Search results for: trans-border cluster

Authors: Shamsa Al Sharji, Athar Al Jabri, Haitham Al Dughaishi, Mirfat Al Barwani, Raja Al Rawahi, Raiya Al Rajhi, Shurooq Al Ruqaishi, Thamreen Al Zadjali, Iman Al Humaidi

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Background: External Ventricular Drains (EVDs) are critical in managing traumatic brain injuries and hydrocephalus by controlling intracranial pressure, but they carry a high risk of infection. Infection rates vary globally, ranging from 5% to 45%, leading to increased morbidity, prolonged hospital stays, and higher healthcare costs. Nursing protocols play a pivotal role in reducing these infection rates. This study investigates the impact of a structured nursing protocol on EVD-associated infections in adult neurosurgery patients at the Directorate General of Khoula Hospital, Oman, from January to September 2024. Methods: A cluster-randomized trial was conducted across neurosurgery wards and the ICU. The intervention group followed a comprehensive nursing protocol, including strict sterile insertion, standardized dressing changes, infection control training, and regular clinical audits. The control group received standard care. The primary outcome was the incidence of EVD-associated infections, with secondary outcomes including protocol compliance, infection severity, recovery times, length of stay, and 30-day mortality. Statistical analysis was conducted using Chi-square tests, paired t-tests, and logistic regression to assess the differences between groups. Results: The study involved 75 patients, with an overall infection rate of 13.3%. The intervention group showed a reduced infection rate of 8.9% compared to 20% in the control group. Compliance rates for key nursing actions were high, with 89.7% for hand hygiene and 86.2% for wound dressing. The relative risk of infection was 0.44 in the intervention group, reflecting a 55.6% reduction. Logistic regression identified obesity as a significant predictor of EVD infections. Although mortality rates were slightly higher in the intervention group, the number needed to treat (NNT) of 9 suggests that the nursing protocol may improve survival outcomes. Conclusion: This study demonstrates that structured nursing protocols can reduce EVD-related infections and improve patient outcomes in neurosurgery. While the findings are promising, further research with larger sample sizes is needed to confirm these results and optimize infection control strategies in neurosurgical care.

Keywords: EVD, CSF, nursing protocol, EVD infection

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Authors: Michael Safwat Kotit Istemalek

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In this study, a project on a small project called Zeytinseli, which plays an important role from the beginning to the end of olive oil and olive oil production, is presented with the help of tourism companies that play an important role in the tourism sector. In the study, first of all, a framework of ideas about travel agency, tourism, specific tourism agency and rural tourism was created and tourism knowledge in the modern world was emphasized. After this, the "olive", which had an important place in both mythology and the religion of God, disappeared in the field of rural tourism. Since Didim Zeytinseli is the Aydın district, accommodation prices were calculated within the scope of the project and a 15-day factory tour was given at the end of the project. It can be said that the study is an original study as it covers not only environmental and agricultural tourism but also cultural tourism and non-traditional tourism98.

Keywords: financial problems, the problems of tourism businesses, tourism businesses, internet, marketing, tourism, tourism management economic competitiveness, enhancing competitiveness

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Touraj Hashemi, Zahara Saeidi, Paxshan H. Gader-l-Shateri

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The present study aimed to determine the relationship between dimensions of psychological capital and psychological well-being. This research was conducted with a correlatiove method. The study population included the students of Sulaymaniyah, Garmian, and Halabja Universities in the Kurdistan region of Iraq. Therefore, using the one-stage cluster method, 300 subjects were selected and completed Riff's psychological well-being scale, and Luthans' psychological capital questionnaire. Data were analyzed using the multiple regression method. Results showed that self-efficacy, optimism, hope, and resilience had a positive relationship with psychological well-being. Hence, it can be concluded the four dimensions of psychological capital are able, in addition to modulating the effects of stress sources, to set the stage for the motivational use of life's stressors in order to develop new challenges and help the individual to continuous effort in order to develop new goals and expand happiness.

Keywords: psychological well-being, self-efficacy, optimism, hope, resilience

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Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai

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Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.

Keywords: knowledge clustering, knowledge acquisition, knowledge based engineering, knowledge cell, biologically inspired design

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Authors: Chidiebere C. Agoha, Armstong C. Awuzie, Chukwuebuka N. Onwubuariri, Joy O. Njoku

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Spatial analysis is a field of study that utilizes geographic or spatial information to understand and analyze patterns, relationships, and trends in data. It is characterized by the use of geographic or spatial information, which allows for the analysis of data in the context of its location and surroundings. It is different from non-spatial or aspatial techniques, which do not consider the geographic context and may not provide as complete of an understanding of the data. Spatial analysis is applied in a variety of fields, which includes urban planning, environmental science, geosciences, epidemiology, marketing, to gain insights and make decisions about complex spatial problems. This review paper explores definitions of spatial analysis from various sources, including examples of its application and different analysis techniques such as Buffer analysis, interpolation, and Kernel density analysis (multi-distance spatial cluster analysis). It also contrasts spatial analysis with non-spatial analysis.

Keywords: aspatial technique, buffer analysis, epidemiology, interpolation

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: Paramet Damchoo

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The purpose of this study is to study the factors that influence the decision making of foreign volunteer tourists in Thailand. A sample size was 400 drawn from 10 provinces of Thailand using cluster sampling method. The factor analysis was used to analysis the data. The findings indicate that volunteer tourism which was based in Thailand contained a total of 45 activities which could be divided into 4 categories. The most of these tourists were from Europe including UK and Scandinavia which was 54.50 percent. Moreover, the tourists were male rather than female and 63.50 Percent of them ware younger than 20 years old. It is also found that there are 67.00 percent of the tourists used website to find where the volunteer tourism was based. Finally, the factors that influence the decision making of foreign volunteer tourists in Thailand consist of a wide variety of activities together with a flexibility in their activities and also low prices.

Keywords: decision making, volunteer tourism, special interest tourism, GAP year

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Authors: Çağrı Özgün Kibiroğlu, Zeynep Turgut

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This paper considers a hub location problem where the network service area partitioned into predetermined zones (represented by node clusters is given) and potential hub nodes capacity levels are determined a priori as a selection criteria of hub to investigate congestion effect on network. The objective is to design hub network by determining all required hub locations in the node clusters and also allocate non-hub nodes to hubs such that the total cost including transportation cost, opening cost of hubs and penalty cost for exceed of capacity level at hubs is minimized. A mixed integer linear programming model is developed introducing additional constraints to the traditional model of capacitated multiple allocation hub location problem and empirically tested.

Keywords: hub location problem, p-hub median problem, clustering, congestion

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Authors: Maryam Eskafi

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Purpose: Frequent referrals of the martyr offspring to The Martyr Foundation and studying divorce documents revealed the depth of family quarrels among the martyr families. For this reason, conducting the research of this type can be effective. Method: Research method is survey. Statistical population is the total of married offspring of the martyr living in Mashhad City of Iran. Data were gathered by using questionnaire administered with a sample of 250 selected by using cluster sampling method. Results: Family order may lead to the ground actions for divorce through life satisfaction. Conclusion: life satisfaction with -0.62 beta value has a strong negative effect on the ground actions for divorce.

Keywords: ground actions for divorce, life satisfaction, family order, satisfaction

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Authors: Nadarajah I. Ramesh

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Much of the research on stochastic point process models for rainfall has focused on Poisson cluster models constructed from either the Neyman-Scott or Bartlett-Lewis processes. The doubly stochastic Poisson process provides a rich class of point process models, especially for fine-scale rainfall modelling. This paper provides an account of recent development on this topic and presents the results based on some of the fine-scale rainfall models constructed from this class of stochastic point processes. Amongst the literature on stochastic models for rainfall, greater emphasis has been placed on modelling rainfall data recorded at hourly or daily aggregation levels. Stochastic models for sub-hourly rainfall are equally important, as there is a need to reproduce rainfall time series at fine temporal resolutions in some hydrological applications. For example, the study of climate change impacts on hydrology and water management initiatives requires the availability of data at fine temporal resolutions. One approach to generating such rainfall data relies on the combination of an hourly stochastic rainfall simulator, together with a disaggregator making use of downscaling techniques. Recent work on this topic adopted a different approach by developing specialist stochastic point process models for fine-scale rainfall aimed at generating synthetic precipitation time series directly from the proposed stochastic model. One strand of this approach focused on developing a class of doubly stochastic Poisson process (DSPP) models for fine-scale rainfall to analyse data collected in the form of rainfall bucket tip time series. In this context, the arrival pattern of rain gauge bucket tip times N(t) is viewed as a DSPP whose rate of occurrence varies according to an unobserved finite state irreducible Markov process X(t). Since the likelihood function of this process can be obtained, by conditioning on the underlying Markov process X(t), the models were fitted with maximum likelihood methods. The proposed models were applied directly to the raw data collected by tipping-bucket rain gauges, thus avoiding the need to convert tip-times to rainfall depths prior to fitting the models. One advantage of this approach was that the use of maximum likelihood methods enables a more straightforward estimation of parameter uncertainty and comparison of sub-models of interest. Another strand of this approach employed the DSPP model for the arrivals of rain cells and attached a pulse or a cluster of pulses to each rain cell. Different mechanisms for the pattern of the pulse process were used to construct variants of this model. We present the results of these models when they were fitted to hourly and sub-hourly rainfall data. The results of our analysis suggest that the proposed class of stochastic models is capable of reproducing the fine-scale structure of the rainfall process, and hence provides a useful tool in hydrological modelling.

Keywords: fine-scale rainfall, maximum likelihood, point process, stochastic model

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Authors: Aisha Ijaz

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The paper focuses on the impact of Muslim religiosity on convenience food purchases and involvement experienced in a non-Muslim culture. There is a scarcity of research on the purchasing patterns of Muslim diaspora communities residing in risk societies, particularly in contexts where there is an increasing inclination toward industrialized food items alongside a renewed interest in the concept of natural foods. The United Kingdom serves as an appropriate setting for this study due to the increasing Muslim population in the country, paralleled by the expanding Halal Food Market. A multi-dimensional framework is proposed, testing for five forms of involvement, specifically Purchase Decision Involvement, Product Involvement, Behavioural Involvement, Intrinsic Risk and Extrinsic Risk. Quantitative cross-sectional consumer data were collected through a face-to-face survey contact method with 141 Muslims during the summer of 2020 in Liverpool located in the Northwest of England. proportion formula was utilitsed, and the population of interest was stratified by gender and age before recruitment took place through local mosques and community centers. Six input variables were used (intrinsic religiosity and involvement dimensions), dividing the sample into 4 clusters using the Two-Step Cluster Analysis procedure in SPSS. Nuanced variances were observed in the type of involvement experienced by religiosity group, which influences behaviour when purchasing convenience food. Four distinct market segments were identified: highly religious ego-involving (39.7%), less religious active (26.2%), highly religious unaware (16.3%), less religious concerned (17.7%). These segments differ significantly with respects to their involvement, behavioural variables (place of purchase and information sources used), socio-cultural (acculturation and social class), and individual characteristics. Choosing the appropriate convenience food is centrally related to the value system of highly religious ego-involving first-generation Muslims, which explains their preference for shopping at ethnic food stores. Less religious active consumers are older and highly alert in information processing to make the optimal food choice, relying heavily on product label sources. Highly religious unaware Muslims are less dietary acculturated to the UK diet and tend to rely on digital and expert advice sources. The less-religious concerned segment, who are typified by younger age and third generation, are engaged with the purchase process because they are worried about making unsuitable food choices. Research implications are outlined and potential avenues for further explorations are identified.

Keywords: consumer behaviour, consumption, convenience food, religion, muslims, UK

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Authors: Saurabh Ahluwalia, Sul Kassicieh

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The conventional wisdom has maintained that being in proximity to entrepreneurial ecosystems helps startups to raise financing, develop and grow. In this paper, we examine the effect of a major component of an entrepreneurial ecosystem- financial or venture capital clusters on the exit of a startup through mergers and acquisitions (M&A). We find that the presence of a venture capitalist in a venture capital (VC) cluster is a major success factor for M&A exits. The location of startups in the top VC clusters did not turn out to be significant for success. Our results are robust to different specifications of the model that use different time periods, types of success, the reputation of VC, industry and the quality of the startup company. Our results provide evidence for VCs, startups and policymakers who want to better understand the components of entrepreneurial ecosystems and their relation to the M&A exits of startups.

Keywords: financial institution, mergers and acquisitions, startup financing, venture capital

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Authors: Pei-Chann Chang, Jhen-Fu Liao, Chin-Hung Teng, Meng-Hui Chen

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This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset.

Keywords: artificial immune system, collaborative filtering, recommendation system, similarity

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Authors: Minami Ito, Akihiro Iijima

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On March 11, 2011, the Great East Japan Earthquake occurred off the coast of Sanriku, Japan. It is important to build a sustainable society through the reconstruction process rather than simply restoring the infrastructure. To compare the goals of reconstruction plans of quake-stricken municipalities, Japanese language morphological analysis was performed by using text mining techniques. Frequently-used nouns were sorted into four main categories of “life”, “disaster prevention”, “economy”, and “harmony with environment”. Because Soma City is affected by nuclear accident, sentences tagged to “harmony with environment” tended to be frequent compared to the other municipalities. Results from cluster analysis and principle component analysis clearly indicated that the local government reinforces the efforts to reduce risks from radiation exposure as a top priority.

Keywords: eco-friendly reconstruction, harmony with environment, decontamination, nuclear disaster

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Authors: Zhenqiu Liu

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Visualizing the heterogeneity within cell-populations for single-cell RNA-seq data is crucial for studying the functional diversity of a cell. However, because of the high level of noises, outlier, and dropouts, it is very challenging to measure the cell-to-cell similarity (distance), visualize and cluster the data in a low-dimension. Minimum volume embedding (MVE) projects the data into a lower-dimensional space and is a promising tool for data visualization. However, it is computationally inefficient to solve a semi-definite programming (SDP) when the sample size is large. Therefore, it is not applicable to single-cell RNA-seq data with thousands of samples. In this paper, we develop an efficient algorithm with an accelerated proximal gradient method and visualize the single-cell RNA-seq data efficiently. We demonstrate that the proposed approach separates known subpopulations more accurately in single-cell data sets than other existing dimension reduction methods.

Keywords: single-cell RNA-seq, minimum volume embedding, visualization, accelerated proximal gradient method

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Authors: Thais O. de Paula, Marjorie R. A. Sarmiento, Francis M. Borges, Alessandra B. Ferreira-Machado, Juliana A. Resende, Dioneia E. Cesar, Vania L. Silva, Claudio G. Diniz

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Obesity is currently a worldwide public health threat, being considered a pandemic multifactorial disease related to the human gut microbiota (GM). Add to that GM is considered an important reservoir of antimicrobial resistance genes (ARG) and little is known on GM and ARG in obesity, considering the altered physiology and xenobiotics intake. As regional and social behavior may play important roles in GM modulation, and most of the studies are based on small sample size and various methodological approaches resulting in difficulties for data comparisons, this study was focused on the investigation of GM bacterial diversity in obese (OB), overweight (OW) and eutrophic individuals (ET) considering their nutritional, clinical and social characteristics; and comparative screening of AGR related to their physiology and xenobiotics intake. Microbial community was accessed by FISH considering phyla as a taxonomic level, and PCR-DGGE followed by dendrograms evaluation (UPGMA method) from fecal metagenome of 72 volunteers classified according to their body mass index (BMI). Nutritional, clinical, social parameters and xenobiotics intake were recorded for correlation analysis. The fecal metagenome was also used as template for PCR targeting 59 different ARG. Overall, 62% of OB were hypertensive, and 12% or 4% were, regarding the OW and ET individuals. Most of the OB were rated as low income (80%). Lower relative bacterial densities were observed in the OB compared to ET for almost all studied taxa (p < 0.05) with Firmicutes/Bacteroidetes ratio increased in the OB group. OW individuals showed a bacterial density representative of GM more likely to the OB. All the participants were clustered in 3 different groups based on the PCR-DGGE fingerprint patterns (C1, C2, C3), being OB mostly grouped in C1 (83.3%) and ET mostly grouped in C3 (50%). The cluster C2 showed to be transitional. Among 27 ARG detected, a cluster of 17 was observed in all groups suggesting a common core. In general, ARG were observed mostly within OB individuals followed by OW and ET. The ratio between ARG and bacterial groups may suggest that AGR were more related to enterobacteria. Positive correlations were observed between ARG and BMI, calories and xenobiotics intake (especially use of sweeteners). As with nutritional and clinical characteristics, our data may suggest that GM of OW individuals behave in a heterogeneous pattern, occasionally more likely to the OB or to the ET. Regardless the regional and social behaviors of our population, the methodological approaches in this study were complementary and confirmatory. The imbalance of GM over the health-disease interface in obesity is a matter of fact, but its influence in host's physiology is still to be clearly elucidated to help understanding the multifactorial etiology of obesity. Although the results are in agreement with observations that GM is altered in obesity, the altered physiology in OB individuals seems to be also associated to the increased xenobiotics intake and may interfere with GM towards antimicrobial resistance, as observed by the fecal metagenome and ARG screening. Support: FAPEMIG, CNPQ, CAPES, PPGCBIO/UFJF.

Keywords: antimicrobial resistance, bacterial diversity, gut microbiota, obesity

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Authors: S. Rustagi, N. S. Sodhi, B. Dhillon, T. Kaur

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The objective of this study was to check whether there is any relationship between electromyographic (EMG) and textural parameters during food texture evaluation. In this study, a total of eighteen mastication variables were measured for entire mastication, per chew mastication and three different stages of mastication (viz. early, middle and late) by EMG for five different foods using eight human subjects. Cluster analysis was used to reduce the number of mastication variables from 18 to 5, so that principal component analysis (PCA) could be applied on them. The PCA further resulted in two meaningful principal components. The principal component scores for each food were measured and correlated with five textural parameters (viz. hardness, cohesiveness, chewiness, gumminess and adhesiveness). Correlation coefficients were found to be statistically significant (p < 0.10) for cohesiveness and adhesiveness while if we reduce the significance level (p < 0.20) then chewiness also showed correlation with mastication parameters.

Keywords: electromyography, mastication, sensory, texture

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Authors: Dechao Meng, Yongqi Dong, Qiyuan Feng, Zhangzhang Cui, Xiang Hu, Haoliang Huang, Genhao Liang, Huanhua Wang, Hua Zhou, Hawoong Hong, Jinghua Guo, Qingyou Lu, Xiaofang Zhai, Yalin Lu

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Great efforts have been taken to reveal the intrinsic origins of emerging ferromagnetism (FM) in strained LaCoO₃ (LCO) films. However, some macro magnetic performances of LCO are still not well understood and even controversial, such as magnetic anisotropy. Determining and understanding magnetic anisotropy might help to find the true causes of FM in turn. Perpendicular magnetic anisotropy (PMA) was the first time to be directly observed in high-quality LCO films with different thickness. The in-plane (IP) and out of plane (OOP) remnant magnetic moment ratio of 30 unit cell (u.c.) films is as large as 20. The easy axis lays in the OOP direction with an IP/OOP coercive field ratio of 10. What's more, the PMA could be simply tuned by changing the thickness. With the thickness increases, the IP/OOP magnetic moment ratio remarkably decrease with magnetic easy axis changing from OOP to IP. Such a huge and tunable PMA performance exhibit strong potentials in fundamental researches or applications. What causes PMA is the first concern. More OOP orbitals occupation may be one of the micro reasons of PMA. A cluster-like magnetic domain pattern was found in 30 u.c. with no obvious color contrasts, similar to that of LaAlO₃/SrTiO₃ films. And the nanosize domains could not be totally switched even at a large OOP magnetic field of 23 T. It indicates strong IP characters or none OOP magnetism of some clusters. The IP magnetic domains might influence the magnetic performance and help to form PMA. Meanwhile some possible nonmagnetic clusters might be the reason why the measured moments of LCO films are smaller than the calculated values 2 μB/Co, one of the biggest confusions in LCO films.What tunes PMA seems much more interesting. Totally different magnetic domain patterns were found in 180 u.c. films with cluster magnetic domains surrounded by < 110 > cross-hatch lines. These lines were regarded as structure domain walls (DWs) determined by 3D reciprocal space mapping (RSM). Two groups of in-plane features with fourfold symmetry were observed near the film diffraction peaks in (002) 3D-RSM. One is along < 110 > directions with a larger intensity, which is well match the lines on the surfaces. The other is much weaker and along < 100 > directions, which is from the normal lattice titling of films deposited on cubic substrates. The < 110 > domain features obtained from (103) and (113) 3D-RSMs exhibit similar evolution of the DWs percentages and magnetic behavior. Structure domains and domain walls are believed to tune PMA performances by transform more IP magnetic moments to OOP. Last but not the least, thick films with lots of structure domains exhibit different electrical transport behaviors. A metal-to-insulator transition (MIT) and an angular dependent negative magnetic resistivity were observed near 150 K, higher than FM transition temperature but similar to that of spin-orbital coupling related 1/4 order diffraction peaks.

Keywords: structure domain, magnetic anisotropy, magnetic domain, domain wall, 3D-RSM, strain

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Authors: Shinji Kawakura, Ryosuke Shibasaki

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We have been grouping and developing various kinds of practical, promising sensing applied systems concerning agricultural advancement and technical tradition (guidance). These include advanced devices to secure real-time data related to worker motion, and we analyze by methods of various advanced statistics and human dynamics (e.g. primary component analysis, Ward system based cluster analysis, and mapping). What is more, we have been considering worker daily health and safety issues. Targeted fields are mainly common farms, meadows, and gardens. After then, we observed and discussed time-line style, changing data. And, we made some suggestions. The entire plan makes it possible to improve both the aforementioned applied systems and farms.

Keywords: advanced statistical analysis, wearable sensing system, tradition of skill, supporting for workers, detecting crisis

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Authors: Nahid Parween Anwar, Sadia Muzaffar Bhutta, Takbir Ali

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Mentoring is a structured activity which is designed to facilitate engagement between mentor and mentee to enhance mentee’s professional capability as an effective teacher. Both mentor and mentee are important elements of the ‘mentoring equation’ and play important roles in nourishing this dynamic, collaborative and reciprocal relationship. Cluster-Based Mentoring Programme (CBMP) provides an indigenous example of a project which focused on development of primary school teachers in selected clusters with a particular focus on their classroom practice. A study was designed to examine the efficacy of CBMP as part of Strengthening Teacher Education in Pakistan (STEP) project. This paper presents results of one of the components of this study. As part of the larger study, a cross-sectional survey was employed to explore nature and patterns of mentoring process from mentees’ perspectives in the selected districts of Sindh and Balochistan. This paper focuses on the results of the study related to the question: What are mentees’ perceptions of their mentors’ support for enhancing their classroom practice during mentoring process? Data were collected from mentees (n=1148) using a 5-point scale -‘Mentoring for Effective Primary Teaching’ (MEPT). MEPT focuses on seven factors of mentoring: personal attributes, pedagogical knowledge, modelling, feedback, system requirement, development and use of material, and gender equality. Data were analysed using SPSS 20. Mentees perceptions of mentoring practice of their mentors were summarized using mean and standard deviation. Results showed that mean scale scores on mentees’ perceptions of their mentors’ practices fell between 3.58 (system requirement) and 4.55 (personal attributes). Mentees’ perceives personal attribute of the mentor as the most significant factor (M=4.55) towards streamlining mentoring process by building good relationship between mentor and mentees. Furthermore, mentees have shared positive views about their mentors efforts towards promoting gender impartiality (M=4.54) during workshop and follow up visit. Contrary to this, mentees felt that more could have been done by their mentors in sharing knowledge about system requirement (e.g. school policies, national curriculum). Furthermore, some of the aspects in high scoring factors were highlighted by the mentees as areas for further improvement (e.g. assistance in timetabling, written feedback, encouragement to develop learning corners). Mentees’ perceptions of their mentors’ practices may assist in determining mentoring needs. The results may prove useful for the professional development programme for the mentors and mentees for specific mentoring programme in order to enhance practices in primary classrooms in Pakistan. Results would contribute into the body of much-needed knowledge from developing context.

Keywords: cluster-based mentoring programme, mentoring for effective primary teaching (MEPT), professional development, survey

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Penelope Paliadelis, Asheley Jones, Glenn Campbell

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This paper discusses the development of co-designed capability frameworks for two large multinational organizations led by a university department. The aim was to create evidence-based, integrated capability frameworks that could define, identify, and measure human skill capabilities independent of specific work roles. The frameworks capture and cluster human skills required in the workplace and capture their application at various levels of mastery. Identified capability gaps inform targeted learning opportunities for workers to enhance their employability skills. The paper highlights the value of this evidence-based framework development process in capturing, defining, and assessing desired human-focused capabilities for organizational growth and success.

Keywords: capability framework, human skills, work-integrated learning, credentialing, digital badging

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Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Hossein Vahedi, Zahra Soleymani Fard

Abstract:

SefidKuh area in Baluchistan, Iran, is one of the impossible areas which the focused archeological investigations have not been on it. In the Sefidkuh area, there are colonies as if they were stopped in the Neolithic and Chalcolithic ages. These colonies exhibit culturally specific behaviors, which their study can reveal much of the cultural nature of the Neolithic, Chalcolithic inhabitants of the region. In the villages of this area, still, circular architecture is used in different types. The political management of the villages in the region is also the responsibility of Khan, whose characteristics can be compared to the prehistoric era. These people's livelihoods include hunting, animal husbandry, horticulture, and limited crop storage. Residents of Sefidkuh use the exchange of goods to obtain needed supplies that they themselves cannot produce. In this area, there are central location villages that are quite similar to the cluster model, and the Great Khan leads the surrounding villages.

Keywords: archaeology, social structure, neolithic, chalcolithic, Sefidkuh, Baluchistan

Procedia PDF Downloads 118

Authors: Yuan Xu, Xun Liang, Meina Zhang

Abstract:

The rise and attenuation of the public opinion broadcast of an emergent accident, in the social network, has a close relationship with the dynamic development of its subevents cluster. In this article, we take Tianjin Port explosion's subevents as an example to research the dynamic propagation discipline of Internet public opinion in a sudden accident, and analyze the overall structure of dynamic propagation to propose four different routes for subevents clusters propagation. We also generate network diagrams for the dynamic public opinion propagation, analyze each propagation type specifically. Based on this, suggestions on the supervision and guidance of Internet public opinion broadcast can be made.

Keywords: network dynamic transmission modes, emergent subevents clusters, Tianjin Port explosion, public opinion supervision

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Authors: Farzaneh Golshekoh

Abstract:

The aim of this study was to examine the relationship between religious beliefs, family responsibility and emotional atmosphere with a tendency to addiction in high school female students in Ahwaz. The sample consisted of 250 students who were selected by cluster random sampling from among all high school female students in Ahvaz. Measuring tools were Iranian tendency towards addiction (IAPS), responsibility California Psychological Inventory (CPI), emotional family atmosphere (AFC) and religious beliefs. The simple correlation coefficient at α=0/05 showed that there is a significant negative relationship between religious beliefs, family responsibility and emotional atmosphere with a tendency to abuse female students. The regression analysis showed that the variables of the emotional atmosphere of the family and religious beliefs as predictors of female students have a tendency to addiction.

Keywords: emotional atmosphere, family responsibility, religious beliefs, tendency to addiction

Procedia PDF Downloads 435

Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

Abstract:

Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

Procedia PDF Downloads 497

Authors: Lutful Karim, Mohammed S. Al-kahtani

Abstract:

Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

Procedia PDF Downloads 346