Search results for: panel data modelling

Authors: R. M. Kalayappan, N. Kathiravan

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In automobile industries, Controller Area Network (CAN) is widely used to reduce the system complexity and inter-task communication. Therefore, this paper proposes the hardware implementation of data frame communication between one controller to other. The CAN data frames and protocols will be explained deeply, here. The data frames are transferred without any collision or corruption. The simulation is made in the KEIL vision software to display the data transfer between transmitter and receiver in CAN. ARM7 micro-controller is used to transfer data’s between the controllers in real time. Data transfer is verified using the CRO.

Keywords: control area network (CAN), automotive electronic control unit, CAN 2.0, industry

Procedia PDF Downloads 398

Authors: Feng Ji Mervin Goh, Edmund Chee Jian Pua, Stuart Alexander Cook

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Dilated cardiomyopathy (DCM) is a common cause of heart failure. Genetic mutations account for 50% of DCM cases with TTN mutations being the most common, accounting for up to 25% of DCM cases. However, the genetic architecture underlying Asian DCM patients is unknown. We evaluated 68 patients (female= 17) with DCM who underwent follow-up at the National Heart Centre, Singapore from 2013 through 2014. Clinical data were obtained and analyzed retrospectively. Genomic DNA was subjected to next-generation targeted sequencing. Nextera Rapid Capture Enrichment was used to capture the exons of a panel of 169 cardiac genes. DNA libraries were sequenced as paired-end 150-bp reads on Illumina MiSeq. Raw sequence reads were processed and analysed using standard bioinformatics techniques. The average age of onset of DCM was 46.1±10.21 years old. The average left ventricular ejection fraction (LVEF), left ventricular diastolic internal diameter (LVIDd), left ventricular systolic internal diameter (LVIDs) were 26.1±11.2%, 6.20±0.83cm, and 5.23±0.92cm respectively. The frequencies of mutations in major DCM-associated genes were as follows TTN (5.88% vs published frequency of 20%), LMNA (4.41% vs 6%), MYH7 (5.88% vs 4%), MYH6 (5.88% vs 4%), and SCN5a (4.41% vs 3%). The average callability at 10 times coverage of each major gene were: TTN (99.7%), LMNA (87.1%), MYH7 (94.8%), MYH6 (95.5%), and SCN5a (94.3%). In conclusion, TTN mutations are not common in Singaporean DCM patients. The frequencies of other major DCM-associated genes are comparable to frequencies published in the current literature.

Keywords: heart failure, dilated cardiomyopathy, genetics, next-generation sequencing

Procedia PDF Downloads 243

Authors: Rajbir Cheema

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In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research information.

Keywords: Research information systems (RIS), research information, heterogeneous sources, data quality, data cleansing, science system, standardization

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Authors: Carla Silva

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Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

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Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

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Authors: Mohammad Abdullah Almalki

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Background: Despite the frequent usage of ankle arthroscopy, there is limited medical literature regarding its indications, patterns of admissions, surgical outcomes, and associated complicated at Saudi Arabia. Hence, this study would highlight the surgical outcomes of such surgical approach that will assist orthopedic surgeons to detect which surgical procedure needs to be done as well as to help them regarding their diagnostic workups. Methods: At the Orthopedic Division of King Abdul‑Aziz Medical City in Riyadh and through a cross‑sectional design and convenient sampling techniques, the present study had recruited 20 subjects who fulfill the inclusion and exclusion criteria between 2016 and 2018. Data collection was carried out by a questionnaire designed and revised by an expert panel of health professionals. Results: Twenty patients were reviewed (11M and 9F) with an average age of 40.1 ± 12.2. Only 30% of the patients (5M, 1F) have no comorbidity, but 70% of patients (7M, 8F) were having at least one comorbidity. The most common indications were osteochondritis dissecans (n = 7, 35%), ankle fracture without dislocation (n = 4, 20%), and tibiotalar impingement (n = 3, 15%). Patients recorded pain in all cases (100%). The top four symptoms after pain were instability (30%, n = 6), muscle weakness (15%, n = 3) swelling (15%, n = 3), and stiffness (5%, n = 1). Two‑third of cases reached to their full healthy status and toe‑touch weight‑bearing was seen in two patients (10%). Conclusion: Ankle arthroscopy improved the rehabilitation rates in our tertiary care center. In addition, the surgical outcomes are favorable in our hospital since it has a very short length of stay, unexpended surgery, and fewest physiotherapy sessions.

Keywords: ankle, arthroscopy, indications, patterns

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Authors: Digvijaysingh S. Bana, Kiran R. Trivedi

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This paper introduces various methods for the alpha wave to detect the difference between two states of brain. One healthy subject participated in the experiment. EEG was measured on the forehead above the eye (FP1 Position) with reference and ground electrode are on the ear clip. The data samples are obtained in the form of EEG raw data. The time duration of reading is of one minute. Various test are being performed on the alpha band EEG raw data.The readings are performed in different time duration of the entire day. The statistical analysis is being carried out on the EEG sample data in the form of various tests.

Keywords: electroencephalogram(EEG), biometrics, authentication, EEG raw data

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Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: F. S. Alavi, D. Mowla, F. Esmaeilzadeh

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In this paper, the Eclipse 300 simulator was used to perform compositional modeling of gas injection process for enhanced condensate recovery of a real gas condensate well in south of Iran here referred to as SA4. Some experimental data were used to tune the Peng-Robinson equation of state for this case. Different scenarios of gas injection at current reservoir pressure and at abandonment reservoir pressure had been considered with different gas compositions. Methane, carbon dioxide, nitrogen and two other gases with specified compositions were considered as potential gases for injection. According to the obtained results, nitrogen leads to highest pressure maintenance in the reservoir but methane results in highest condensate recovery among the selected injection gases. At low injection rates, condensate recovery percent is strongly affected by gas injection rate but this dependency shifts to zero at high injection rates. Condensate recovery is higher in all cases of injection at current reservoir pressure than injection at abandonment pressure. Using a constant injection rate, increasing the production well bottom hole pressure results in increasing the condensate recovery percent and time of gas breakthrough.

Keywords: gas-condensate reservoir, case-study, compositional modelling, enhanced condensate recovery, gas injection

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Authors: Machimontorn Promtong, Sherman C. P. Cheung, Guan H. Yeoh, Sara Vahaji, Jiyuan Tu

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The wide range of industrial applications involved with boiling flows promotes the necessity of establishing fundamental knowledge in boiling flow phenomena. For this purpose, a number of experimental and numerical researches have been performed to elucidate the underlying physics of this flow. In this paper, the improved wall boiling models, implemented on ANSYS CFX 14.5, were introduced to study subcooled boiling flow at elevated pressure. At the heated wall boundary, the Fractal model, Force balance approach and Mechanistic frequency model are given for predicting the nucleation site density, bubble departure diameter, and bubble departure frequency. The presented wall heat flux partitioning closures were modified to consider the influence of bubble sliding along the wall before the lift-off, which usually happens in the flow boiling. The simulation was performed based on the Two-fluid model, where the standard k-ω SST model was selected for turbulence modelling. Existing experimental data at around 5 bars were chosen to evaluate the accuracy of the presented mechanistic approach. The void fraction and Interfacial Area Concentration (IAC) are in good agreement with the experimental data. However, the predicted bubble velocity and Sauter Mean Diameter (SMD) are over-predicted. This over-prediction may be caused by consideration of only dispersed and spherical bubbles in the simulations. In the future work, the important physical mechanisms of bubbles, such as merging and shrinking during sliding on the heated wall will be incorporated into this mechanistic model to enhance its capability for a wider range of flow prediction.

Keywords: subcooled boiling flow, computational fluid dynamics (CFD), mechanistic approach, two-fluid model

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Authors: Md Tanvir Rahman, Mahbube Subhani, Mahmud Ashraf, Paul Kremer

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Cross-laminated timber (CLT) is made from layers of timber boards orthogonally oriented in the thickness direction, and due to this, CLT can withstand bi-axial bending in contrast with most other engineered wood products such as laminated veneer lumber (LVL) and glued laminated timber (GLT). Wood is cylindrically anisotropic in nature and is characterized by significantly lower elastic modulus and shear modulus in the planes perpendicular to the fibre direction, and is therefore classified as orthotropic material and is thus characterized by 9 elastic constants which are three elastic modulus in longitudinal direction, tangential direction and radial direction, three shear modulus in longitudinal tangential plane, longitudinal radial plane and radial tangential plane and three Poisson’s ratio. For simplification, timber materials are generally assumed to be transversely isotropic, reducing the number of elastic properties characterizing it to 5, where the longitudinal plane and radial planes are assumed to be planes of symmetry. The validity of this assumption was investigated through numerical modelling of CLT with both orthotropic mechanical properties and transversely isotropic material properties for three softwood species, which are Norway spruce, Douglas fir, Radiata pine, and three hardwood species, namely Victorian ash, Beech wood, and Aspen subjected to uniformly distributed loading under simply supported boundary condition. It was concluded that assuming the timber to be transversely isotropic results in a negligible error in the order of 1 percent. It was also observed that along with longitudinal elastic modulus, ratio of longitudinal shear modulus (GL) and rolling shear modulus (GR) has a significant effect on a deflection for CLT panels of lower span to depth ratio. For softwoods such as Norway spruce and Radiata pine, the ratio of longitudinal shear modulus, GL to rolling shear modulus GR is reported to be in the order of 12 to 15 times in literature. This results in shear flexibility in transverse layers leading to increased deflection under out-of-plane loading. The rolling shear modulus of hardwoods has been found to be significantly higher than those of softwoods, where the ratio between longitudinal shear modulus to rolling shear modulus as low as 4. This has resulted in a significant rise in research into the manufacturing of CLT from entirely from hardwood, as well as from a combination of softwood and hardwoods. The commonly used beam theory to analyze the performance of CLT panels under out-of-plane loads are the Shear analogy method, Gamma method, and k-method. The shear analogy method has been found to be the most effective method where shear deformation is significant. The effect of the ratio of longitudinal shear modulus and rolling shear modulus of cross-layer on the deflection of CLT under uniformly distributed load with respect to its length to depth ratio was investigated using shear analogy method. It was observed that shear deflection is reduced significantly as the ratio of the shear modulus of the longitudinal layer and rolling shear modulus of cross-layer decreases. This indicates that there is significant room for improvement of the bending performance of CLT through developing hybrid CLT from a mix of softwood and hardwood.

Keywords: rolling shear modulus, shear deflection, ratio of shear modulus and rolling shear modulus, timber

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Authors: Aliffaizi Arsat, Nur Ida Fatihah, Che Shalifullizam

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Malaysia is well known as a Muslim country and is quickly becoming a Global Halal-hub of Halal business in promoting Halal food products in both Muslim countries and non-Muslim countries. The objective of this study is to analyse the Muslim consumer purchased behaviour on doubtful Halal packed food by using theory of planned behaviour, to examine the mediating effects between certification, and Muslim consumer purchased behaviour on doubtful Halal packed food. The relevant questionnaires have been distributed in Kuala Selangor. Among the 300 Muslim participants from Kuala Selangor, Selangor, Malaysia, only 107 of them have returned the questionnaire with complete answers. The respondent’s rate was discovered to be at 35.67%. The data have been analysed by using SPSS version 22 and Structural equation modelling Partial Least Square SEM-PLS. There are three dimensions needed to identify Muslim consumer purchased behaviour on doubtful Halal packed food. They are attitude towards behaviour, subjective norm and perceived behavioural. All the results from this study show that the hypothesis has been supported. However, subjective norm had shown that there is a negative relationship towards Muslim consumer purchased behaviour on doubtful Halal packed food.

Keywords: Muslim consumer purchase behaviour, theory planned behaviour, doubtful Halal, certification

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Authors: Sherry Ann Ganase, Sandra Sookram

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In this paper, the vulnerability level to climate change is analysed using a comprehensive index, consisting of five pillars: human, social, natural, physical, and financial. A structural equation model is also applied to determine the indicators and relationships that exist between the observed environmental changes and the quality of life. Using survey data to model the results, a value of 0.382 is derived as the vulnerability level for San Pedro, where values closer to zero indicates lower vulnerability and values closer to one indicates higher vulnerability. The results showed the social pillar to be most vulnerable, with the indicator ‘participation’ ranked the highest in its cohort. Although, the environmental pillar is ranked as least vulnerable, the indicators ‘hazard’ and ‘biodiversity’ obtained scores closer to 0.4, suggesting that changes in the environment are occurring from natural and anthropogenic activities. These changes can negatively influence the quality of life as illustrated in the structural equation modelling. The study concludes by reporting on the need for collective action and participation by households in lowering vulnerability to ensure sustainable development and livelihood.

Keywords: climate change, participation, San Pedro, structural equation model, vulnerability index

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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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: Polvasut Mahaiamsiri, Piraphong Foosiri

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The purpose of this research is to explore the model of factors affecting decision making to select successor in family business of Thailand. A Structural Equation Model (SEM) was created from relevant theories and researches. Consequently, examine and analyse, the causal relation factors of Succession Plan, Recruitment Process and Strategic Planning, whether they have direct or indirect effects on Decision Making to Select Successor in family business. Units of analysis are selected from the family business, totalling 300 sampling. Population sampling is current owners or CEO from the percentage of six district areas in Thailand with multi-stage sampling. A set of questionnaires is used to collect data. An analysis of structural equation modelling (SEM) technique using AMOS 21 program is conducted to test the hypotheses and confirmatory factor analysis is performed and shows that these variables can be tested. The finding of this study revealed that these factors are separate constructs that combine to determine decision making to select successors.

Keywords: succession plan, family business, recruitment process, strategic planning, decision making to select successor

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Authors: Igor Vishnevskyi

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Introduction: The urinary bladder undergoes repeated strain during its working cycle, suggesting the presence of an efficient support system, force transmission, and mechanical amplification. The concept of a "hydrostatic skeleton" (HS) could contribute to our understanding of the functional relationships among bladder constituents. Methods: A multidisciplinary literature review was conducted to identify key features of the HS and to gather evidence supporting its applicability in urinary bladder biomechanics. The collected evidence was synthesized to propose a framework for understanding the potential hydrostatic properties of the urinary bladder based on existing knowledge and HS principles. Results: Our analysis revealed similarities in biomechanical features between living fluid-filled structures and the urinary bladder. These similarities include the geodesic arrangement of fibres, the role of enclosed fluid (urine) in force transmission, prestress as a determinant of stiffness, and the ability to maintain shape integrity during various activities. From a biomechanical perspective, urine may be considered an essential component of the bladder. The hydrostatic skeleton, with its autonomy and flexibility, may provide insights for researchers involved in bladder engineering. Discussion: The concept of a hydrostatic skeleton offers a holistic perspective for understanding bladder function by considering multiple mechanical factors as a single structure with emergent properties. Incorporating viewpoints from various fields on HS can help identify how this concept applies to live fluid-filled structures or organs and reveal its broader relevance to biological systems, both natural and artificial. Conclusion: The hydrostatic skeleton (HS) design principle can be applied to the urinary bladder. Understanding the bladder as a structure with HS can be instrumental in biomechanical modelling and engineering. Further research is required to fully elucidate the cellular and molecular mechanisms underlying HS in the bladder.

Keywords: hydrostatic skeleton, urinary bladder morphology, shape integrity, prestress, biomechanical modelling

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Authors: Viriyavudh Sim, WooYoung Jung

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Window system in high rise building is occasionally subjected to an excessive wind intensity, particularly during typhoon. The failure of window system did not affect overall safety of structural performance; however, it could endanger the safety of the residents. In this paper, comparison of fragility curves for window system of two residential buildings was studied. The probability of failure for individual window was determined with Monte Carlo Simulation method. Then, lognormal cumulative distribution function was used to represent the fragility. The results showed that windows located on the edge of leeward wall were more susceptible to wind load and the probability of failure for each window panel increased at higher floors.

Keywords: wind fragility, window system, high rise building, wind disaster

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Anthony Quansah, Tresor Ntaryamira, Shula Mushota

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Models for geotechnical centrifuge testing are usually made from re-formed soil, allowing for comparisons with naturally occurring soil deposits. However, there is a fundamental omission in this process because the natural soil is deposited in layers creating a unique structure. Nonlinear dynamics of clay material deposit is an essential part of changing the attributes of ground movements when subjected to solid seismic loading, particularly when diverse intensification conduct of speeding up and relocation are considered. The paper portrays a review of axis shaking table tests and numerical recreations to explore the offshore clay deposits subjected to seismic loadings. These perceptions are accurately reenacted by DEEPSOIL with appropriate soil models and parameters reviewed from noteworthy centrifuge modeling researches. At that point, precise 1-D site reaction investigations are performed on both time and recurrence spaces. The outcomes uncover that for profound delicate clay is subjected to expansive quakes, noteworthy increasing speed lessening may happen close to the highest point of store because of soil nonlinearity and even neighborhood shear disappointment; nonetheless, huge enhancement of removal at low frequencies are normal in any case the forces of base movements, which proposes that for dislodging touchy seaward establishments and structures, such intensified low-recurrence relocation reaction will assume an essential part in seismic outline. This research shows centrifuge as a tool for creating a layered sample important for modelling true soil behaviour (such as permeability) which is not identical in all directions. Currently, there are limited methods for creating layered soil samples.

Keywords: seismic analysis, layered modeling, terotechnology, finite element modeling

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Authors: Sana Masmoudi Mardassi, Yosra Makni Fourati

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Regulators highlight the importance of the Audit Committee (AC) as a key internal corporate governance mechanism. One of the most important roles of this committee is to oversee the financial reporting process. The purpose of this paper is to examine the link between the characteristics of an audit committee and the financial reporting quality by investigating whether the characteristics of audit committees are associated with improved financial reporting quality, especially the Real Earnings Management. In the current study, a panel data from 80 nonfinancial companies listed on the Amsterdam Stock Exchange during the period between 2010 and 2017 were used. To measure audit committee characteristics, four proxies have been used, specifically, audit committee independence, financial expertise, gender diversity and AC meetings. For this research, a linear regression model was used to identify the influence of a set of board characteristics of the audit committee on real earnings management after controlling for firm audit committee size, leverage, size, loss, growth and board size. This research provides empirical evidence of the association between audit committee independence, financial expertise, gender diversity and meetings and Real Earnings Management (REM) as a proxy of financial reporting quality. The study finds that independence and AC Gender diversity are strongly related to financial reporting quality. In fact, these two characteristics constrain REM. The results also suggest that AC- financial expertise reduces to some extent, the likelihood of engaging in REM. These conclusions provide support then to the audit committee requirement under the Dutch Corporate Governance Code rules regarding gender diversity and AC meetings.

Keywords: audit committee, financial expertise, independence, real earnings management

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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Authors: Sami Kabir, Sadek Hossain Khoka

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Open Government Data (OGD) refers to all data produced by government which are accessible in reusable way by common people with access to Internet and at free of cost. In line with “Digital Bangladesh” vision of Bangladesh government, the concept of open data has been gaining momentum in the country. Opening all government data in digital and customizable format from single platform can enhance e-governance which will make government more transparent to the people. This paper presents a well-in-progress case study on OGD portal by Bangladesh Government in order to link decentralized data. The initiative is intended to facilitate e-service towards citizens through this one-stop web portal. The paper further discusses ways of collecting data in digital format from relevant agencies with a view to making it publicly available through this single point of access. Further, possible layout of this web portal is presented.

Keywords: e-governance, one-stop web portal, open government data, reusable data, web of data

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Authors: Emad Alenany, M. Adel El-Baz

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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: queueing network, discrete-event simulation, health applications, SPT

Procedia PDF Downloads 187

Authors: C. W. F. Che Wan Putra, M. Alshawi, M. S. Al Ahbabi, M. Jabakhanji

Abstract:

The construction industry is a multi-disciplinary and multi-national industry, which has an important role to play within the overall economy of any country. There are major challenges to an improved performance within the industry. Particularly lacking is, the ability to capture the large amounts of information generated during the life-cycle of projects and to make these available, in the right format, so that professionals can then evaluate alternative solutions based on life-cycle analysis. The fragmented nature of the industry is the main reason behind the unavailability and ill utilisation of project information. The lack of adequately engaging clients and managing their requirements contributes adversely to construction budget and schedule overruns. This is a difficult task to achieve, particularly if clients are not continuously and formally involved in the design and construction process, which means that the design intent is left to designers that may not always satisfy clients’ requirements. Client lead is strongly recognised in bringing change through better collaboration between project stakeholders. However, one of the major challenges is that collaboration is operated under conventional procurement methods, which hugely limit the stakeholders’ roles and responsibilities to bring about the required level of collaboration. A research has been conducted with a typical project in the UAE. A qualitative research work was conducted including semi-structured interviews with project partners to discover the real reasons behind this delay. The case study also investigated the real causes of the problems and if they can be adequately addressed by BIM and IPD. Special focus was also placed on the Client leadership and the role the Client can play to eliminate/minimize these problems. It was found that part of the ‘key elements’ from which the problems exist can be attributed to the client leadership and the collaborative environment and BIM.

Keywords: client leadership, building information modelling (BIM), integrated project delivery (IPD), case study

Procedia PDF Downloads 323

Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

Abstract:

The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

Procedia PDF Downloads 30

Authors: Lukman Abiodun Nafiu

Abstract:

This study examines the impact evaluation of vaccination against eight-child-killer diseases on under-five children mortality at Mbale District. It was driven by three specific objectives which are to determine the proportion of under-five children mortality due to the eight-child-killer diseases to the total under-five children mortality; establish the cause-effect relationship between the eight-child-killer diseases and under-five children mortality; as well as establish the dependence of under-five children mortality in the location at Mbale District. A community based cross-sectional and longitudinal (panel) study design involving both quantitative and qualitative (focus group discussion and in-depth interview) approaches was employed over a period of 36 months. Multi-stage cluster design involving Health Sub-District (HSD), Forms of Ownership (FOO) and Health Facilities Centres (HFC) as the first, second and third stages respectively was used. Data was collected regarding the eight-child-killer diseases namely: measles, pneumonia, pertussis (whooping cough), diphtheria, poliomyelitis (polio), tetanus, haemophilus influenza, rotavirus gastroenteritis and mortality regarding immunized and non-immunized children aged 0-59 months. We monitored the children over a period of 24 months. The study used a sample of 384 children out of all the registered children for each year at Mbale Referral Hospital and other Primary Health Care Centres (HCIV, HCIII and HCII) at Mbale District between 2015 and 2019. These children were followed from birth to their current state (living or dead). The data collected in this study was analysed using cross tabulation and the chi-square test. The study concluded that majority of mothers at Mbale district took their children for immunization and thus reducing the occurrence of under-five children mortality. Overall, 2.3%, 4.6%, 3.1%, 5.4%, 1.5%, 3.8%, 0.0% and 0.0% of under-five children had polio, tetanus, diphtheria, measles, pertussis, pneumonia, haemophilus influenzae and rotavirus gastroenteritis respectively across all the sub counties at Mbale district during the period considered. Also, different locations (sub counties) do not have significant influence on the occurrence of these eight-child-killer diseases among the under-five children at Mbale district. Therefore, the study recommended that government and agencies should continue to work together to implement measures of vaccination programs and increasing access to basic health care with a continuous improvement on the social interventions to progress child survival.

Keywords: Diseases, Mortality, Children, Vaccination

Procedia PDF Downloads 124

Authors: Nur A. Özdemir, Sude Erkin, Hatice K. Güney, Cemre S. Atılgan, Enes Huylu, Hüseyin Y. Altıntaş, Aysemin Top, Özak Durmuş

Abstract:

Carbon emissions produced during a product’s life cycle, from extraction of raw materials up to waste disposal and market consumption activities are the major contributors to global warming. In the light of the science-based targets (SBT) leading the way to a zero-carbon economy for sustainable growth of the companies, carbon footprint reporting of the purchased goods has become critical for identifying hotspots and best practices for emission reduction opportunities. In line with Ford Otosan's corporate sustainability strategy, research was conducted to evaluate the carbon footprint of purchased products in accordance with Scope 3 of the Greenhouse Gas Protocol (GHG). The purpose of this paper is to develop a systematic and transparent methodology to calculate carbon footprint of the products produced by automotive OEMs (Original Equipment Manufacturers) within the context of automobile supply chain management. To begin with, primary material data were collected through IMDS (International Material Database System) corresponds to company’s three distinct types of vehicles including Light Commercial Vehicle (Courier), Medium Commercial Vehicle (Transit and Transit Custom), Heavy Commercial Vehicle (F-MAX). Obtained material data was classified as metals, plastics, liquids, electronics, and others to get insights about the overall material distribution of produced vehicles and matched to the SimaPro Ecoinvent 3 database which is one of the most extent versions for modelling material data related to the product life cycle. Product life cycle analysis was calculated within the framework of ISO 14040 – 14044 standards by addressing the requirements and procedures. A comprehensive literature review and cooperation with suppliers were undertaken to identify the production methods of parts used in vehicles and to find out the amount of scrap generated during part production. Cumulative weight and material information with related production process belonging the components were listed by multiplying with current sales figures. The results of the study show a key modelling on carbon footprint of products and processes based on a scientific approach to drive sustainable growth by setting straightforward, science-based emission reduction targets. Hence, this study targets to identify the hotspots and correspondingly provide broad ideas about our understanding of how to integrate carbon footprint estimates into our company's supply chain management by defining convenient actions in line with climate science. According to emission values arising from the production phase including raw material extraction and material processing for Ford OTOSAN vehicles subjected in this study, GHG emissions from the production of metals used for HCV, MCV and LCV account for more than half of the carbon footprint of the vehicle's production. Correspondingly, aluminum and steel have the largest share among all material types and achieving carbon neutrality in the steel and aluminum industry is of great significance to the world, which will also present an immense impact on the automobile industry. Strategic product sustainability plan which includes the use of secondary materials, conversion to green energy and low-energy process design is required to reduce emissions of steel, aluminum, and plastics due to the projected increase in total volume by 2030.

Keywords: automotive, carbon footprint, IMDS, scope 3, SimaPro, sustainability

Procedia PDF Downloads 108