Search results for: hierarchical Bayesian framework

Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Oluwayemisi O. Alaba, John O. Olaomi

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The 2013 Nigerian Demographic Health Survey (NDHS) data was used to investigate the determinants of family size in Nigeria using the geo-additive model. The fixed effect of categorical covariates were modelled using the diffuse prior, P-spline with second-order random walk for the nonlinear effect of continuous variable, spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effects of the community and household. The Negative Binomial distribution was used to handle overdispersion of the dependent variable. Inference was fully Bayesian approach. Results showed a declining effect of secondary and higher education of mother, Yoruba tribe, Christianity, family planning, mother giving birth by caesarean section and having a partner who has secondary education on family size. Big family size is positively associated with age at first birth, number of daughters in a household, being gainfully employed, married and living with partner, community and household effects.

Keywords: Bayesian analysis, family size, geo-additive model, negative binomial

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Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

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

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

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Authors: Luis Javier Serrano Tamayo

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This research on technology makes a first approach to the selection of an amphibious landing ship with strategic capabilities, through the implementation of a multi-criteria model using Analytical Hierarchical Process (AHP), in which a significant group of alternatives of latest technology has been considered. The variables were grouped at different levels to match design and performance characteristics, which affect the lifecycle as well as the acquisition, maintenance and operational costs. The model yielded an overall measure of effectiveness and an overall measure of cost of each kind of ship that was compared each other inside the model and showed in a Pareto chart. The modeling was developed using the Expert Choice software, based on AHP method.

Keywords: analytic hierarchy process, multi-criteria decision-making, Pareto analysis, Colombian Marine Corps, projection operations, expert choice, amphibious landing ship

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Alireza Vahid, Santu Rana, Sunil Gupta, Pratibha Vellanki, Svetha Venkatesh, Thomas Dorin

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Aluminum is the preferred material for lightweight applications and its alloys are constantly improving. The high strength 7xxx alloys have been extensively used for structural components in aerospace and automobile industries for the past 50 years. In the next decade, a great number of airplanes will be retired, providing an obvious source of valuable used metals and great demand for cost-effective methods to re-use these alloys. The design of proper aerospace alloys is primarily based on optimizing strength and ductility, both of which can be improved by controlling the additional alloying elements as well as heat treatment conditions. In this project, we explore the design of high-performance alloys with 7xxx as a base material. These designed alloys have to be optimized and improved to compare with modern 7xxx-series alloys and to remain competitive for aircraft manufacturing. Aerospace alloys are extremely complex with multiple alloying elements and numerous processing steps making optimization often intensive and costly. In the present study, we used Bayesian optimization algorithm, a well-known adaptive design strategy, to optimize this multi-variable system. An Al alloy was proposed and the relevant heat treatment schedules were optimized, using the tensile yield strength as the output to maximize. The designed alloy has a maximum yield strength and ultimate tensile strength of more than 730 and 760 MPa, respectively, and is thus comparable to the modern high strength 7xxx-series alloys. The microstructure of this alloy is characterized by electron microscopy, indicating that the increased strength of the alloy is due to the presence of a high number density of refined precipitates.

Keywords: aluminum alloys, Bayesian optimization, heat treatment, tensile properties

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Authors: L. Melzerová, T. Janda, M. Šejnoha, J. Šejnoha

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Two finite element (FEM) models are presented in this paper to address the random nature of the response of glued timber structures made of wood segments with variable elastic moduli evaluated from 3600 indentation measurements. This total database served to create the same number of ensembles as was the number of segments in the tested beam. Statistics of these ensembles were then assigned to given segments of beams and the Latin Hypercube Sampling (LHS) method was called to perform 100 simulations resulting into the ensemble of 100 deflections subjected to statistical evaluation. Here, a detailed geometrical arrangement of individual segments in the laminated beam was considered in the construction of two-dimensional FEM model subjected to in four-point bending to comply with the laboratory tests. Since laboratory measurements of local elastic moduli may in general suffer from a significant experimental error, it appears advantageous to exploit the full scale measurements of timber beams, i.e. deflections, to improve their prior distributions with the help of the Bayesian statistical method. This, however, requires an efficient computational model when simulating the laboratory tests numerically. To this end, a simplified model based on Mindlin’s beam theory was established. The improved posterior distributions show that the most significant change of the Young’s modulus distribution takes place in laminae in the most strained zones, i.e. in the top and bottom layers within the beam center region. Posterior distributions of moduli of elasticity were subsequently utilized in the 2D FEM model and compared with the original simulations.

Keywords: Bayesian inference, FEM, four point bending test, laminated timber, parameter estimation, prior and posterior distribution, Young’s modulus

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Authors: Wiem Daoud Ben Amor, Luis Martínez López, Hela Moalla Frikha

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Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain contexts where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ARAS-ELH). Within the ARAS-ELH approach, the DM can diagnose the results (the ranking of the alternatives) in a decomposed style, i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e the collective final results of all experts able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ARAS-ELH technique makes it easier for decision-makers to understand the results. Finally, An MCGDM case study is given to illustrate the proposed approach.

Keywords: additive ratio assessment, extended hierarchical linguistic, multi-criteria group decision making problems, multi granular linguistic contexts

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Authors: Elham Saraee, Saurabh Singh, Margrit Betke

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Evaluation of a person’s posture while exercising is important in physical therapy. During a therapy session, a physical therapist or a monitoring system must assure that the person is performing an exercise correctly to achieve the desired therapeutic effect. In this work, we introduce a system called POSTURECHECK for exercise assessment in physical therapy. POSTURECHECK assesses the posture of a person who is exercising with the Proficio robotic arm while being recorded by the Microsoft Kinect interface. POSTURECHECK extracts unique features from the person’s upper body during the exercise, and classifies the sequence of postures as correct or incorrect using Bayesian estimation and majority voting. If POSTURECHECK recognizes an incorrect posture, it specifies what the user can do to correct it. The result of our experiment shows that POSTURECHECK is capable of recognizing the incorrect postures in real time while the user is performing an exercise.

Keywords: Bayesian estimation, majority voting, Microsoft Kinect, PostureCheck, Proficio robotic arm, upper body physical therapy

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Authors: Chechen Liao, Pui-Lai To, Chiao-Ying Chen

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An introduction of internet auction has significantly widened the pool of consumers who participate in auctions and increased the number of companies attempting to sell their products in an auction format. Previous research on auctions has focused almost exclusively on the behavior of professional bidders. In this study, we focus on the characteristic of seller, auction parameter and the effect of supply and demand, and examine these impacts on auction effectiveness. In particular, a framework for online auction effectiveness was developed. The framework will help researchers and practitioner to find ways to improve online auction effectiveness.

Keywords: Auction Effectiveness, Framework Developing, Online Auction, Selling Strategy

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Jaewook Ha, Jin-beak Kim, Seongmin Kim

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In order to solve global warming and climate change issues, increased efforts have been devoted towards clean and sustainable energy sources as well as new energy generating devices. Nanogenerator is a device that converts mechanical/thermal energy as produced by small-scale physical change into electricity. Here we propose that nature-leaf surface could be used for preparation of a triboelectric nanogenerator. The nature-leaf surface consists of polydimethylsiloxane microscale pillars and polytetrafluoroethylene nanoparticles. Interaction between the nature-leaf surface and water was studied and the electrical outputs from the motion of single water drop were measured. A 40-μL water drop can generate a peak voltage of 1 V and a peak current of 0.7 μA. This nanogenerator might be used to drive electric devices in the outdoor environments in a sustainable manner.

Keywords: hierarchical surface, lotus-leaf, electrical generator, waterdrop

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Authors: Andi Darmawan, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Diabetes mellitus (DM) is a chronic disease in human that occurred if pancreas cannot produce enough of insulin hormone or the body uses ineffectively insulin hormone which causes increasing level of glucose in the blood, or it was called hyperglycemia. In Indonesia, DM is a serious disease on health because it can cause blindness, kidney disease, diabetic feet (gangrene), and stroke. The type of DM criteria can also be divided based on the main causes; they are DM type 1, type 2, and gestational. Diabetes type 1 or previously known as insulin-independent diabetes is due to a lack of production of insulin hormone. Diabetes type 2 or previously known as non-insulin dependent diabetes is due to ineffective use of insulin while gestational diabetes is a hyperglycemia that found during pregnancy. The most one type commonly found in patient is DM type 2. The main factors of this disease are genetic (A) and life style (E). Those disease with 2 factors can be constructed with additive genetic and unique environment (AE) model. In this article was discussed parameter estimation of AE model using Bayesian method and the inheritance character simulation on parent-offspring. On the AE model, there are response variable, predictor variables, and parameters were capable of representing the number of population on research. The population can be measured through a taken random sample. The response and predictor variables can be determined by sample while the parameters are unknown, so it was required to estimate the parameters based on the sample. Estimation of AE model parameters was obtained based on a joint posterior distribution. The simulation was conducted to get the value of genetic variance and life style variance. The results of simulation are 0.3600 for genetic variance and 0.0899 for life style variance. Therefore, the variance of genetic factor in DM type 2 is greater than life style.

Keywords: AE model, Bayesian method, diabetes mellitus type 2, genetic, life style

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Authors: Betty Johanna Garzon Rozo, Jonathan Crook, Fernando Moreira

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Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. We propose a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severity distributions, and to calculate the capital for Operational Risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mix distribution (the Lognormal for the body of losses and the Generalized Pareto Distribution for the tail) via extreme value theory using SAS®, (ii) the multivariate skew t-copula applied for the first time for operational losses and (iii) Bayesian theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyses a newly operational loss data set, SAS Global Operational Risk Data [SAS OpRisk], to model operational risk at international financial institutions. All the severity models are constructed in SAS® 9.2. We implement the procedure PROC SEVERITY and PROC NLMIXED. This paper focuses in describing this implementation.

Keywords: operational risk, loss distribution approach, extreme value theory, copulas

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Authors: Paul Rowley, Adam Thirkill, Nick Doylend, Philip Leicester, Becky Gough

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The dynamic performance of all energy generation technologies is impacted to varying degrees by the stochastic properties of the wider system within which the generation technology is located. This stochasticity can include the varying nature of ambient renewable energy resources such as wind or solar radiation, or unpredicted changes in energy demand which impact upon the operational behaviour of thermal generation technologies. An understanding of these stochastic impacts are especially important in contexts such as highly distributed (or embedded) generation, where an understanding of issues affecting the individual or aggregated performance of high numbers of relatively small generators is especially important, such as in ESCO projects. Probabilistic evaluation of monitored or simulated performance data is one technique which can provide an insight into the dynamic performance characteristics of generating systems, both in a prognostic sense (such as the prediction of future performance at the project’s design stage) as well as in a diagnostic sense (such as in the real-time analysis of underperforming systems). In this work, we describe the development, application and outcomes of a new approach to the acquisition of datasets suitable for use in the subsequent performance and impact analysis (including the use of Bayesian approaches) for a number of distributed generation technologies. The application of the approach is illustrated using a number of case studies involving domestic and small commercial scale photovoltaic, solar thermal and natural gas boiler installations, and the results as presented show that the methodology offers significant advantages in terms of plant efficiency prediction or diagnosis, along with allied environmental and social impacts such as greenhouse gas emission reduction or fuel affordability.

Keywords: renewable energy, dynamic performance simulation, Bayesian analysis, distributed generation

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Authors: Agnieszka Feliczak-Guzik, Paulina Szczyglewska, Izabela Nowak

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A catalyst-assisted oxidation reaction is one of the key reactions exploited by various industries. Their conductivity yields essential compounds and intermediates, such as alcohols, epoxides, aldehydes, ketones, and organic acids. Researchers are devoting more and more attention to developing active and selective materials that find application in many catalytic reactions, such as cyclohexene epoxidation. This reaction yields 1,2-epoxycyclohexane and 1,2-diols as the main products. These compounds are widely used as intermediates in the perfume industry and synthesizing drugs and lubricants. Hence, our research aimed to use hierarchical zeolites modified with transition metal ions, e.g., Nb, V, and Ta, in the epoxidation reaction of cyclohexene using microwaveheating. Hierarchical zeolites are materials with secondary porosity, mainly in the mesoporous range, compared to microporous zeolites. In the course of the research, materials based on two commercial zeolites, with Faujasite (FAU) and Zeolite Socony Mobil-5 (ZSM-5) structures, were synthesized and characterized by various techniques, such as X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and low-temperature nitrogen adsorption/desorption isotherms. The materials obtained were then used in a cyclohexene epoxidation reaction, which was carried out as follows: catalyst (0.02 g), cyclohexene (0.1 cm3), acetonitrile (5 cm3) and dihydrogen peroxide (0.085 cm3) were placed in a suitable glass reaction vessel with a magnetic stirrer inside in a microwave reactor. Reactions were carried out at 45° C for 6 h (samples were taken every 1 h). The reaction mixtures were filtered to separate the liquid products from the solid catalyst and then transferred to 1.5 cm3 vials for chromatographic analysis. The test techniques confirmed the acquisition of additional secondary porosity while preserving the structure of the commercial zeolite (XRD and low-temperature nitrogen adsorption/desorption isotherms). The results of the activity of the hierarchical catalyst modified with niobium in the cyclohexene epoxidation reaction indicate that the conversion of cyclohexene, after 6 h of running the process, is about 70%. As the main product of the reaction, 2-cyclohexanediol was obtained (selectivity > 80%). In addition to the mentioned product, adipic acid, cyclohexanol, cyclohex-2-en-1-one, and 1,2-epoxycyclohexane were also obtained. Furthermore, in a blank test, no cyclohexene conversion was obtained after 6 h of reaction. Acknowledgments The work was carried out within the project “Advanced biocomposites for tomorrow’s economy BIOG-NET,” funded by the Foundation for Polish Science from the European Regional Development Fund (POIR.04.04.00-00-1792/18-00.

Keywords: epoxidation, oxidation reactions, hierarchical zeolites, synthesis

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Karrar Raoof Kareem Kamoona, Enas Fathi Taher AlHares, Zeynep Isik

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The various disciplines in the construction industry and the co-existence of the people in the various disciplines are what builds well-developed, closely-knit interpersonal skills at various hierarchical levels thus leading to a varied way of leadership. The varied decision making aspects during the lifecycle of a project include: autocratic, participatory and last but not least, free-rein. We can classify some of the decision makers in the construction industry in a hierarchical manner as follows: project executive, project manager, superintendent, office engineer and finally the field engineer. This survey looked at how decisions are made during the construction period by the key stakeholders in the project. From the paper it is evident that the three decision making aspects can be used at different times or at times together in order to bring out the best leadership decision. A blend of different leadership styles should be used to enhance the success rate during the project lifecycle.

Keywords: leadership style, construction, decision-making, built environment

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Authors: Walaa Diab, Baher Atlam, Nadia El Nimer

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Egypt faces many obvious economic challenges, and it is so clear that a real economic transformation is needed to address those problems, especially after the recent decisions of floating the Egyptian pound and the gradual subsidy cuts that are trying to meet the needed conditions to get the IMF support of (a £12bn loan) for its economic reform program. Following the post-2008 revival of the interest in the fiscal policy and its vital role in speeding up or slowing down the economic growth. Here comes the value of this paper as it seeks to analyze the macroeconomic effects of fiscal policy in Egypt by applying A Bayesian SVAR Approach. The study uses the Bayesian method because it includes the prior information and no relevant information is omitted and so it is well suited for rational, evidence-based decision-making. Since the study aims to define the effects of fiscal policy shocks in Egypt to help the decision-makers in determining the proper means to correct the structural problems in the Egyptian economy, it has to study the period of 1990s economic reform, but unfortunately; the available data is on an annual frequency. Thus, it uses annual time series to study the period 1991: 2005 And quarterly data over the period 2006–2016. It uses a set of six main variables includes government expenditure and net tax revenues as fiscal policy arms affecting real GDP, unemployment, inflation and the interest rate. The study also tries to assess the 'crowding out' effects by considering the effects of government spending and government revenue shocks on the composition of GDP, namely, on private consumption and private investment. Last but not least the study provides its policy implications regarding the needed role of fiscal policy in Egypt in the upcoming economic reform building on the results it concludes from the previous reform program.

Keywords: fiscal policy, government spending, structural vector autoregression, taxation

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Authors: Smita Singh

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The paper develops a critical framework to the hegemonic discourses resorted to by the dominant powers in the global security architecture. Within this framework, security is viewed as a discourse through which identities and threats are represented and produced to legitimize the security concerns of few at the cost of others. International security have long been driven and dominated by power relations. Since the end of the Cold War, the global transformations have triggered contestations to the idea of security at both theoretical and practical level. These widening and deepening of the concept of security have challenged the existing power hierarchies at the theoretical level but not altered the substance and actors defining it. When discourses are introduced into security studies, several critical questions erupt: how has power shaped security policies of the globe through language? How does one understand the meanings and impact of those discourses? Who decides the agenda, rules, players and outliers of the security? Language as a symbolic system and form of power is fluid and not fixed. Over the years the dominant Western powers, led by the United States of America have employed various discursive practices such as humanitarian intervention, responsibility to protect, non proliferation, human rights, war on terror and so on to reorient the constitution of identities and interests and hence the policies that need to be adopted for its actualization. These power relations are illustrated in this paper through the narratives used in the nonproliferation regime. The hierarchical security dynamics is a manifestation of the global power relations driven by many factors including discourses.

Keywords: hegemonic discourse, global security, non-proliferation regime, power politics

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Authors: Md. Rashidul Hasan, Atikur Rahman Baizid

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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.

Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

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This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Deepti Mishra, K. K Pant, Xiu Song Zhao, Muxina Konarova

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Catalytic transformation of simplest hydrocarbon methane into benzene and valuable chemicals over Mo/HZSM-5 has a great economic potential, however, it suffers serious hurdles due to the blockage in the micropores because of extensive coking at high temperature during methane dehydroaromatization (MDA). Under such conditions, it necessitates the design of micro/mesoporous ZSM-5, which has the advantages viz. uniform dispersibility of MoOx species, consequently the formation of active Mo sites in the micro/mesoporous channel and lower carbon deposition because of improved mass transfer rate within the hierarchical pores. In this study, we report a unique strategy to control the porous structures of ZSM-5 through a dual templating approach, utilizing C6 and C12 -surfactants as porogen. DFT studies were carried out to correlate the ZSM-5 framework development using the C6 and C12 surfactants with structure directing agent. The structural and morphological parameters of the synthesized ZSM-5 were explored in detail to determine the crystallinity, porosity, Si/Al ratio, particle shape, size, and acidic strength, which were further correlated with the physicochemical and catalytic properties of Mo modified HZSM-5 catalysts. After Mo incorporation, all the catalysts were tested for MDA reaction. From the activity test, it was observed that C6 surfactant-modified hierarchically porous Mo/HZSM-5(H) showed the highest benzene formation rate (1.5 μmol/gcat. s) and longer catalytic stability up to 270 min of reaction as compared to the conventional microporous Mo/HZSM-5(C). In contrary, C12 surfactant modified Mo/HZSM-5(D) is inferior towards MDA reaction (benzene formation rate: 0.5 μmol/gcat. s). We ascribed that the difference in MDA activity could be due to the hierarchically interconnected meso/microporous feature of Mo/HZSM-5(H) that precludes secondary reaction of coking from benzene and hence contributing substantial stability towards MDA reaction.

Keywords: hierarchical pores, Mo/HZSM-5, methane dehydroaromatization, coke deposition

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Authors: Seon-Ho Yoon, Jin-Young Choi, Dong-Jun Won

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This paper presents hierarchical operation strategies which are minimizing operation error between day ahead operation plan and real time operation. Operating power systems between centralized and decentralized approaches can be represented as hierarchical control scheme, featured as primary control, secondary control and tertiary control. Primary control is known as local control, featuring fast response. Secondary control is referred to as microgrid Energy Management System (EMS). Tertiary control is responsible of coordinating the operations of multi-microgrids. In this paper, we formulated 3 stage microgrid operation strategies which are similar to hierarchical control scheme. First stage is to set a day ahead scheduled output power of Battery Energy Storage System (BESS) which is only controllable source in microgrid and it is optimized to minimize cost of exchanged power with main grid using Particle Swarm Optimization (PSO) method. Second stage is to control the active and reactive power of BESS to be operated in day ahead scheduled plan in case that State of Charge (SOC) error occurs between real time and scheduled plan. The third is rescheduling the system when the predicted error is over the limited value. The first stage can be compared with the secondary control in that it adjusts the active power. The second stage is comparable to the primary control in that it controls the error in local manner. The third stage is compared with the secondary control in that it manages power balancing. The proposed strategies will be applied to one of the buildings in Electronics and Telecommunication Research Institute (ETRI). The building microgrid is composed of Photovoltaic (PV) generation, BESS and load and it will be interconnected with the main grid. Main purpose of that is minimizing operation cost and to be operated in scheduled plan. Simulation results support validation of proposed strategies.

Keywords: Battery Energy Storage System (BESS), Energy Management System (EMS), Microgrid (MG), Particle Swarm Optimization (PSO)

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Authors: Kita R. Ashman, Anthony R. Rendall, Matthew R. E. Symonds, Desley A. Whisson

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Forest cover is decreasing globally, chiefly due to the conversion of forest to agricultural landscapes. In contrast, the area under plantation forestry is increasing significantly. For wildlife occupying landscapes where native forest is the dominant land cover, plantations generally represent a lower value habitat; however, plantations established on land formerly used for pasture may benefit wildlife by providing temporary forest habitat and increasing connectivity. This study investigates the influence of landscape, site, and climatic factors on koala population density in far south-west Victoria where there has been extensive plantation establishment. We conducted koala surveys and habitat characteristic assessments at 72 sites across three habitat types: plantation, native vegetation blocks, and native vegetation strips. We employed a hierarchical modeling framework for estimating abundance and constructed candidate multinomial N-mixture models to identify factors influencing the abundance of koalas. We detected higher mean koala density in plantation sites (0.85 per ha) than in either native block (0.68 per ha) or native strip sites (0.66 per ha). We found five covariates of koala density and using these variables, we spatially modeled koala abundance and discuss factors that are key in determining large-scale distribution and density of koala populations. We provide a distribution map that can be used to identify high priority areas for population management as well as the habitat of high conservation significance for koalas. This information facilitates the linkage of ecological theory with the on-ground implementation of management actions and may guide conservation planning and resource management actions to consider overall landscape configuration as well as the spatial arrangement of plantations adjacent to the remnant forest.

Keywords: abundance modelling, arboreal mammals plantations, wildlife conservation

Procedia PDF Downloads 115

Authors: Umberto Platini

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This paper analyses the role of government fragmentation as predictor of the use of emergency decrees in parliamentary democracies. In particular, it focuses on the relationship between ideological divisiveness within cabinets and the choice by executives to issue emergency decrees rather initiating ordinary legislative procedures. A Bayesian multilevel analysis conducted on the population of government-initiated legislation in Italy between 1996 and 2018 finds significant evidence that those legislative proposals which are further away from the ideological centre of gravity of the executive are around three times more likely to be issued as emergency decrees. Likewise, legislative projects regulating more contentious policy areas are significantly more likely to be issued by decree. However, for more contentious issues the importance of ideological distance as a predictor diminishes. This evidence suggests that cabinets prefer decrees to ordinary legislative procedures when they expect that the bargaining environment in Parliament is more hostile. These results persist regardless of the fluctuations of the political-economic cycle. Their robustness is also tested against a battery of controls and against fixed effects both at the government level and at the legislature level.

Keywords: Bayesian multilevel logit models, executive action, executive decrees, ideology, legislative studies, polarization

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Authors: Mohammed Hasan Salahat, Stave Wade

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This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology

Procedia PDF Downloads 560