Search results for: agglomerative hierarchical clustering (AHC)

Authors: Tariqul Islam, Shaheen Mollah

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The study's main objective is to assess the relationship between psychological adjustment and parental acceptance-rejection, teacher acceptance-rejection, and best friend acceptance-rejection among secondary school students. This study was conducted on a sample of 300 (6th through 10th-grade students) recruited from over ten schools in Dhaka. While the schools were selected purposively, the respondents within each school were selected conveniently. The collected data were analyzed using Pearson product-moment correlation, hierarchical regression, and simultaneous regression analysis. The results showed that psychological adjustment is positively correlated with paternal, maternal, teacher, and best friend acceptance. The paternal acceptance was significantly connected with maternal acceptance. The teacher and best friend acceptance are correlated substantially with paternal and maternal acceptance. The hierarchical multiple regressions indicated that maternal, paternal, teacher, and best friend acceptance-rejection contributed significantly to students' psychological adjustment. The results revealed substantial independent contributions of maternal, paternal, teacher, and best friend acceptance on the students' psychological adjustment. The simultaneous regression analysis indicates that the maternal and best friend acceptances (but not paternal acceptance) were significant predictors of psychological adjustments. It showed that 41.7% variability in psychological adjustment could be explained by paternal, maternal, and best friend acceptance. The findings of the present study are exciting. They may contribute to developing insight in parents and best friends for behaving properly with their offspring and friend, respectively, for better psychological adjustment.

Keywords: adjustment, parenting, rejection, acceptance

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Authors: Naouelle Azzag, Nadia Haddad, Benoit Durand, Elisabeth Petit, Ali Ammouche, Bruno Chomel, Henri J. Boulouis

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Bartonella henselae is a small, gram negative, arthropod-borne bacterium that has been shown to cause multiple clinical manifestations in humans including cat scratch disease, bacillary angiomatosis, endocarditis, and bacteremia. In this research, we report the results of a cross sectional study of Bartonella henselae bacteremia in stray cats from Algiers. Whole blood of 227 stray cats from Algiers was tested for the presence of Bartonella species by culture and for the evaluation of the genetic diversity of B. henselae strains by multi-locus variable number of tandem repeats assay (MLVA). Bacteremia prevalence was 17% and only B. henselae was identified. Type I was the predominant type (64%). MLVA typing of 259 strains from 30 bacteremic cats revealed 52 different profiles. 51 of these profiles were specific to Algerian cats/identified for the first time. 20/30 cats (67%) harbored 2 to 7 MLVA profiles simultaneously. The similarity of MLVA profiles obtained from the same cat, neighbor-joining clustering and structure-neighbor clustering showed that such a diversity likely results from two different mechanisms occurring either independently or simultaneously independent infections and genetic drift from a primary strain.

Keywords: Bartonella, cat, MLVA, genetic

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Authors: Michel Zaitouni, Mohamed Nassar

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The purpose of this study is first, to explore the effect of employees’ perception of justice on trust in the supervisor in the context of performance appraisal; Second, to assess the role of perceived supervisor support as a mediator between organizational justice and trust in the supervisor in a non-western society such as Kuwait.The survey data consisted of 415 employees working at different hierarchical levels in three major banks in Kuwait. Hierarchical regression analysis was used to test the research hypotheses. Results supported hypothesized relationships between distributive, informational and interpersonal justice and trust in the supervisor but failed to support that procedural justice positively and significantly relate to trust in the supervisor. Moreover, results found that this relationship is partially mediated by perceived supervisor support. A potential limitation of this study is that data were obtained from the same industry which limits the generalizability of this study to other industries. Moreover, a longitudinal research will be helpful to strengthen the mediating relationship. The findings provide valuable information for the development of common perspectives regarding the perception of justice in the context of performance appraisal between the western and non-western societies. The paper has the privilege to explore additional relationships related to justice perceptions in the Kuwaiti banking sector, whereas previous research focused mainly on procedural and distributive justice as predictors of trust in the supervisor.

Keywords: Kuwait, organizational justice, perceived supervisor support, trust in the supervisor

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Authors: Tauqir A. Moughal, Fusheng Yu, Abeer Mazher

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Land-cover and land-use change information are important because of their practical uses in various applications, including deforestation, damage assessment, disasters monitoring, urban expansion, planning, and land management. Therefore, developing change detection methods for remote sensing images is an important ongoing research agenda. However, detection of change through optical remote sensing images is not a trivial task due to many factors including the vagueness between the boundaries of changed and unchanged regions and spatial dependence of the pixels to its neighborhood. In this paper, we propose a binary change detection technique for bi-temporal optical remote sensing images. As in most of the optical remote sensing images, the transition between the two clusters (change and no change) is overlapping and the existing methods are incapable of providing the accurate cluster boundaries. In this regard, a methodology has been proposed which uses the fuzzy c-means clustering to tackle the problem of vagueness in the changed and unchanged class by formulating the soft boundaries between them. Furthermore, in order to exploit the neighborhood information of the pixels, the input patterns are generated corresponding to each pixel from bi-temporal images using 3×3, 5×5 and 7×7 window. The between images and within image spatial dependence of the pixels to its neighborhood is quantified by using Pearson product moment correlation and Moran’s I statistics, respectively. The proposed technique consists of two phases. At first, between images and within image spatial correlation is calculated to utilize the information that the pixels at different locations may not be independent. Second, fuzzy c-means technique is used to produce two clusters from input feature by not only taking care of vagueness between the changed and unchanged class but also by exploiting the spatial correlation of the pixels. To show the effectiveness of the proposed technique, experiments are conducted on multispectral and bi-temporal remote sensing images. A subset (2100×1212 pixels) of a pan-sharpened, bi-temporal Landsat 5 thematic mapper optical image of Los Angeles, California, is used in this study which shows a long period of the forest fire continued from July until October 2009. Early forest fire and later forest fire optical remote sensing images were acquired on July 5, 2009 and October 25, 2009, respectively. The proposed technique is used to detect the fire (which causes change on earth’s surface) and compared with the existing K-means clustering technique. Experimental results showed that proposed technique performs better than the already existing technique. The proposed technique can be easily extendable for optical hyperspectral images and is suitable for many practical applications.

Keywords: burned area, change detection, correlation, fuzzy clustering, optical remote sensing

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Authors: Vinko Lešić, Mario Vašak, Anita Martinčević, Marko Gulin, Antonio Starčić, Hrvoje Novak

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With 40% of total world energy consumption, building systems are developing into technically complex large energy consumers suitable for application of sophisticated power management approaches to largely increase the energy efficiency and even make them active energy market participants. Centralized control system of building heating and cooling managed by economically-optimal model predictive control shows promising results with estimated 30% of energy efficiency increase. The research is focused on implementation of such a method on a case study performed on two floors of our faculty building with corresponding sensors wireless data acquisition, remote heating/cooling units and central climate controller. Building walls are mathematically modeled with corresponding material types, surface shapes and sizes. Models are then exploited to predict thermal characteristics and changes in different building zones. Exterior influences such as environmental conditions and weather forecast, people behavior and comfort demands are all taken into account for deriving price-optimal climate control. Finally, a DC microgrid with photovoltaics, wind turbine, supercapacitor, batteries and fuel cell stacks is added to make the building a unit capable of active participation in a price-varying energy market. Computational burden of applying model predictive control on such a complex system is relaxed through a hierarchical decomposition of the microgrid and climate control, where the former is designed as higher hierarchical level with pre-calculated price-optimal power flows control, and latter is designed as lower level control responsible to ensure thermal comfort and exploit the optimal supply conditions enabled by microgrid energy flows management. Such an approach is expected to enable the inclusion of more complex building subsystems into consideration in order to further increase the energy efficiency.

Keywords: price-optimal building climate control, Microgrid power flow optimisation, hierarchical model predictive control, energy efficient buildings, energy market participation

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Zahra Asgar Pour

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Two types of Ti-Al-containing zeolitic beads with an average diameter of 450 to 750 µm and hierarchical porosity were synthesized using a hard template method and tested as heterogeneous catalysts in the epoxidation of cycloalkenes (i.e. cyclohexene and cis-cyclooctene) with aqueous hydrogen peroxide (H₂O₂) or tert-butyl hydroperoxide(TBHP) as the oxidant agent. The first type of zeolitic beads was prepared by hydrothermal treatment of a primarygel (containing the Si, Ti, and Al precursors) in the presence of porous anion-exchange resin beads as the hard shaping template. After calcination, these beads (Ti-Al-Beta-HDT-B) consisted of both crystalline zeolite Beta and an amorphous silicate phase. The second type of zeolitic beads (Ti-Beta-PS-deAl-14.4-B) was obtained by post-synthesis dealumination of Al-containing zeolite Beta beads using 14.4 M HNO₃, followed by Ti grafting (3 wt% per gram of zeolite). The prepared materials were characterised by means of XRD, N2-physisorption, UV-vis, XRF, SEM, and TEM and tested as heterogeneous epoxidation catalysts. This post-synthetically prepared catalyst demonstrated higher activity (cyclohexene conversion of 22.7 % and epoxide selectivity of 33.5 %) after 5 h at60 °C, which emanates from the crystalline structure and higher degrees of hydrophobicity. In addition, the post-synthetically prepared beads were prone to partial Ti leaching in the presence of H₂O₂, whereas they showed to be resistant against Ti leaching using tert-butyl hydroperoxide as the oxidant agent.

Keywords: epoxidation, structured catalysts, hierarchical porosity, bead-shape catalysts

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Authors: Malini Nandi Majumdar, Avijan Dutta

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The West Police (WBP) is restructured under provisions of the Police Act 1861 during the period of British domination. It is one of the two police forces of the Indian state of west Bengal and is headed by an officer designated as Director General of Police (DG) who directly reports to the State Government. It covers a jurisdiction with eighteen revenue districts of the state and a District Superintendent of Police (SP) controls each district. The purpose of this empirical study is to explore the causes and factors of occupational stress in West Bengal Police officers so that the incumbents can perform their assigned tasks more diligently and the society could be free from evils and devils at a large. Using a self-developed close ended questionnaire that covers 20 critical job-related stressors, the study captures 310 respondents across the organizational hierarchy ranging from Sub Inspectors to the Superintendant of police and covers 5 districts and one commision rate under the jurisdiction of West Bengal Police. The present research has successfully indicated four major occupational stressors such as Organizational Stressors, Hierarchical Stressors, Situational Stressors and Environmental Stressors with 64% of the variance. Further we have employed CFA to determine the goodness of fit indices in terms of i) Absolute Fit Measures like CMIN, FMIN, RMSEA, ECVI ii) Incremental Fit Measures like TLI, NFI, AGFI, CFI(Byne, 2010) demonstrate that value of the measure has passed the requirement criteria and thus fit the model. The major stressors of West Bengal Police have been explored and the ways to deal with these inevitable stressors have been suggested.

Keywords: organizational stressors, hierarchical stressors, situational stressors, environmental stressors

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Authors: Ashraf Osman

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Effective deployable bridges that are characterized by an increased capacity to weight ratio are recently needed for post-disaster rapid mobility and military operations. In deployable bridging, replacing metals as the fabricating material with advanced composite laminates as lighter alternatives with higher strength is highly advantageous. This article presents a hierarchical optimization strategy of a composite bridge treadway considering maximum strength design and bridge weight minimization. Shape optimization of a generic deployable bridge beam cross-section is performed to achieve better stress distribution over the bridge treadway hull. The developed cross-section weight is minimized up to reserving the margins of safety of the deployable bridging code provisions. Hence, the strength of composite bridge plates is maximized through varying the plies orientation. Different loading cases are considered of a tracked vehicle patch load. The orthotropic plate properties of a composite sandwich core are used to simulate the bridge deck structural behavior. Whereas, the failure analysis is conducted using Tsai-Wu failure criterion. The naturally inspired particle swarm optimization technique is used in this study. The proposed technique efficiently reduced the weight to capacity ratio of the developed bridge beam.

Keywords: CFRP deployable bridges, disaster relief, military bridging, optimization of composites, particle swarm optimization

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Wenwen Lei, David R. Mckenzie

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Recent findings of anomalously high gas flow rates in carbon nanotubes show smooth hydrophobic walls can increase specular reflection of molecules and reduce the tangential momentum accommodation coefficient (TMAC). Here we report the first measurements of water vapor flows in microtubes over a wide humidity range and show that for hydrophobic silica there is a range of humidity over which an adsorbed water layer reduces TMAC and accelerates flow. Our results show that this association between hydrophobicity and accelerated moisture flow occurs in readily available materials. We develop a hierarchical theory that unifies Maxwell’s ideas on TMAC with Langmuir’s ideas on adsorption. We fit the TMAC data as a function of humidity with the hierarchical theory based on two stages of Langmuir adsorption and derive total adsorption isotherms for water on hydrophobic silica that agree with direct observations. We propose structures for each stage of the water adsorption, the first reducing TMAC by a passivation of adsorptive patches and a smoothing of the surface, the second resembling bulk water with large TMAC. We find that leak testing of moisture barriers with an ideal gas such as helium may not be accurate enough for critical applications and that direct measurements of the water leak rate should be made.

Keywords: water vapor flows, silica microtubes, TMAC, enhanced flow rates

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Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

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Authors: Beatrice Eyram Afi Ziorklui, Norihisa Shima

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Community Driven Development (CDD) is now a widely recommended and accepted development strategy for informal communities across the continent. Centered on the utilization of social capital through community structures, different informal settlements have different structures and different levels of social capital, which affect the implementation and ability to overcome CDD challenges. Although known to be very successful, there are few perspectives on the implementation of CDD initiatives in different informal settlements. This study assesses the implementation of CDD initiatives in migrant and indigenous informal settlements and their ability to navigate challenges. The case study research design was adopted in this research, and respondents were chosen through simple random sampling. Using the Statistical Package for social scientists (SPSS) for data analysis, the study found that migrant informal settlements implement CDD projects through the network of hierarchical structures based on government systems, whereas indigenous informal settlements implement through the hierarchical social structure based on traditions and culture. The study also found that, with the exception of the challenge of land accessibility in migrant informal settlements, all other challenges, such as participation, resource mobilization, and maintenance, have a significant relationship with social capital, although indigenous informal settlements have higher levels of social capital than migrant informal settlements. The study recommends a framework that incorporates community characteristics and the underlying social capital to facilitate upgrading strategies in informal in Ghana.

Keywords: community driven development, informal settlements, social capital, upgrading

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Authors: F. Sangiorgio, J. Silfwerbrand, G. Mancini

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Geometric and mechanical properties all influence the resistance of RC structures and may, in certain combination of property values, increase the risk of a brittle failure of the whole system. This paper presents a statistical and probabilistic investigation on the resistance of RC beams designed according to Eurocodes 2 and 8, and subjected to multiple failure modes, under both the natural variation of material properties and the uncertainty associated with cross-section and transverse reinforcement geometry. A full probabilistic model based on JCSS Probabilistic Model Code is derived. Different beams are studied through material nonlinear analysis via Monte Carlo simulations. The resistance model is consistent with Eurocode 2. Both a multivariate statistical evaluation and the data clustering analysis of outcomes are then performed. Results show that the ultimate load behaviour of RC beams subjected to flexural and shear failure modes seems to be mainly influenced by the combination of the mechanical properties of both longitudinal reinforcement and stirrups, and the tensile strength of concrete, of which the latter appears to affect the overall response of the system in a nonlinear way. The model uncertainty of the resistance model used in the analysis plays undoubtedly an important role in interpreting results.

Keywords: modelling, Monte Carlo simulations, probabilistic models, data clustering, reinforced concrete members, structural design

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Shima Behkami, Nor Shahirul Umirah Idris, Sharifuddin Md. Zain, Kah Hin Low, Mehrdad Gholami, Nima A. Behkami, Ahmad Firdaus Kamaruddin

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In this work, Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Isotopic Ratio Mass Spectrometry (IRMS) and Ultrasound Milko Tester were used to study milk samples obtained from various geographical locations in Malaysia. ICP-MS was used to determine the concentration of trace elements in milk, water and soil samples obtained from seven dairy farms at different geographical locations in peninsular Malaysia. IRMS was used to analyze the milk samples for isotopic ratios of δ13C, 15N and 18O. Nutritional parameters in the milk samples were determined using an ultrasound milko tester. Data obtained from these measurements were evaluated by Principal Component Analysis (PCA) and Hierarchical Analysis (HA) as a preliminary step in determining geographical origin of these milk samples. It is observed that the isotopic ratios and a number of the nutritional parameters are responsible for the discrimination of the samples. It was also observed that it is possible to determine the geographical origin of these milk samples solely by the isotopic ratios of δ13C, 15N and 18O. The accuracy of the geographical discrimination is demonstrated when several milk samples from a milk factory taken from one of the regions under study were appropriately assigned to the correct PCA cluster.

Keywords: inductively coupled plasma mass spectroscopy ICP-MS, isotope ratio mass spectroscopy IRMS, ultrasound, principal component analysis, hierarchical analysis, geographical origin, milk

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Authors: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: C. Akhras

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Change is the life blood of business. Dynamic factors inspire change yet may act as barriers, influencing the company’s position in the market and challenging its organizational mission and culture. Today, the business context has globalized with business enterprises in the North and South joint in mergers and the East forges a strategic alliance with the West. Moreover, given that very little remains stable in certain industries, national business goals in the millennial marketplaces might be rapid, accelerated, and differentiated growth while distinctive competitive advantage might mark new qualitative excellence in others. In a new age culture marked by change, organizations, leaders, and followers are impacted; indigenous business leaders seem to have a very important role to play in change management. This case study was carried out on 178 business employees employed in local industry to evaluate perceptions of indigenous business leadership, workplace assessment, and organizational change management in the Middle East and North Africa. Three research questions were posed: (1) In your work context, do you think business leaders are essentially changing agents? (2) In your work context, is workplace change more effective in business leaders perceived as a hierarchical change agent rather than those perceived as an empowering change agent? (3) In your work context, is workplace change more efficient in business leaders perceived as a hierarchical change agent rather than those perceived as an empowering change agent? The results of the study and its limitations imposed by time and space indicate that more comprehensive research is required in this area.

Keywords: catalyst, change management, business enterprise, workplace assessment

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Authors: Sresha Yadav, Ishwar Kumar

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Second language learning is an important area of research in the language and linguistic domains. Literature suggests that several factors impact second language learning, including age, motivation, objectives, teacher, instructional material, classroom interaction, intelligence and previous background, previous linguistic experience, other student characteristics. Previous researchers have also highlighted that classroom atmospherics has a significant impact on learning as well as on the performance of students. However, the impact of classroom atmospherics on second language learning is still not known in the existing literature. Therefore, the purpose of the present study is to explore whether classroom atmospherics has an impact on second language learning or not? And if it does, it would be worthwhile to explore the nature of such relationship. The present study aims to explore the impact of classroom atmospherics on second language learning by dwelling into the existing literature to explore factors which impact second language learning, classroom atmospherics which impact language learning and the metrics through which such learning impacts could be measured. Based on the findings of literature review, the researchers have adopted a clustering approach for categorization and positioning of various measures of second language learning. Based on the clustering approach, the researchers have approach for measuring the impact of classroom atmospherics on second language learning by drawing a student sample consisting of 80 respondents. The results of the study uncover various basic premises of second language learning, especially with regard to classroom atmospherics. The present study is important not only from the point of view of language learning but implications could be drawn with regard to the design of classroom atmospherics, environmental psychology, anthropometrics, etc as well.

Keywords: classroom atmospherics, cluster analysis, linguistics, second language learning

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Authors: Riyadh Jamegh, AllaEldin Kassam, Sawsan Sabih

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In modern trends of market, organizations face high-pressure environment which is characterized by globalization, high competition, and customer orientation, so it is very crucial to control and know the weak and strong points of the supply chain in order to improve their performance. So the performance measurements presented as an important tool of supply chain management because it's enabled the organizations to control, understand, and improve their efficiency. This paper aims to identify supply chain performance measurement (SCPM) by using Fuzzy Analytical Hierarchical Process (FAHP). In our real application, the performance of organizations estimated based on four parameters these are cost parameter indicator of cost (CPI), inventory turnover parameter indicator of (INPI), raw material parameter (RMPI), and safety stock level parameter indicator (SSPI), these indicators vary in impact on performance depending upon policies and strategies of organization. In this research (FAHP) technique has been used to identify the importance of such parameters, and then first fuzzy inference (FIR1) is applied to identify performance indicator of each factor depending on the importance of the factor and its value. Then, the second fuzzy inference (FIR2) also applied to integrate the effect of these indicators and identify (SCPM) which represent the required output. The developed approach provides an effective tool for evaluation of supply chain performance measurement.

Keywords: fuzzy performance measurements, supply chain, fuzzy logic, key performance indicator

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Authors: Alba Barbara-i-Molinero, Rosalia Cascon Pereira, Ana Beatriz Hernandez Lara

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Our general motivation in undertaking this study is to propose alternative measures to lighten students experienced tensions during the transitions from high school to higher education based on the concept of professional identity strength. In order to do so, we measured the influence that three different factors external motivational conditionals, educational experience conditionals and personal motivation conditionals exerted over students’ professional identity strength and proposed the measures considering the obtained results. By using hierarchical regression analysis we addressed this issue, across disciplines and bachelor degrees, allowing us to gain also deeper insight into first-year university students PID. Our findings suggest that students’ from the different disciplines are influenced by personal motivational conditionals; while students from sciences are also influenced by external motivational conditionals. Based on the obtained results we propose three different alternative educational and recruitment strategies which aim to increase students’ professional identity strength and reduce the tensions generated during high school-university transitions. From this study theoretical contributions regarding the differences in the influence of these factors on students from different bachelor degrees arise; and practical implications for universities, derived from the proposed strategies.

Keywords: professional identity, transitions, higher education, strategies

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: Shreya Banerjee, Roshmi Sen, Subrata Chattopadhyay

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Urbanization during the second half of the last century has created many serious environment related issues leading to global warming and climate change. India is not an exception as the country is also facing the problems of global warming and urban heat islands (UHI) in all the major metropolises. This paper discusses the effect of different housing cluster layouts, site geometry, and geometry of urban street canyons on the urban heat island profile. The study is carried out using the three dimensional microclimatic computational fluid dynamics model ENVI-met version 3.1. Simulation models are done for a typical summer day of 21st June, 2015 in four different residential neighborhoods in the city of Kolkata which predominantly belongs to Warm-Humid Monsoon Climate. The results show the changing pattern of urban heat island profile with respect to different clustering layouts, geometry, and morphology of urban street canyons. The comparison between the four neighborhoods shows that different microclimatic variables are strongly dependant on the neighborhood layout pattern and geometry. The inferences obtained from this study can be indicative towards the formulation of neighborhood design by-laws that will attenuate the urban heat island effect.

Keywords: urban heat island, neighborhood morphology, site microclimate, ENVI-met, numerical analysis

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Authors: Siwapech Silapaprayoon, Januluk Khanobdee, Sompid Samipak

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Chili peppers are the fruits of Capsicum pepper plants well known for their fiery burning sensation on the tongue after consumption. They are members of the Solanaceae or common nightshade family along with potato, tomato and eggplant. Thai cuisine has gained popularity for its distinct flavors due to usages of various spices and its heat from the addition of chili pepper. Though being used in little quantity for each dish, chili pepper holds a special place in Thai cuisine. There are many varieties of chili peppers in Thailand, and thirty accessions were collected at Rajamangala University of Technology Lanna, Lampang, Thailand. To effectively manage any germplasm it is essential to know the diversity and relationships among members. Thirty-six Inter Simple Sequence Repeat (ISSRs) DNA markers were used to analyze the germplasm. Total of 335 polymorphic bands was obtained giving the average of 9.3 alleles per marker. Unweighted pair-group mean arithmetic method (UPGMA) clustering of data using NTSYS-pc software indicated that the accessions showed varied levels of genetic similarity ranging from 0.57-1.00 similarity coefficient index indicating significant levels of variation. At SM coefficient of 0.81, the germplasm was separated into four groups. Phenotypic variation was discussed in context of phylogenetic tree clustering.

Keywords: diversity, germplasm, Chili pepper, ISSR

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Hisham Motkal Abu-Rayya, Maram Hussein Abu-Rayya

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Education in religious private schools raises questions regarding identity, belonging and adaptation in multicultural Australia. This research project aimed at examined cultural identification styles among Australian adolescent Muslims studying in Muslim schools, adolescents’ religiosity and the interconnections between cultural identification styles, religiosity, and adaptation. Two Muslim high school samples were recruited for the purposes of this study, one from Muslim schools in metropolitan Sydney and one from Muslim schools in metropolitan Melbourne. Participants filled in a survey measuring themes of the current study. Findings revealed that the majority of Australian adolescent Muslims showed a preference for the integration identification style (55.2%); separation was less prevailing (26.9%), followed by assimilation (9.7%) and marginalisation (8.3%). Supporting evidence suggests that the styles of identification were valid representation of the participants’ identification. A series of hierarchical regression analyses revealed that while adolescents’ preference for integration of their cultural and Australian identities was advantageous for a range of their psychological and socio-cultural adaptation measures, marginalisation was consistently the worst. Further hierarchical regression analyses showed that adolescent Muslims’ religiosity was better for a range of their adaptation measures compared to their preference for an integration acculturation style. Theoretical and practical implications of these findings are discussed.

Keywords: adaptation, identity, multiculturalism, religious school education

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Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

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The portfolio selection problem includes the evaluation of many criteria that are difficult to compare directly and is characterized by uncertain elements. The portfolio selection problem can be modeled as a group decision problem in which several experts are invited to present their assessment. In this context, it is important to study and analyze the process of reaching a consensus among group members. Indeed, due to the various diversities among experts, reaching consensus is not necessarily always simple and easily achievable. Moreover, the concept of consensus is accompanied by the concept of false consensus, which is particularly interesting in the dynamics of group decision-making processes. False consensus can alter the evaluation and selection phase of the alternative and is the consequence of the decision maker's inability to recognize that his preferences are conditioned by subjective structures. The present work aims to investigate the dynamics of consensus attainment in a group decision problem in which equivalent portfolios are proposed. In particular, the study aims to analyze the impact of the subjective structure of the decision-maker during the evaluation and selection phase of the alternatives. Therefore, the experimental framework is divided into three phases. In the first phase, experts are sent to evaluate the characteristics of all portfolios individually, without peer comparison, arriving independently at the selection of the preferred portfolio. The experts' evaluations are used to obtain individual Analytical Hierarchical Processes that define the weight that each expert gives to all criteria with respect to the proposed alternatives. This step provides insight into how the decision maker's decision process develops, step by step, from goal analysis to alternative selection. The second phase includes the description of the decision maker's state through Markov chains. In fact, the individual weights obtained in the first phase can be reviewed and described as transition weights from one state to another. Thus, with the construction of the individual transition matrices, the possible next state of the expert is determined from the individual weights at the end of the first phase. Finally, the experts meet, and the process of reaching consensus is analyzed by considering the single individual state obtained at the previous stage and the false consensus bias. The work contributes to the study of the impact of subjective structures, quantified through the Analytical Hierarchical Process, and how they combine with the false consensus bias in group decision-making dynamics and the consensus reaching process in problems involving the selection of equivalent portfolios.

Keywords: analytical hierarchical process, consensus building, false consensus effect, markov chains, portfolio selection problem

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Authors: Dona Shaini Abhilasha Nanayakkara, Kurugamage Jude Pravinda Gregory Perera

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E-commerce platforms have revolutionized the shopping experience, offering convenient ways for consumers to make purchases. To improve interactions with customers and optimize marketing strategies, it is essential for businesses to understand user behavior, preferences, and needs on these platforms. This paper focuses on recommending businesses to customize interactions with users based on their behavioral patterns, leveraging data-driven analysis and machine learning techniques. Businesses can improve engagement and boost the adoption of e-commerce platforms by aligning behavioral patterns with user goals of usability and satisfaction. We propose TheAnalyzer, a clustering-based system designed to enhance business productivity by analyzing user-profiles and improving human-computer interaction. The Analyzer seamlessly integrates with business applications, collecting relevant data points based on users' natural interactions without additional burdens such as questionnaires or surveys. It defines five key user analytics as features for its dataset, which are easily captured through users' interactions with e-commerce platforms. This research presents a study demonstrating the successful distinction of users into specific groups based on the five key analytics considered by TheAnalyzer. With the assistance of domain experts, customized business rules can be attached to each group, enabling The Analyzer to influence business applications and provide an enhanced personalized user experience. The outcomes are evaluated quantitatively and qualitatively, demonstrating that utilizing TheAnalyzer’s capabilities can optimize business outcomes, enhance customer satisfaction, and drive sustainable growth. The findings of this research contribute to the advancement of personalized interactions in e-commerce platforms. By leveraging user behavioral patterns and analyzing both new and existing users, businesses can effectively tailor their interactions to improve customer satisfaction, loyalty and ultimately drive sales.

Keywords: data clustering, data standardization, dimensionality reduction, human computer interaction, user profiling

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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