Search results for: cluster analysis

Authors: Abiodun Ezekiel Adesina, Ijeoma Ginikanwa Akubugwo

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Information and Communication Technology, ICT can heighten pre-service teachers’ attitudes toward learning and metacognitive learning; however, there is a dearth of literature on the perception of the pre-service teachers on heightening their attitude toward learning and metacognitive learning. Thus, this study investigates the perception of pre-service science teachers on heightening their attitude towards learning and metacognitive learning through ICT. Two research questions and four hypotheses guided the research. A mixed methods research was adopted for the study in concurrent triangulation type of integrating qualitative and quantitative approaches to the study. The cluster random sampling technique was adopted to select 250 pre-service science teachers in Oyo township. Two self-constructed instruments: Heightening Pre-service Science Teachers’ Attitude towards Learning and Metacognitive Learning through Information and Communication Technology Scale (HPALMIS, r=.73), and an unstructured interview were used for data collection. Thematic analysis, frequency counts and percentages, t-tests, and analysis of variance were used for data analysis. The perception level of the pre-service science teachers on heightening their attitude towards learning and metacognitive learning through ICT is above average, with the majority perceiving that ICT can enhance their thinking about their learning. The perception was significant (mean=92.68, SD=10.86, df=249, t=134.91, p<.05). The perception was significantly differentiated by gender (t=2.10, df= 248, p<.05) in favour of the female pre-service teachers and based on the first time of ICTs use (F(5,244)= 9.586, p<.05). Lecturers of science and science related courses should therefore imbibe the use of ICTs in heightening pre-service teachers’ attitude towards learning and metacognitive learning. Government should organize workshops, seminars, lectures, and symposia along with professional bodies for the science education lecturers to keep abreast of the trending ICT.

Keywords: pre-service teachers’ attitude towards learning, metacognitive learning, ICT, pre-service teachers’ perspectives

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Authors: M. Novak, E. Munoz, A. Jana, M. Nelemans

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Cities and local governments the world over are interested to employ circular strategies as a means to bring about food security, create employment and increase resilience. The selection and implementation of circular strategies is facilitated by modeling the effects of strategies locally and understanding the impacts such strategies have had in other (comparable) cities and how that would translate locally. Urban areas are heterogeneous because of their geographic, economic, social characteristics, governance, and culture. In order to better understand the effect of circular strategies on urban systems, we create a dataset for over 300 cities around the world designed to facilitate circular strategy scenario modeling. This new dataset integrates data from over 20 prominent global national and urban data sources, such as the Global Human Settlements layer and International Labour Organisation, as well as incorporating employment data from over 150 cities collected bottom up from local departments and data providers. The dataset is made to be reproducible. Various clustering techniques are explored in the paper. The result is sets of clusters of cities, which can be used for further research, analysis, and support comparative, regional, and national policy making on circular cities.

Keywords: data integration, urban innovation, cluster analysis, circular economy, city profiles, scenario modelling

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Authors: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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Authors: Gurkirat Kaur, Manoj Kumar, Manju Bala

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Ethernet is the most influential and widely used technology in the world. With the growing demand of low latency and high throughput technologies like InfiniBand and RoCE, unique features viz. RDMA (Remote Direct Memory Access) have evolved. RDMA is an effective technology which is used for reducing system load and improving performance. InfiniBand is a well known technology which provides high-bandwidth and low-latency and makes optimal use of in-built features like RDMA. With the rapid evolution of InfiniBand technology and Ethernet lacking the RDMA and zero copy protocol, the Ethernet community has came out with a new enhancements that bridges the gap between InfiniBand and Ethernet. By adding the RDMA and zero copy protocol to the Ethernet a new networking technology is evolved, called RDMA over Converged Ethernet (RoCE). RoCE is a standard released by the IBTA standardization body to define RDMA protocol over Ethernet. With the emergence of lossless Ethernet, RoCE uses InfiniBand’s efficient transport to provide the platform for deploying RDMA technology in mainstream data centres over 10GigE, 40GigE and beyond. RoCE provide all of the InfiniBand benefits transport benefits and well established RDMA ecosystem combined with converged Ethernet. In this paper, we evaluate the heterogeneous Linux cluster, having multi nodes with fast interconnects i.e. gigabit Ethernet and Soft RoCE. This paper presents the heterogeneous Linux cluster configuration and evaluates its performance using Intel’s MPI Benchmarks. Our result shows that Soft RoCE is performing better than Ethernet in various performance metrics like bandwidth, latency and throughput.

Keywords: ethernet, InfiniBand, RoCE, RDMA, MPI, Soft RoCE

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Forrest Kaatz, Adhemar Bultheel

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We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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Authors: Pei-Syuan Ruan, Ya-Chi Yu, Shao-Wen Chen, Jin-Der Lee, Jong-Rong Wang, Chunkuan Shih

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This study investigated the void fraction characteristics under low superficial gas velocity (J_g) and low superficial fluid velocity (J_f) conditions in a 3x3 rod bundle geometry. Three arrangements of conductivity probes were set to measure the void fraction at various cross-sectional regions, including rod-gap, sub-channel and rod-wall regions. The experimental tests were performed under the flow conditions of J_g = 0-0.236 m/s and J_f = 0-0.142 m/s, and the time-averaged void fractions were recorded at each flow condition. It was observed that while the superficial gas velocity increases, the small bubbles started to cluster together and become big bubbles. As the superficial fluid velocity increases, the local void fractions of the three test regions will get closer and the bubble distribution will be more uniform across the cross section.

Keywords: conductivity probes, rod bundles, two-phase flow, void fraction

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Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Gachara Grace, Kebira Anthony, Harvey Jagger, Wainaina James

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Aflatoxin contamination of maize in Africa poses a major threat to food security and the health of many African people. In Kenya, aflatoxin contamination of maize is high due to the environmental, agricultural and socio-economic factors. Many studies have been conducted to understand the scope of the problem, especially at pre-harvest level. This research was carried out to gather scientific information on the fungi population, diversity and aflatoxin level during the post-harvest period. The study was conducted in three geographical locations of; Kitui, Kitale and Nakuru. Samples were collected from storage structures of farmers and transported to the Biosciences eastern and central Africa (BecA), International Livestock and Research Institute (ILRI) hub laboratories. Mycoflora was recovered using the direct plating method. A total of five fungal genera (Aspergillus, Penicillium, Fusarium, Rhizopus and Bssyochlamys spp.) were isolated from the stored maize samples. The most common fungal species that were isolated from the three study sites included A. flavus at 82.03% followed by A.niger and F.solani at 49% and 26% respectively. The aflatoxin producing fungi A. flavus was recovered in 82.03% of the samples. Aflatoxin levels were analysed on both the maize samples and in vitro. Most of the A. flavus isolates recorded a high level of aflatoxin when they were analysed for presence of aflatoxin B1 using ELISA. In Kitui, all the samples (100%) had aflatoxin levels above 10ppb with a total aflatoxin mean of 219.2ppb. In Kitale, only 3 samples (n=39) had their aflatoxin levels less than 10ppb while in Nakuru, the total aflatoxin mean level of this region was 239.7ppb. When individual samples were analysed using Vicam fluorometer method, aflatoxin analysis revealed that most of the samples (58.4%) had been contaminated. The means were significantly different (p=0.00<0.05) in all the three locations. Genetic relationships of A. flavus isolates were determined using 13 Simple Sequence Repeats (SSRs) markers. The results were used to generate a phylogenetic tree using DARwin5 software program. A total of 5 distinct clusters were revealed among the genotypes. The isolates appeared to cluster separately according to the geographical locations. Principal Coordinates Analysis (PCoA) of the genetic distances among the 91 A. flavus isolates explained over 50.3% of the total variation when two coordinates were used to cluster the isolates. Analysis of Molecular Variance (AMOVA) showed a high variation of 87% within populations and 13% among populations. This research has shown that A. flavus is the main fungal species infecting maize grains in Kenya. The influence of aflatoxins on human populations in Kenya demonstrates a clear need for tools to manage contamination of locally produced maize. Food basket surveys for aflatoxin contamination should be conducted on a regular basis. This would assist in obtaining reliable data on aflatoxin incidence in different food crops. This would go a long way in defining control strategies for this menace.

Keywords: aflatoxin, Aspergillus flavus, genotyping, Kenya

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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: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Jessica Carolina Hernandez Galeano, Juan Carlos Moreno Pirajan, Liliana Giraldo

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Carbon gels are materials whose structure and porous texture can be designed and controlled on a nanoscale. Among their characteristics it is found their low density, large surface area and high degree of porosity. These materials are produced by a sol-gel polymerization of organic monomers using basic or acid catalysts, followed by drying and controlled carbonization. In this work, the synthesis and characterization of carbon aerogels from resorcinol, phenol and formaldehyde in ethanol is described. The aim of this study is obtaining different carbonaceous materials in the form of spheres using the Stöber method to perform a further evaluation of CO₂ adsorption of each material. In general, the synthesis consisted of a sol-gel polymerization process that generates a cluster (cross-linked organic monomers) from the precursors in the presence of NH₃ as a catalyst. This cluster was subjected to specific conditions of gelling and curing (30°C for 24 hours and 100°C for 24 hours, respectively) and CO₂ supercritical drying. Finally, the dry material was subjected to a process of carbonization or pyrolysis, in N₂ atmosphere at 350°C (1° C / min) for 2 h and 600°C (1°C / min) for 4 hours, to obtain porous solids that retain the structure initially desired. For this work, both the concentrations of the precursors and the proportion of ammonia in the medium where modify to describe the effect of the use of phenol and the amount of catalyst in the resulting material. Carbon aerogels were characterized by Scanning Electron Microscope (SEM), N₂ isotherms, infrared spectroscopy (IR) and X-ray Powder Diffraction (XRD) showing the obtention of carbon spheres in the nanometric scale with BET areas around 500 m2g-1.

Keywords: carbon aerogels, carbon spheres, CO₂ adsorption, Stöber method

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Authors: Marco F. Benaglia, Mei H. Chen, Kune M. Tsai, Chia H. Hung

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As societal and family structures, consumer dietary habits, and awareness about food safety and quality continue to evolve in most developed countries, the demand for refrigerated and frozen foods has been growing, and the issues related to their preservation have gained increasing attention. A well-established cold chain logistics system is essential to avoid any temperature abuse; therefore, assessing potential disruptions in the operational processes of cold chain logistics centers becomes pivotal. This study preliminarily employs HACCP to find disruption factors in cold chain logistics centers that may cause temperature abuse. Then, concept mapping is applied: selected experts engage in brainstorming sessions to identify any further factors. The panel consists of ten experts, including four from logistics and home delivery, two from retail distribution, one from the food industry, two from low-temperature logistics centers, and one from the freight industry. Disruptions include equipment-related aspects, human factors, management aspects, and process-related considerations. The areas of observation encompass freezer rooms, refrigerated storage areas, loading docks, sorting areas, and vehicle parking zones. The experts also categorize the disruption factors based on perceived similarities and build a similarity matrix. Each factor is evaluated for its impact, frequency, and investment importance. Next, multiple scale analysis, cluster analysis, and other methods are used to analyze these factors. Simultaneously, key disruption factors are identified based on their impact and frequency, and, subsequently, the factors that companies prioritize and are willing to invest in are determined by assessing investors’ risk aversion behavior. Finally, Cumulative Prospect Theory (CPT) is applied to verify the risk patterns. 66 disruption factors are found and categorized into six clusters: (1) "Inappropriate Use and Maintenance of Hardware and Software Facilities", (2) "Inadequate Management and Operational Negligence", (3) "Product Characteristics Affecting Quality and Inappropriate Packaging", (4) "Poor Control of Operation Timing and Missing Distribution Processing", (5) "Inadequate Planning for Peak Periods and Poor Process Planning", and (6) "Insufficient Cold Chain Awareness and Inadequate Training of Personnel". This study also identifies five critical factors in the operational processes of cold chain logistics centers: "Lack of Personnel’s Awareness Regarding Cold Chain Quality", "Personnel Not Following Standard Operating Procedures", "Personnel’s Operational Negligence", "Management’s Inadequacy", and "Lack of Personnel’s Knowledge About Cold Chain". The findings show that cold chain operators prioritize prevention and improvement efforts in the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster, particularly focusing on the factors of "Temperature Setting Errors" and "Management’s Inadequacy". However, through the application of CPT theory, this study reveals that companies are not usually willing to invest in the improvement of factors related to the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster due to its low occurrence likelihood, but they acknowledge the severity of the consequences if it does occur. Hence, the main implication is that the key disruption factors in cold chain logistics centers’ processes are associated with personnel issues; therefore, comprehensive training, periodic audits, and the establishment of reasonable incentives and penalties for both new employees and managers may significantly reduce disruption issues.

Keywords: concept mapping, cold chain, HACCP, cumulative prospect theory

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

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Authors: Laura N. Bolivar T.

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The tourism importance for regional development is mainly highlighted by the collaborative, cooperating and competitive relationships of the involved agents. The fostering of associativity processes, in particular, the cluster approach emphasizes the beneficial outcomes from the concentration of enterprises, where innovation and entrepreneurship flourish and shape the dynamics for tourism empowerment. Considering the department of Huila, it is located in the south-west of Colombia and holds the biggest coffee production in the country, although it barely contributes to the national GDP. Hence, its economic development strategy is looking for more dynamism and Huila could be consolidated as a leading destination for cultural, ecological and heritage tourism, if at least the public policy making processes for the tourism management of La Tatacoa Desert, San Agustin Park and Bambuco’s National Festival, were implemented in a more efficient manner. In this order of ideas, this study attempts to address the potential restrictions and beneficial factors for the consolidation of the tourism sector of Huila-Colombia as a cluster and how could it impact its regional development. Therefore, a set of theoretical frameworks such as the Tourism Routes Approach, the Tourism Breeding Environment, the Community-based Tourism Method, among others, but also a collection of international experiences describing tourism clustering processes and most outstanding problematics, is analyzed to draw up learning points, structure of proceedings and success-driven factors to be contrasted with the local characteristics in Huila, as the region under study. This characterization involves primary and secondary information collection methods and comprises the South American and Colombian context together with the identification of involved actors and their roles, main interactions among them, major tourism products and their infrastructure, the visitors’ perspective on the situation and a recap of the related needs and benefits regarding the host community. Considering the umbrella concepts, the theoretical and the empirical approaches, and their comparison with the local specificities of the tourism sector in Huila, an array of shortcomings is analytically constructed and a series of guidelines are proposed as a way to overcome them and simultaneously, raise economic development and positively impact Huila’s well-being. This non-exhaustive bundle of guidelines is focused on fostering cooperating linkages in the actors’ network, dealing with Information and Communication Technologies’ innovations, reinforcing the supporting infrastructure, promoting the destinations considering the less known places as well, designing an information system enabling the tourism network to assess the situation based on reliable data, increasing competitiveness, developing participative public policy-making processes and empowering the host community about the touristic richness. According to this, cluster dynamics would drive the tourism sector to meet articulation and joint effort, then involved agents and local particularities would be adequately assisted to cope with the current changing environment of globalization and competition.

Keywords: innovative strategy, local development, network of tourism actors, tourism cluster

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Authors: Wei-Ting Hung

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Inbound tourist expenditure might not only have influenced by individual tourist characteristics but may also be affected by nationality characteristics. The cross national distance effects on tourist consumption behavior should be incorporated in the analytical framework. Additionally, the often used factor analysis, cluster analysis and regression analysis overlook the hierarchical tourist consumption data structure and may lead to misleading results. The objectives of the present study were twofold. First, we propose a multilevel model that takes individual and cross-national differences into account under a hierarchical framework. Second, we further sought to determine the types of cross-national differences affecting tourist expenditure. Thus, this study incorporates the individual tourist effects and cross national distance effects simultaneously, uses the data of 2010 Annual Survey Report on Visitors’ Expenditure and Trends in Taiwan to investigate the determinants of inbound tourist expenditure. Multilevel analysis was used to investigate the influence of individual tourist effects and cross national distance effects on inbound tourist expenditure. The empirical results show that cross national distance plays a crucial role in tourist consumption behavior. Our findings also indicate age and income have positive influence on tourism expenditure., whereas education and gender do not have significant impact. Regarding macro-level factors, geographic and cultural differences exhibited significant positive relationships on tourism expenditure, while economic differences did not. Based on the above empirical results, it is suggested that tour operators should take tourists’ individual attributes, particularly their income and age, into consideration when arranging tours. In addition, nationality holds sway over tourists’ consumption behavior, of which geographic and cultural differences are the two major factors at play. The empirical results of this study serve as practical suggestions for tourism marketing strategies and policy implications for government policies.

Keywords: cross national distance, inbound tourist, multilevel analysis, tourist expenditure

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Authors: Radka Sefcu, Vaclava Antuskova, Ivana Turkova

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A growing market with modern artworks of a high price leads to the creation and selling of artwork counterfeits. Material analysis is an important part of the process of assessment of authenticity. Knowledge of materials and techniques used by original authors is also necessary. The contribution presents possibilities of non-invasive methods of structural analysis in research on paintings. It was proved that unambiguous identification of many art materials is feasible without sampling. The combination of Raman spectroscopy with FTIR-external reflection enabled the identification of pigments and binders on selected artworks of prominent Czech painters from the first half of the 20th century – Josef Čapek, Emil Filla, Václav Špála and Jan Zrzavý. Raman spectroscopy confirmed the presence of a wide range of white pigments - lead white, zinc white, titanium white, barium white and also Freeman's white as a special white pigment of painting. Good results were obtained for red, blue and most of the yellow areas. Identification of green pigments was often impossible due to strong fluorescence. Oil was confirmed as a binding medium on most of the analyzed artworks via FTIR - external reflection. Collected data present the valuable background for the determination of art materials characteristic for each painter (his palette) and its development over time. Obtained results will further serve as comparative material for the authentication of artworks. This work has been financially supported by the project of the Ministry of the Interior of the Czech Republic: The Development of a Strategic Cluster for Effective Instrumental Technological Methods of Forensic Authentication of Modern Artworks (VJ01010004).

Keywords: non-invasive analysis, Raman spectroscopy, FTIR-external reflection, forgeries

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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: Laura Piedra-Munoz, Angeles Godoy-Duran, Emilio Galdeano-Gomez, Juan C. Perez-Mesa

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Spain is very sensitive to water management issues due to its climatic characteristics and the deficit of this resource in many areas of its territory. This study examines the characteristics of the family farms that are more efficient in the use of water, focusing on a semi-arid area located in Almeria, southeast of Spain. In the case of irrigated agriculture, water usage efficiency usually indicates water productivity in terms of yield (kg/m³), or in economic terms (euros/m³). These two water usage indicators were considered to analyse water usage efficiency according to other studies on water efficiency in the horticultural area under analysis. This work also takes into account other water usage characteristics such as water supplied, innovative irrigation practices, water-efficient technology, and water-saving practices. The results show that the most water efficient farms have technical advisors and use irrigation on demand, that measures the water needs of the crops and are considered the most technological irrigation system. These farms are more technological and less labor intensive. They are also aware of water scarcity and the need to conserve the environment. This approach allow managers to identify the principal factors and best practices related to water efficiency in order to promote and implement them in inefficient farms and promote sustainability.

Keywords: cluster analysis, family farms, Spain, sustainability, water-use efficiency

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Authors: Songul Cinaroglu

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Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

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Authors: Vahid Rashedi, Banafsheh Ebrahimi, Mahtab Sharif Mohseni, Mohammadali Hosseini

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Introduction: One of the metrics used to evaluate health status is life expectancy. This index alters as people age as a result of several events, illnesses, stress, and anxiety. One of the issues that might develop into a lethal phobia is death anxiety. This study looked at older persons in Tehran, Iran, to see if there was any correlation between life expectancy and fear of dying. Methods: Cluster random sampling was used to select 208 older persons (age 60) who had been sent to adult daycare facilities in Tehran for this correlational descriptive study. A demographic questionnaire, Temper's death anxiety scale, and Snyder's life expectancy scale were used to gather the data. Statistical Package for the Social Sciences softwear version 22 was used to conduct the data analysis. Results: The average age of the senior citizens was 66.60 (6.58) years. With a mean life expectancy of 24.94, it was discovered that the average death anxiety was 12.21. Additionally, Pearson's correlation coefficient demonstrated a bad correlation between fear of dying and life expectancy. Age, residential status, and death fear were the three primary predictors of a decline in life expectancy, according to multiple regression analysis. Conclusion: The findings suggest that there is a link between death fear and a lower life expectancy, which calls for the use of appropriate strategies to increase older individuals' life expectancies as well as the teaching of anxiety coping mechanisms.

Keywords: aged, frailty, death, anxiety, life

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Authors: Elaosi Vhurumuku

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During the past twenty years, there has been an increased interest by science educators in researching and developing teachers’ pedagogical content knowledge for teaching the nature of science (PCKNOS). While there has been this surge in interest in the idea of PCKNOS, there has not been a common understanding among NOS researchers as to how exactly the PCKNOS concept should be construed. In this paper, we analyse and evaluate published accredited journal articles on PCKNOS research. We also draw from our teaching experiences. The major points of foci are the researchers’ presentations of SMKNOS and their centres of attention regarding the elements of PCKNOS. Our content, cluster analysis, and evaluation of the studies on PCKNOS reveal that most researchers have presented SMKNOS in the form of a heuristic or a set of heuristics (targeted NOS ideas) to be mastered by teachers or learners. Furthermore, we found that most of the researchers’ attention has been on developing and recommending teacher pedagogical practices for teaching NOS. From this, we synthesize and propose a subject knowledge content structure and a pedagogical approach that we believe is relevant and appropriate for secondary school and science teacher education if the goal of science education for scientific literacy is to be achieved. The justification of our arguments is rooted in tracing and unpacking the origins and meaning of pedagogical content knowledge (PCK). From our analysis, synthesis, and evaluation, as well as teaching experiences, we distil and construct a meaning for the PCKNOS construct.

Keywords: pedagogical content knowledge, teaching, nature of science, construct, subject matter knowledge

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Authors: Hatem Diaf, Patrice Melinon, Antonio Pereira, Bernard Moine, Nicholas Blanchard, Florent Bourquard, Florence Garrelie, Christophe Donnet

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Glassy carbon exhibits excellent biological compatibility with live tissues meaning it has high potential for applications in life science. Moreover, glassy carbon has interesting properties including 'high temperature resistance', hardness, low density, low electrical resistance, low friction, and low thermal resistance. The structure of glassy carbon has long been a subject of debate. It is now admitted that glassy carbon is 100% sp2. This term is a little bit confusing as long sp2 hybridization defined from quantum chemistry is related to both properties: threefold configuration and pi bonding (parallel pz orbitals). Using plasma laser deposition of carbon clusters combined with pulsed nano/femto laser annealing, we are able to synthesize thin films of glassy carbon of good quality (probed by G band/ D disorder band ratio in Raman spectroscopy) without thermal post annealing. A careful inspecting of Raman signal, plasmon losses and structure performed by HRTEM (High Resolution Transmission Electron Microscopy) reveals that both properties (threefold and pi orbitals) cannot coexist together. The structure of the films is compared to models including schwarzites based from negatively curved surfaces at the opposite of onions or fullerene-like structures with positively curved surfaces. This study shows that a huge collection of porous carbon named vitreous carbon with different structures can coexist.

Keywords: glassy carbon, cluster deposition, coating, electronic structure

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Authors: Sérgio J. Teixeira, João J. Ferreira

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Tourism frequently gets identified as one of the sectors with the greatest potential for expansion on a global scale and hence conveying the importance of attempting to better understand the regional factors of competitiveness prevailing in this sector. This study’s objective essentially strives to provide a mapping of the scientific publications and the intellectual knowledge therein contained while conveying past research trends and identifying potential future lines of research in the fields of regional competitiveness and tourism innovation. This correspondingly deploys a systematic review of the literature in keeping with the bibliometric approach based upon VOSviewer software, with a particular focus on drafting maps for visualising the underlying intellectual structure. This type of analysis encapsulates the number of articles published and their annual number of citations for the period between 1900 and 2016 as registered by the Web of Science database. The results demonstrate how the intellectual structure on regional competitiveness divides essentially into three major categories: regional competitiveness, tourism innovation, and tourism clusters. Thus, the main contribution of this study arises out of identifying the main research trends in this field and the respective shortcomings and specific needs for future scientific research in the field of regional competitiveness and innovation in tourism.

Keywords: regional competitiveness, tourism cluster, bibliometric studies, tourism innovation, systematic review

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Authors: Naveeda Akhtar Qureshi, Wajiha

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Coccidiosis is a protozoan parasitic disease of genus Eimeria. It is an avian infection causing a great economic loss of 3 billion USD per year globally. A number of anticoccidial drugs are in use however many of them have side effects and cost effective. With increase in poultry demand throughout the world there is a need of more drugs and vaccines against coccidiosis. The present study is based upon the use of F. racemosa a medicinal plant to be a potential source of anticoccidial agents. The methanolic leaves extract was fractionated by column and thin layer chromatography and got nineteen fractions. Each fraction different concentrations was evaluated for its anticoccidial properties in an invitro experiment against E. tenella, E. necatrix and E. mitis. The anticoccidial active fractions were further characterized by spectroscopy (UV-Vis, FTIR) and GC-MS analysis. The in silico molecular docking of active fractions identified compounds were carried out. Among all fractions significantly maximum sporulation inhibition efficacy was shown by F-19 (67.11±2.18) followed by F-15 (65.21±1.34) at concentration of 30mg/ml against E. tenella. The significantly highest sporozoites viability inhibition was shown by F-19 (69.23±2.11) followed by F-15 (67.14±1.52) against E. necatrix at concentration 30mg/ml. Anticoccidial active fractions 15 and 19 showed peak spectrum at 207 and 202nm respectively by UV analysis. Their FTIR analysis confirmed the presence of carboxylic acid, amines, phenols, etc. Anticoccidial active compounds like Cyclododecane methanol, oleic acid, Octadecanoic acid, etc were identified by GC-MS analysis. Identified compounds in silico molecular docking study showed that cyclododecane methanol of F-19 and oleic acid of F-15 showed highest binding affinity with target S-Adenosylmethionine synthase. Hence for further authentication in vivo anticoccidial studies are recommended.

Keywords: ficus racemosa, cluster fig, column chromatography, anticoccidial fractions, GC-MS, molecular docking., s-adenosylmethionine synthase

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Authors: Farzaneh Golshekoh

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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

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Authors: Weiyao Zhu, Qian Qi, Ming Yue, Dongxu Ma

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Due to the nano-micro pore structures and the massive multi-stage multi-cluster hydraulic fracturing in shale gas reservoirs, the multi-scale seepage flows are much more complicated than in most other conventional reservoirs, and are crucial for the economic development of shale gas. In this study, a new multi-scale non-linear flow model was established and simplified, based on different diffusion and slip correction coefficients. Due to the fact that different flow laws existed between the fracture network and matrix zone, a three-zone composite model was proposed. Then, according to the conformal transformation combined with the law of equivalent percolation resistance, the productivity equation of a horizontal fractured well, with consideration given to diffusion, slip, desorption, and absorption, was built. Also, an analytic solution was derived, and the interference of the multi-cluster fractures was analyzed. The results indicated that the diffusion of the shale gas was mainly in the transition and Fick diffusion regions. The matrix permeability was found to be influenced by slippage and diffusion, which was determined by the pore pressure and diameter according to the Knudsen number. It was determined that, with the increased half-lengths of the fracture clusters, flow conductivity of the fractures, and permeability of the fracture network, the productivity of the fractured well also increased. Meanwhile, with the increased number of fractures, the distance between the fractures decreased, and the productivity slowly increased due to the mutual interference of the fractures. In regard to the fractured horizontal wells, the free gas was found to majorly contribute to the productivity, while the contribution of the desorption increased with the increased pressure differences.

Keywords: multi-scale, fracture network, composite model, productivity

Procedia PDF Downloads 248