcement Learning.

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Authors: Mohammed Gamil Montasser, Angelo Battaglia

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Entrepreneurship is mostly related to the beginning of organization. In growing business organizations, entrepreneurship expands its conceptualization. It reveals itself through new business creation in the active organization, through renewal, change, innovation, creation and development of current organization, through breaking and changing of established rules inside or outside the organization and becomes more flexible, adaptive and competitive, also improving effectiveness of organization activity. Therefore, the topic of entrepreneurship, relates the creation of firms to personal / individual characteristics of the entrepreneurs and their social context. This paper is an empirical study, which aims to address these two gaps in the literature. For this endeavor, we use the latest available data from the Global Entrepreneurship Monitor (GEM) project. This data set is widely regarded as a unique source of information about entrepreneurial activity, as well as the aspirations and attitudes of individuals across a wide number of countries and territories worldwide. This paper tries to contribute to fill this gap, by exploring the key drivers of innovative entrepreneurship in the tourism sector. Our findings are consistent with the existing literature in terms of the individual characteristics of entrepreneurs, but quite surprisingly we find an inverted U-shape relation between human development and innovative entrepreneurship in tourism sector. It has been revealed that tourism entrepreneurs are less likely to have innovative products, compared with entrepreneurs in medium developed countries.

Keywords: GEM, human development, innovative entrepreneurship, occupational choice, tourism business, U-shape relation.

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Authors: Ashly Joseph

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Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to the data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies.

Keywords: Machine Learning, Natural Learning Processing, Denial-of-Service attacks, Sentiment Analysis, Cloud computing.

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Authors: N. S. Usha, E. A. Mary Anita

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Recent innovations in the field of technology led to the use of   wireless sensor networks in various applications, which consists of a number of small, very tiny, low-cost, non-tamper proof and resource constrained sensor nodes. These nodes are often distributed and deployed in an unattended environment, so as to collaborate with each other to share data or information. Amidst various applications, wireless sensor network finds a major role in monitoring battle field in military applications. As these non-tamperproof nodes are deployed in an unattended location, they are vulnerable to many security attacks. Amongst many security attacks, the node replication attack seems to be more threatening to the network users. Node Replication attack is caused by an attacker, who catches one true node, duplicates the first certification and cryptographic materials, makes at least one or more copies of the caught node and spots them at certain key positions in the system to screen or disturb the network operations. Preventing the occurrence of such node replication attacks in network is a challenging task. In this survey article, we provide the classification of detection schemes and also explore the various schemes proposed in each category. Also, we compare the various detection schemes against certain evaluation parameters and also its limitations. Finally, we provide some suggestions for carrying out future research work against such attacks.

Keywords: Clone node, data security, detection schemes, node replication attack, wireless sensor networks.

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: Cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing.

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Authors: Z. N. Haji, S. O. Oyadiji, H. Samami, O. Farrell

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The prediction of the vibration response of rubber products by analytical or numerical method depends mainly on the predefined intrinsic material properties such as Young’s modulus, damping factor and Poisson’s ratio. Such intrinsic properties are determined experimentally by subjecting a bonded rubber sample to compression tests. The compression tests on such a sample yield an apparent Young’s modulus which is greater in magnitude than the intrinsic Young’s modulus of the rubber. As a result, many analytical equations have been developed to determine Young’s modulus from an apparent Young’s modulus of bonded rubber materials. In this work, the applicability of some of these analytical equations is assessed via experimental testing. The assessment is based on testing of vulcanized nitrile butadiene rubber (NBR70) samples using tensile test and compression test methods. The analytical equations are used to determine the intrinsic Young’s modulus from the apparent modulus that is derived from the compression test data of the bonded rubber samples. Then, these Young’s moduli are compared with the actual Young’s modulus that is derived from the tensile test data. The results show significant discrepancy between the Young’s modulus derived using the analytical equations and the actual Young’s modulus.

Keywords: Bonded rubber, quasi-static test, shape factor, apparent Young’s modulus.

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