Search results for: geospatial technology competency model

Authors: Mazaher Yarmohamadi-Vasel, Ali Reza Modarresi-Alama, Sahar Shabzendedara

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Purpose/Objectives: The first purpose was synthesize Poly(N-(sulfophenyl)aniline) nanoflowers (PSANFLs) and Poly(N-(sulfophenyl)aniline) nanofibers/titanium dioxide nanoparticles ((PSANFs/TiO2NPs) by a solid-state mechano-chemical reaction and template-free method and use them in hybrid solar cell. Also, our second aim was to increase the solubility and the processability of conjugated nanomaterials in water through polar functionalized materials. poly[N-(4-sulfophenyl)aniline] is easily soluble in water because of the presence of polar groups of sulfonic acid in the polymer chain. Materials/Methods: Iron (III) chloride hexahydrate (FeCl3∙6H2O) were bought from Merck Millipore Company. Titanium oxide nanoparticles (TiO2, <20 nm, anatase) and Sodium diphenylamine-4-sulfonate (99%) were bought from Sigma-Aldrich Company. Titanium dioxide nanoparticles paste (PST-20T) was prepared from Sharifsolar Co. Conductive glasses coated with indium tin oxide (ITO) were bought from Xinyan Technology Co (China). For the first time we used the solid-state mechano-chemical reaction and template-free method to synthesize Poly(N-(sulfophenyl)aniline) nanoflowers. Moreover, for the first time we used the same technique to synthesize nanocomposite of Poly(N-(sulfophenyl)aniline) nanofibers and titanium dioxide nanoparticles (PSANFs/TiO2NPs) also for the first time this nanocomposite was synthesized. Examining the results of electrochemical calculations energy gap obtained by CV curves and UV–vis spectra demonstrate that PSANFs/TiO2NPs nanocomposite is a p-n type material that can be used in photovoltaic cells. Doctor blade method was used to creat films for three kinds of hybrid solar cells in terms of different patterns like ITO│TiO2NPs│Semiconductor sample│Al. In the following, hybrid photovoltaic cells in bilayer and bulk heterojunction structures were fabricated as ITO│TiO2NPs│PSANFLs│Al and ITO│TiO2NPs│PSANFs /TiO2NPs│Al, respectively. Fourier-transform infrared spectra, field emission scanning electron microscopy (FE-SEM), ultraviolet-visible spectra, cyclic voltammetry (CV) and electrical conductivity were the analysis that used to characterize the synthesized samples. Results and Conclusions: FE-SEM images clearly demonstrate that the morphology of the synthesized samples are nanostructured (nanoflowers and nanofibers). Electrochemical calculations of band gap from CV curves demonstrated that the forbidden band gap of the PSANFLs and PSANFs/TiO2NPs nanocomposite are 2.95 and 2.23 eV, respectively. I–V characteristics of hybrid solar cells and their power conversion efficiency (PCE) under 100 mWcm−2 irradiation (AM 1.5 global conditions) were measured that The PCE of the samples were 0.30 and 0.62%, respectively. At the end, all the results of solar cell analysis were discussed. To sum up, PSANFLs and PSANFLs/TiO2NPs were successfully synthesized by an affordable and straightforward mechanochemical reaction in solid-state under the green condition. The solubility and processability of the synthesized compounds have been improved compared to the previous work. We successfully fabricated hybrid photovoltaic cells of synthesized semiconductor nanostructured polymers and TiO2NPs as different architectures. We believe that the synthesized compounds can open inventive pathways for the development of other Poly(N-(sulfophenyl)aniline based hybrid materials (nanocomposites) proper for preparing new generation solar cells.

Keywords: mechanochemical synthesis, PSANFLs, PSANFs/TiO2NPs, solar cell

Procedia PDF Downloads 71

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 65

Authors: Basil Nkosilathi Dube, Wilson R. Nyemba, Panashe Mandevu

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In highway driving, over 50 percent of the power produced by the engine is used to overcome aerodynamic drag, which is a force that opposes a body’s motion through the air. Aerodynamic drag and thus fuel consumption increase rapidly at speeds above 90kph. It is desirable to minimize fuel consumption. Aerodynamic drag reduction in highway driving is the best approach to minimize fuel consumption and to reduce the negative impacts of greenhouse gas emissions on the natural environment. Fuel economy is the ultimate concern of automotive development. This study aims to design and analyze drag-reducing devices for a Mazda T3500 truck, namely, the cab roof and rear (trailer tail) fairings. The aerodynamic effects of adding these append devices were subsequently investigated. To accomplish this, two 3D CAD models of the Mazda truck were designed using the Design Modeler. One, with these, append devices and the other without. The models were exported to ANSYS Fluent for computational fluid dynamics analysis, no wind tunnel tests were performed. A fine mesh with more than 10 million cells was applied in the discretization of the models. The realizable k-ε turbulence model with enhanced wall treatment was used to solve the Reynold’s Averaged Navier-Stokes (RANS) equation. In order to simulate the highway driving conditions, the tests were simulated with a speed of 100 km/h. The effects of these devices were also investigated for low-speed driving. The drag coefficients for both models were obtained from the numerical calculations. By adding the cab roof and rear (trailer tail) fairings, the simulations show a significant reduction in aerodynamic drag at a higher speed. The results show that the greatest drag reduction is obtained when both devices are used. Visuals from post-processing show that the rear fairing minimized the low-pressure region at the rear of the trailer when moving at highway speed. The rear fairing achieved this by streamlining the turbulent airflow, thereby delaying airflow separation. For lower speeds, there were no significant differences in drag coefficients for both models (original and modified). The results show that these devices can be adopted for improving the aerodynamic efficiency of the Mazda T3500 truck at highway speeds.

Keywords: aerodynamic drag, computation fluid dynamics, fluent, fuel consumption

Procedia PDF Downloads 143

Authors: Shoba Rathilal

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High unemployment rates and a shortage of experienced and qualified employees appear to be a paradox that currently plagues most countries worldwide. In a developing country like South Africa, the rate of unemployment is reported to be approximately 35%, the highest recorded globally. At the same time, a countrywide deficit in experienced and qualified potential employees is reported in South Africa, which is causing fierce rivalry among firms. Employers have reported that graduates are very rarely able to meet the demands of the job as there are gaps in their knowledge and conceptual understanding and other 21st-century competencies, attributes, and dispositions required to successfully negotiate the multiple responsibilities of employees in organizations. In addition, the rates of unemployment and suitability of graduates appear to be skewed by race and social class, the continued effects of a legacy of inequitable educational access. Higher Education in the current technologically advanced and dynamic world needs to serve as an agent of transformation, aspiring to develop graduates to be creative, flexible, critical, and with entrepreneurial acumen. This requires that higher education curricula and pedagogy require a re-envisioning of our selection, sequencing, and pacing of the learning, teaching, and assessment. At a particular Higher education Institution in South Africa, Design Thinking and Project Based learning are being adopted as two approaches that aim to enhance the student experience through the provision of a “distinctive education” that brings together disciplinary knowledge, professional engagement, technology, innovation, and entrepreneurship. Using these methodologies forces the students to solve real-time applied problems using various forms of knowledge and finding innovative solutions that can result in new products and services. The intention is to promote the development of skills for self-directed learning, facilitate the development of self-awareness, and contribute to students being active partners in the application and production of knowledge. These approaches emphasize active and collaborative learning, teamwork, conflict resolution, and problem-solving through effective integration of theory and practice. In principle, both these approaches are extremely impactful. However, at the institution in this study, the implementation of the PBL and DT was not as “smooth” as anticipated. This presentation reports on the analysis of the implementation of these two approaches within higher education curricula at a particular university in South Africa. The study adopts a qualitative case study design. Data were generated through the use of surveys, evaluation feedback at workshops, and content analysis of project reports. Data were analyzed using document analysis, content, and thematic analysis. Initial analysis shows that the forces constraining the implementation of PBL and DT range from the capacity to engage with DT and PBL, both from staff and students, educational contextual realities of higher education institutions, administrative processes, and resources. At the same time, the implementation of DT and PBL was enabled through the allocation of strategic funding and capacity development workshops. These factors, however, could not achieve maximum impact. In addition, the presentation will include recommendations on how DT and PBL could be adapted for differing contexts will be explored.

Keywords: design thinking, project based learning, innovative higher education pedagogy, student and staff capacity development

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis Pagone Emmanuele, Agbadede Roupa, Allison Isaiah

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Climate change represents one of the single most challenging problems facing the world today. According to the National Oceanic and Administrative Association, Atmospheric temperature rose almost 25% since 1958, Artic sea ice has shrunk 40% since 1959 and global sea levels have risen more than 5.5cm since 1990. Power plants are the major culprits of GHG emission to the atmosphere. Several technologies have been proposed to reduce the amount of GHG emitted to the atmosphere from power plant, one of which is the less researched Advanced zero-emission power plant. The advanced zero emission power plants make use of mixed conductive membrane (MCM) reactor also known as oxygen transfer membrane (OTM) for oxygen transfer. The MCM employs membrane separation process. The membrane separation process was first introduced in 1899 when Walter Hermann Nernst investigated electric current between metals and solutions. He found that when a dense ceramic is heated, the current of oxygen molecules move through it. In the bid to curb the amount of GHG emitted to the atmosphere, the membrane separation process was applied to the field of power engineering in the low carbon cycle known as the Advanced zero emission power plant (AZEP cycle). The AZEP cycle was originally invented by Norsk Hydro, Norway and ABB Alstom power (now known as Demag Delaval Industrial turbomachinery AB), Sweden. The AZEP drew a lot of attention because its ability to capture ~100% CO2 and also boasts of about 30-50% cost reduction compared to other carbon abatement technologies, the penalty in efficiency is also not as much as its counterparts and crowns it with almost zero NOx emissions due to very low nitrogen concentrations in the working fluid. The advanced zero emission power plants differ from a conventional gas turbine in the sense that its combustor is substituted with the mixed conductive membrane (MCM-reactor). The MCM-reactor is made up of the combustor, low-temperature heat exchanger LTHX (referred to by some authors as air preheater the mixed conductive membrane responsible for oxygen transfer and the high-temperature heat exchanger and in some layouts, the bleed gas heat exchanger. Air is taken in by the compressor and compressed to a temperature of about 723 Kelvin and pressure of 2 Mega-Pascals. The membrane area needed for oxygen transfer is reduced by increasing the temperature of 90% of the air using the LTHX; the temperature is also increased to facilitate oxygen transfer through the membrane. The air stream enters the LTHX through the transition duct leading to inlet of the LTHX. The temperature of the air stream is then increased to about 1150 K depending on the design point specification of the plant and the efficiency of the heat exchanging system. The amount of oxygen transported through the membrane is directly proportional to the temperature of air going through the membrane. The AZEP cycle was developed using the Fortran software and economic analysis was conducted using excel and Matlab followed by optimization case study. The Simple bleed gas heat exchange layout (100 % CO2 capture), Bleed gas heat exchanger layout with flue gas turbine (100 % CO2 capture), Pre-expansion reheating layout (Sequential burning layout)–AZEP 85% (85% CO2 capture) and Pre-expansion reheating layout (Sequential burning layout) with flue gas turbine–AZEP 85% (85% CO2 capture). This paper discusses monte carlo risk analysis of four possible layouts of the AZEP cycle.

Keywords: gas turbine, global warming, green house gas, fossil fuel power plants

Procedia PDF Downloads 397

Authors: Davood Vatankhah, Reza Mokhtari Malekabadi, Mohsen Saghaei

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Function of urban transportation systems depends on the existence of the required infrastructures, appropriate placement of different components, and the cooperation of these components with each other. Establishing various neighboring parking spaces in city neighborhood in order to prevent long-term and inappropriate parking of cars in the allies is one of the most effective operations in reducing the crowding and density of the neighborhoods. Every place with a certain application attracts a number of daily travels which happen throughout the city. A large percentage of the people visiting these places go to these travels by their own cars; therefore, they need a space to park their cars. The amount of this need depends on the usage function and travel demand of the place. The study aims at investigating the spatial distribution of the public parking spaces, determining the effective factors in locating, and their combination in GIS environment in Khomeinishahr of Isfahan city. Ultimately, the study intends to create an appropriate pattern for locating parking spaces, determining the request for parking spaces of the traffic areas, choosing the proper places for providing the required public parking spaces, and also proposing new spots in order to promote quality and quantity aspects of the city in terms of enjoying public parking spaces. Regarding the method, the study is based on applied purpose and regarding nature, it is analytic-descriptive. The population of the study includes people of the center of Khomeinishahr which is located on Northwest of Isfahan having about 5000 hectares of geographic area and the population of 241318 people are in the center of Komeinishahr. In order to determine the sample size, Cochran formula was used and according to the population of 26483 people of the studied area, 231 questionnaires were used. Data analysis was carried out by usage of SPSS software and after estimating the required space for parking spaces, initially, the effective criteria in locating the public parking spaces are weighted by the usage of Analytic Hierarchical Process in the Arc GIS software. Then, appropriate places for establishing parking spaces were determined by fuzzy method of Order Weighted Average (OWA). The results indicated that locating of parking spaces in Khomeinishahr have not been carried out appropriately and per capita of the parking spaces is not desirable in relation to the population and request; therefore, in addition to the present parking lots, 1434 parking lots are needed in the area of the study for each day; therefore, there is not a logical proportion between parking request and the number of parking lots in Khomeinishahr.

Keywords: GIS, locating, parking, khomeinishahr

Procedia PDF Downloads 312

Authors: Laura Pytel, Georg Baumann, Gregor Gstrein, Corina Klug

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Premium cars often coat the instrument panels with a bilaminate consisting of a soft foam and a plastic skin. The coating is torn open during the passenger airbag deployment under high strain rates. Characterizing and simulating the top coat layer is crucial for predicting the attenuation that delays the airbag deployment, effecting the design of the restrain system and to reduce the demand of simulation adjustments through expensive physical component testing.Up to now, bilaminates used within cars either have been modelled by using a two-dimensional shell formulation for the whole coating system as one which misses out the interaction of the two layers or by combining a three-dimensional formulation foam layer with a two-dimensional skin layer but omitting the foam in the significant parts like the expected tear line area and the hinge where high compression is expected. In both cases, the properties of the coating causing the attenuation are not considered. Further, at present, the availability of material information, as there are failure dependencies of the two layers, as well as the strain rate of up to 200 1/s, are insufficient. The velocity of the passenger airbag flap during an airbag shot has been measured with about 11.5 m/s during first ripping; the digital image correlation evaluation showed resulting strain rates of above 1500 1/s. This paper provides a high strain rate material characterization of a bilaminate consisting of a thin polypropylene foam and a thermoplasctic olefins (TPO) skin and the creation of validated material models. With the help of a Split Hopkinson tension bar, strain rates of 1500 1/s were within reach. The experimental data was used to calibrate and validate a more physical modelling approach of the forced ripping of the bilaminate. In the presented model, the three-dimensional foam layer is continuously tied to the two-dimensional skin layer, allowing failure in both layers at any possible position. The simulation results show a higher agreement in terms of the trajectory of the flaps and its velocity during ripping. The resulting attenuation of the airbag deployment measured by the contact force between airbag and flaps increases and serves usable data for dimensioning modules of an airbag system.

Keywords: bilaminate ripping behavior, High strain rate material characterization and modelling, induced material failure, TPO and foam

Procedia PDF Downloads 73

Authors: Alvaro Figueira, Bruno Cabral

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Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

Procedia PDF Downloads 127

Authors: Franco Primo Soffietti, Diego Fernando Turello, Federico Pinto

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Shear waves that propagate through the ground impose deformations that must be taken into account in the design and assessment of buried longitudinal structures such as tunnels, pipelines, and piles. Conventional engineering approaches for seismic evaluation often rely on a Euler-Bernoulli beam models supported by a Winkler foundation. This approach, however, falls short in capturing the distortions induced when the structure is subjected to shear waves. To overcome these limitations, in the present work an analytical solution is proposed considering a Timoshenko beam and including transverse and rotational springs. The present research proposes ground springs derived as closed-form analytical solutions of the equations of elasticity including the seismic wavelength. These proposed springs extend the applicability of previous plane-strain models. By considering variations in displacements along the longitudinal direction, the presented approach ensures the springs do not approach zero at low frequencies. This characteristic makes them suitable for assessing pseudo-static cases, which typically govern structural forces in kinematic interaction analyses. The results obtained, validated against existing literature and a 3D Finite Element model, reveal several key insights: i) the cutoff frequency significantly influences transverse and rotational springs; ii) neglecting displacement variations along the structure axis (i.e., assuming plane-strain deformation) results in unrealistically low transverse springs, particularly for wavelengths shorter than the structure length; iii) disregarding lateral displacement components in rotational springs and neglecting variations along the structure axis leads to inaccurately low spring values, misrepresenting interaction phenomena; iv) transverse springs exhibit a notable drop in resonance frequency, followed by increasing damping as frequency rises; v) rotational springs show minor frequency-dependent variations, with radiation damping occurring beyond resonance frequencies, starting from negative values. This comprehensive analysis sheds light on the complex behavior of embedded longitudinal structures when subjected to shear waves and provides valuable insights for the seismic assessment.

Keywords: shear waves, Timoshenko beams, Winkler springs, sol-structure interaction

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Authors: Saveria Capecchi

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In this paper, the results of qualitative content research on postfeminist female representation in contemporary Western media (advertising, television series, films, social media) are presented. Female role models spectacularized in media culture are an important part of the development of social identities and could inspire new generations. Postfeminist cultural texts have given rise to heated debate between gender and media studies scholars. There are those who claim they are commercial products seeking to sell feminism to women, a feminism whose political and subversive role is completely distorted and linked to the commercial interests of the cosmetics, fashion, fitness and cosmetic surgery industries, in which women’s ‘power’ lies mainly in their power to seduce. There are those who consider them feminist manifestos because they represent independent ‘modern women’ free from male control who aspire to achieve professionally and overcome gender stereotypes like that of the ‘housewife-mother’. Major findings of the research show that feminist principles have been gradually absorbed by the cultural industry and adapted to its commercial needs, resulting in the dissemination of contradictory values. On the one hand, in line with feminist arguments, patriarchal ideology is condemned and the concepts of equality and equal opportunity between men and women are promoted. On the other hand, feminist principles and demands are ascribed to individualism, which translates into the slogan: women are free to decide for themselves, even to objectify their own bodies. In particular, it is observed that femvertising trend in media industry is changing female representation moving away from classic stereotypes: the feminine beauty ideal of slenderness, emphasized in the media since the seventies, is ultimately challenged by the ‘curvy’ body model, which is considered to be more inclusive and based on the concept of ‘natural beauty’. Another aspect of change is the ‘anti-romantic’ revolution performed by some heroines, who are not in search of Prince Charming, in television drama and in the film industry. In conclusion, although femvertising tends to simplify and trivialize the concepts characterizing fourth-wave feminism (‘intersectionality’ and ‘inclusion’), it is also a tendency that enables the challenging of media imagery largely based on male viewpoints, interests and desires.

Keywords: feminine beauty ideal, femvertising, gender and media, postfeminism

Procedia PDF Downloads 155

Authors: Xu Feifei

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The prevalence of COVID-19 currently is the biggest challenge to improving people's quality of life. Its impact on the health-related quality of life (HRQoL) is highly uncertain and has not been summarized so far. The aim of the present systematic review was to assess and provide an up-to-date analysis of the impact of the COVID-19 pandemic on the HRQoL of participants who have been infected, have not been infected but isolated, frontline, with different diseases, and the general population. Therefore, an electronic search of the literature in PubMed databases was performed from 2019 to July 2022 (without date restriction). PRISMA guideline methodology was employed, and data regarding the HRQoL were extracted from eligible studies. Articles were included if they met the following inclusion criteria: (a) reports on the data collection of the health state utility values (HSUVs) related to COVID-19 from 2019 to 2021; (b) English language and peer-reviewed journals; and (c) original HSUV data; (d) using EQ-5D tool to quantify the HRQoL. To identify studies that reported the effects on COVID-19, data on the proportion of overall HSUVs of participants who had the outcome were collected and analyzed using a one-group meta-analysis. As a result, thirty-two studies fulfilled the inclusion criteria and, therefore, were included in the systematic review. A total of 45295 participants and provided 219 means of HSUVs during COVID-19 were included in this systematic review. The range of utility is from 0.224 to 1. The study included participants from Europe (n=16), North America (n=4), Asia (n=10), South America (n=1), and Africa (n=1). Twelve articles showed that the HRQoL of the participants who have been infected with COVID-19 (range of overall HSUVs from 0.6125 to 0.863). Two studies reported the population of frontline workers (the range of overall HSUVs from 0.82 to 0.93). Seven of the articles researched the participants who had not been infected with COVID-19 but suffered from morbidities during the pandemic (range of overall HSUVs from 0.5 to 0.96). Thirteen studies showed that the HRQoL of the respondents who have not been infected with COVID-19 and without any morbidities (range of overall HSUVs from 0.64 to 0.964). Moreover, eighteen articles reported the outcomes of overall HSUVs during the COVID-19 pandemic in different population groups. The estimate of overall HSUVs of direct COVID-19 experience population (n=1333) was 0.751 (95% CI 0.670 - 0.832, I2 = 98.64%); the estimate of frontline population (n=610) was 0.906 ((95% CI 0.854 – 0.957, I2 = 98.61%); participants with different disease (n=132) were 0.768 (95% CI 0.515 - 1.021, I2= 99.26%); general population without infection history (n=29,892) was 0.825 (95% CI 0.766 - 0.885, I2 =99.69%). Conclusively, taking into account these results, this systematic review might confirm that COVID-19 has a negative impact on the HRQoL of the infected population and illness population. It provides practical value for cost-effectiveness model analysis of health states related to COVID-19.

Keywords: COVID-19, health-related quality of life, meta-analysis, systematic review, utility value

Procedia PDF Downloads 86

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 45

Authors: Joanne DiFrancisco-Donoghue, Seth E. Jenny, Peter C. Douris, Sophia Ahmad, Kyle Yuen, Hillary Gan, Kenney Abraham, Amber Sousa

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Background: Guidelines for the maintenance of health of esports players and the cognitive changes that accompany competitive gaming are understudied. Executive functioning is an important cognitive skill for an esports player. The relationship between executive functions and physical exercise has been well established. However, the effects of prolonged sitting regardless of physical activity level have not been established. Prolonged uninterrupted sitting reduces cerebral blood flow. Reduced cerebral blood flow is associated with lower cognitive function and fatigue. This decrease in cerebral blood flow has been shown to be offset by frequent and short walking breaks. These short breaks can be as little as 2 minutes at the 30-minute mark and 6 minutes following 60 minutes of prolonged sitting. The rationale is the increase in blood flow and the positive effects this has on metabolic responses. The primary purpose of this study was to evaluate executive function changes following 6-minute bouts of walking and complete rest mid-session, compared to no break, during prolonged gameplay in competitive first-person shooter (FPS) esports players. Methods: This study was conducted virtually due to the Covid-19 pandemic and was approved by the New York Institute of Technology IRB. Twelve competitive FPS participants signed written consent to participate in this randomized pilot study. All participants held a gold ranking or higher. Participants were asked to play for 2 hours on three separate days. Outcome measures to test executive function included the Color Stroop and the Tower of London tests which were administered online each day prior to gaming and at the completion of gaming. All participants completed the tests prior to testing for familiarization. One day of testing consisted of a 6-minute walk break after 60-75 minutes of play. The Rate of Perceived Exertion (RPE) was recorded. The participant continued to play for another 60-75 minutes and completed the tests again. Another day the participants repeated the same methods replacing the 6-minute walk with lying down and resting for 6 minutes. On the last day, the participant played continuously with no break for 2 hours and repeated the outcome tests pre and post-play. A Latin square was used to randomize the treatment order. Results: Using descriptive statistics, the largest change in mean reaction time incorrect congruent pre to post play was seen following the 6-minute walk (662.0 (609.6) ms pre to 602.8 (539.2) ms post), followed by the 6-minute rest group (681.7(618.1) ms pre to 666.3 (607.9) ms post), and with minimal change in the continuous group (594.0(534.1) ms pre to 589.6(552.9) ms post). The mean solution time was fastest in the resting condition (7774.6(6302.8)ms), followed by the walk condition (7929.4 (5992.8)ms), with the continuous condition being slowest (9337.3(7228.7)ms). the continuous group 9337.3(7228.7) ms; 7929.4 (5992.8 ) ms 774.6(6302.8) ms. Conclusion: Short walking breaks improve blood flow and reduce the risk of venous thromboembolism during prolonged sitting. This pilot study demonstrated that a low intensity 6 -minute walk break, following 60 minutes of play, may also improve executive function in FPS gamers.

Keywords: executive function, FPS, physical activity, prolonged sitting

Procedia PDF Downloads 231

Authors: Piotr J. Skusiewicz, Johnathan Saul, Ijhar Rusli, Svetlana Aleksandrova, Stephen. F. Benjamin, Miroslaw Gall, Steve Pierson, Carol A. Roberts

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The application of turbocharging in automotive engines leads to swirling flow entering the catalyst. The behaviour of this type of flow within the catalyst has yet to be adequately documented. This work discusses the effect of swirling flow on the flow distribution in automotive exhaust catalysts. Compressed air supplied to a moving-block swirl generator allowed for swirling flow with variable intensities to be generated. Swirl intensities were measured at the swirl generator outlet using single-sensor hot-wire probes. The swirling flow was fed into diffusers with total angles of 10°, 30° and 180°. Downstream of the diffusers, a wash-coated diesel oxidation catalyst (DOC) of length 143.8 mm, diameter 76.2 mm and nominal cell density of 400 cpsi was fitted. Velocity profiles were measured at the outlet sleeve about 30 mm downstream of the monolith outlet using single-sensor hot-wire probes. Wall static pressure was recorded using a multi-tube manometer connected to pressure taps positioned along the diffuser walls. The results show that as swirl is increased, more of the flow is directed towards the diffuser walls. The velocity decreases around the centre-line and maximum velocities are observed close to the outer radius of the monolith for all flow rates. At the maximum swirl intensity, reversed flow was recorded near the centre of the monolith. Wall static pressure measurements in the 180° diffuser indicated no pressure recovery as the flow enters the diffuser. This is indicative of flow separation at the inlet to the diffuser. To gain insight into the flow structure, CFD simulations have been performed for the 180° diffuser for a flow rate of 63 g/s. The geometry of the model consists of the complete assembly from the upstream swirl generator to the outlet sleeve. Modelling of the flow in the monolith was achieved using the porous medium approach, where the monolith with parallel flow channels is modelled as a porous medium that resists the flow. A reasonably good agreement was achieved between the experimental and CFD results downstream of the monolith. The CFD simulations allowed visualisation of the separation zones and central toroidal recirculation zones that occur within the expansion region at certain swirl intensities which are highlighted.

Keywords: catalyst, computational fluid dynamics, diffuser, hot-wire anemometry, swirling flow

Procedia PDF Downloads 304

Authors: Robert Miller, Fred Soltero, Sandra Allan, Denise Bonilla

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The Tropical Bont Tick (TBT), Amblyomma variegatum, was introduced to the Caribbean in the mid-1700s. Since it has spread throughout the Caribbean dispersed by cattle egrets (Bubulcus ibis). Tropical Bont Ticks vector many pathogens to livestock and humans. However, only the livestock diseases heartwater, Ehrlichia (Cowdria) ruminantium, and dermatophilosis, Dermatophilus congolensis, are associated with TBT in the Caribbean. African tick bite fever (Rickettsia africae) is widespread in Caribbean TBT but human cases are rare. The Caribbean Amblyomma Programme (CAP) was an effort led by the Food and Agricultural Organization to eradicate TBTs from participating islands. This 10-year effort successfully eradicated TBT from many islands. However, most are reinfested since its termination. Pheromone technology has been developed to aid in TBT control. Although not part of the CAP treatment scheme, this research established that pheromones in combination with pesticide greatly improves treatment efficiencies. Additionally, pheromone combined with CO₂ traps greatly improves active surveillance success. St. Croix has a history of TBT outbreaks. Passive surveillance detected outbreaks in 2016 and in May of 2021. Surveillance efforts are underway to determine the extent of TBT on St Croix. Puerto Rico is the next island in the archipelago and is at a greater risk of re-infestation due to active outbreaks in St Croix. Tropical Bont Ticks were last detected in Puerto Rico in the 1980s. The infestation started on the small Puerto Rican island of Vieques, the closest landmass to St Croix, and spread to the main island through cattle movements. This infestation was eradicated with the help of the Tropical Cattle Tick (TCT), Rhipicephalus (Boophilus) microplus, eradication program. At the time, large percentages of Puerto Rican cattle were treated for ticks along with the necessary material and manpower mobilized for the effort. Therefore, a shift of focus from the TCT to TBT prevented its establishment in Puerto Rico. Currently, no large-scale treatment of TCTs occurs in Puerto Rico. Therefore, the risk of TBT establishment is now greater than it was in the 1980s. From Puerto Rico, the risk of TBT movement to the American continent increases significantly. The establishment of TBTs in the Americas would cause $1.2 billion USD in losses to the livestock industry per year. The USDA Agricultural Research Service recently worked with the USDA Animal Health Inspection Service and the Puerto Rican Department of Agriculture to modernize the management of the TCT. This modernized program uses safer pesticides and has successfully been used to eradicate pesticide-susceptible and -resistant ticks throughout the island. The objective of this work is to prevent the infestation of Puerto Rico by TBTs by combining the current TCT management efforts with TBT surveillance in Vieques. The combined effort is designed to eradicate TCT from Vieques while using the treated cattle as trap animals for TBT using pheromone impregnated tail tags attached to treated animals. Additionally, active surveillance using CO₂-baited traps combined with pheromone will be used to actively survey the environment for free-living TBT. Knowledge gained will inform TBT control efforts in St. Croix.

Keywords: Amblyomma variegatum, caribbean, eradication, Rhipicephalus (boophilus) microplus, pheromone

Procedia PDF Downloads 181

Authors: Sylvie Michel, Sylvie Gerbaix, Marc Bidan

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Artificial intelligence (AI) can be held accountable for its detrimental impacts. This question gains heightened relevance given AI's pervasive reach across various domains, magnifying its power and potential. The expanding influence of AI raises fundamental ethical inquiries, primarily centering on biases, responsibility, and transparency. This encompasses discriminatory biases arising from algorithmic criteria or data, accidents attributed to autonomous vehicles or other systems, and the imperative of transparent decision-making. This article aims to stimulate reflection on AI accountability, denoting the necessity to elucidate the effects it generates. Accountability comprises two integral aspects: adherence to legal and ethical standards and the imperative to elucidate the underlying operational rationale. The objective is to initiate a reflection on the obstacles to this "accountability," facing the challenges of the complexity of artificial intelligence's system and its effects. Then, this article proposes to mobilize Edgar Morin's complex thought to encompass and face the challenges of this complexity. The first contribution is to point out the challenges posed by the complexity of A.I., with fractional accountability between a myriad of human and non-human actors, such as software and equipment, which ultimately contribute to the decisions taken and are multiplied in the case of AI. Accountability faces three challenges resulting from the complexity of the ethical issues combined with the complexity of AI. The challenge of the non-neutrality of algorithmic systems as fully ethically non-neutral actors is put forward by a revealing ethics approach that calls for assigning responsibilities to these systems. The challenge of the dilution of responsibility is induced by the multiplicity and distancing between the actors. Thus, a dilution of responsibility is induced by a split in decision-making between developers, who feel they fulfill their duty by strictly respecting the requests they receive, and management, which does not consider itself responsible for technology-related flaws. Accountability is confronted with the challenge of transparency of complex and scalable algorithmic systems, non-human actors self-learning via big data. A second contribution involves leveraging E. Morin's principles, providing a framework to grasp the multifaceted ethical dilemmas and subsequently paving the way for establishing accountability in AI. When addressing the ethical challenge of biases, the "hologrammatic" principle underscores the imperative of acknowledging the non-ethical neutrality of algorithmic systems inherently imbued with the values and biases of their creators and society. The "dialogic" principle advocates for the responsible consideration of ethical dilemmas, encouraging the integration of complementary and contradictory elements in solutions from the very inception of the design phase. Aligning with the principle of organizing recursiveness, akin to the "transparency" of the system, it promotes a systemic analysis to account for the induced effects and guides the incorporation of modifications into the system to rectify deviations and reintroduce modifications into the system to rectify its drifts. In conclusion, this contribution serves as an inception for contemplating the accountability of "artificial intelligence" systems despite the evident ethical implications and potential deviations. Edgar Morin's principles, providing a lens to contemplate this complexity, offer valuable perspectives to address these challenges concerning accountability.

Keywords: accountability, artificial intelligence, complexity, ethics, explainability, transparency, Edgar Morin

Procedia PDF Downloads 67

Authors: Marita Heyns

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Job insecurity is a distressing phenomenon which has far reaching consequences for both employees and their organizations. Previously, much attention has been given to the link between job insecurity and individual level performance outcomes, while less is known about how subjectively perceived job insecurity might transfer beyond the individual level to affect performance of the organization on an aggregated level. Research focusing on how employees’ fear of job loss might affect the organization’s ability to respond proactively to volatility and drastic change through applying its capabilities of sensing, seizing, and reconfiguring, appears to be practically non-existent. Equally little is known about the potential underlying mechanisms through which job insecurity might affect the dynamic capabilities of an organization. This study examines how job insecurity might affect dynamic organizational capability through trust as an underlying process. More specifically, it considered the simultaneous roles of trust at an impersonal (organizational) level as well as trust at an interpersonal level (in leaders and co-workers) as potential underlying mechanisms through which job insecurity might affect the organization’s dynamic capability to respond to opportunities and imminent, drastic change. A quantitative research approach and a stratified random sampling technique enabled the collection of data among 314 managers at four different plant sites of a large South African steel manufacturing organization undergoing dramatic changes. To assess the study hypotheses, the following statistical procedures were employed: Structural equation modelling was performed in Mplus to evaluate the measurement and structural models. The Chi-square values test for absolute fit as well as alternative fit indexes such as the Comparative Fit Index and the Tucker-Lewis Index, the Root Mean Square Error of Approximation and the Standardized Root Mean Square Residual were used as indicators of model fit. Composite reliabilities were calculated to evaluate the reliability of the factors. Finally, interaction effects were tested by using PROCESS and the construction of two-sided 95% confidence intervals. The findings indicate that job insecurity had a lower-than-expected detrimental effect on evaluations of the organization’s dynamic capability through the conducive buffering effects of trust in the organization and in its leaders respectively. In contrast, trust in colleagues did not seem to have any noticeable facilitative effect. The study proposes that both job insecurity and dynamic capability can be managed more effectively by also paying attention to factors that could promote trust in the organization and its leaders; some practical recommendations are given in this regard

Keywords: dynamic organizational capability, impersonal trust, interpersonal trust, job insecurity

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Authors: Mariamawit T. Belete

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Cereal production and marketing are the means of livelihood for millions of households in Ethiopia. However, cereal production is constrained by technical and socio-economic factors. Among the technical factors, cereal crop diseases are the major contributing factors to the low yield. The aim of this research is to develop an integration of data mining and knowledge based system for sorghum anthracnose disease diagnosis that assists agriculture experts and development agents to make timely decisions. Anthracnose diagnosing systems gather information from Melkassa agricultural research center and attempt to score anthracnose severity scale. Empirical research is designed for data exploration, modeling, and confirmatory procedures for testing hypothesis and prediction to draw a sound conclusion. WEKA (Waikato Environment for Knowledge Analysis) was employed for the modeling. Knowledge based system has come across a variety of approaches based on the knowledge representation method; case-based reasoning (CBR) is one of the popular approaches used in knowledge-based system. CBR is a problem solving strategy that uses previous cases to solve new problems. The system utilizes hidden knowledge extracted by employing clustering algorithms, specifically K-means clustering from sampled anthracnose dataset. Clustered cases with centroid value are mapped to jCOLIBRI, and then the integrator application is created using NetBeans with JDK 8.0.2. The important part of a case based reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution, and retain to store the confirmed solution to the case base for future use. Evaluation of the system was done for both system performance and user acceptance. For testing the prototype, seven test cases were used. Experimental result shows that the system achieves an average precision and recall values of 70% and 83%, respectively. User acceptance testing also performed by involving five domain experts, and an average of 83% acceptance is achieved. Although the result of this study is promising, however, further study should be done an investigation on hybrid approach such as rule based reasoning, and pictorial retrieval process are recommended.

Keywords: sorghum anthracnose, data mining, case based reasoning, integration

Procedia PDF Downloads 85

Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

Procedia PDF Downloads 67

Authors: Satya Narayan

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India, a rapidly growing economy with a burgeoning population, grapples with the dual challenge of meeting rising energy demands and reducing its carbon footprint. Geothermal energy, an often overlooked and underutilized renewable source, holds immense potential for addressing this challenge. Geothermal resources offer a valuable, consistent, and sustainable energy source, and may significantly contribute to India's energy. This paper discusses the importance of geothermal exploration in India, emphasizing its role in achieving sustainable energy production while mitigating environmental impacts. It also delves into the methodology employed to assess geothermal resource feasibility, including geophysical surveys and borehole drilling. The results and discussion sections highlight promising geothermal sites across India, illuminating the nation's vast geothermal potential. It detects potential geothermal reservoirs, characterizes subsurface structures, maps temperature gradients, monitors fluid flow, and estimates key reservoir parameters. Globally, geothermal energy falls into high and low enthalpy categories, with India mainly having low enthalpy resources, especially in hot springs. The northwestern Himalayan region boasts high-temperature geothermal resources due to geological factors. Promising sites, like Puga Valley, Chhumthang, and others, feature hot springs suitable for various applications. The Son-Narmada-Tapti lineament intersects regions rich in geological history, contributing to geothermal resources. Southern India, including the Godavari Valley, has thermal springs suitable for power generation. The Andaman-Nicobar region, linked to subduction and volcanic activity, holds high-temperature geothermal potential. Geophysical surveys, utilizing gravity, magnetic, seismic, magnetotelluric, and electrical resistivity techniques, offer vital information on subsurface conditions essential for detecting, evaluating, and exploiting geothermal resources. The gravity and magnetic methods map the depth of the mantle boundary (high-temperature) and later accurately determine the Curie depth. Electrical methods indicate the presence of subsurface fluids. Seismic surveys create detailed sub-surface images, revealing faults and fractures and establishing possible connections to aquifers. Borehole drilling is crucial for assessing geothermal parameters at different depths. Detailed geochemical analysis and geophysical surveys in Dholera, Gujarat, reveal untapped geothermal potential in India, aligning with renewable energy goals. In conclusion, geophysical surveys and borehole drilling play a pivotal role in economically viable geothermal site selection and feasibility assessments. With ongoing exploration and innovative technology, these surveys effectively minimize drilling risks, optimize borehole placement, aid in environmental impact evaluations, and facilitate remote resource exploration. Their cost-effectiveness informs decisions regarding geothermal resource location and extent, ultimately promoting sustainable energy and reducing India's reliance on conventional fossil fuels.

Keywords: geothermal resources, geophysical methods, exploration, exploitation

Procedia PDF Downloads 92

Authors: Payling Harn

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LEGO® SERIOUS PLAY® is a kind of facilitated workshop of thinking and problem-solving approach. Participants built symbolic and metaphorical brick models in response to tasks given by the facilitator and presented these models to other participants. LEGO® SERIOUS PLAY® applied the positive psychological mechanism of Flow and positive emotions to help participants perceiving self-experience and unknown fact and increasing the happiness of life by building bricks and narrating story. At present, LEGO® SERIOUS PLAY® is often utilized for facilitating professional identity and strategy development to assist workers in career development. The researcher desires to apply LEGO® SERIOUS PLAY® to the career courses of college students in order to promote their career ability. This study aimed to use the facilitative method of LEGO® SERIOUS PLAY® to develop the career courses of college students, then explore the effects of Taiwanese college students' positive and negative emotions, career adaptabilities, and career sense of hope by LEGO® SERIOUS PLAY® career courses. The researcher regarded strength as the core concept and use the facilitative mode of LEGO® SERIOUS PLAY® to develop the 8 weeks’ career courses, which including ‘emotion of college life’ ‘career highlights’, ‘career strengths’, ‘professional identity’, ‘business model’, ‘career coping’, ‘strength guiding principles’, ‘career visions’,’ career hope’, etc. The researcher will adopt problem-oriented teaching method to give tasks which according to the weekly theme, use the facilitative mode of LEGO® SERIOUS PLAY® to guide participants to respond tasks by building bricks. Then participants will conduct group discussions, reports, and writing reflection journals weekly. Participants will be 24 second-grade college students. They will attend LEGO® SERIOUS PLAY® career courses for 2 hours a week. The researcher used’ ‘Career Adaptability Scale’ and ‘Career Hope Scale’ to conduct pre-test and post-test. The time points of implementation testing will be one week before courses starting, one day after courses ending respectively. Then the researcher will adopt repeated measures one-way ANOVA for analyzing data. The results revealed that the participants significantly presented immediate positive effect in career adaptability and career hope. The researcher hopes to construct the mode of LEGO® SERIOUS PLAY® career courses by this study and to make a substantial contribution to the future career teaching and researches of LEGO® SERIOUS PLAY®.

Keywords: LEGO® SERIOUS PLAY®, career courses, strength, positive and negative affect, career hope

Procedia PDF Downloads 255

Authors: Fagner O. Serrano, Danielle R. Bouchard, Todd A. Duhame

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Walking cadence (steps/min) is an effective way to prescribe exercise so an individual can reach a moderate intensity, which is recommended to optimize health benefits. To our knowledge, there is no study on the required walking cadence to reach a moderate intensity for people that present chronic conditions or risk factors for chronic conditions such as Cardiovascular Diseases (CVD). The objectives of this study were: 1- to identify the walking cadence needed for people at risk of CVD to a reach moderate intensity, and 2- to develop and test an equation using clinical variables to help professionals working with individuals at risk of CVD to estimate the walking cadence needed to reach moderate intensity. Ninety-one people presenting a minimum of two risk factors for CVD completed a medically supervised graded exercise test to assess maximum oxygen consumption at the first visit. The last visit consisted of recording walking cadence using a foot pod Garmin FR-60 and a Polar heart rate monitor, aiming to get participants to reach 40% of their maximal oxygen consumption using a portable metabolic cart on an indoor flat surface. The equation to predict the walking cadence needed to reach moderate intensity in this sample was developed as follows: The sample was randomly split in half and the equation was developed with one half of the participants, and validated using the other half. Body mass index, height, stride length, leg height, body weight, fitness level (VO2max), and self-selected cadence (over 200 meters) were measured using objective measured. Mean walking cadence to reach moderate intensity for people age 64.3 ± 10.3 years old at risk of CVD was 115.8  10.3 steps per minute. Body mass index, height, body weight, fitness level, and self-selected cadence were associated with walking cadence at moderate intensity when evaluated in bivariate analyses (r ranging from 0.22 to 0.52; all P values ≤0.05). Using linear regression analysis including all clinical variables associated in the bivariate analyses, body weight was the significant predictor of walking cadence for reaching a moderate intensity (ß=0.24; P=.018) explaining 13% of walking cadence to reach moderate intensity. The regression model created was Y = 134.4-0.24 X body weight (kg).Our findings suggest that people presenting two or more risk factors for CVD are reaching moderate intensity while walking at a cadence above the one officially recommended (116 steps per minute vs. 100 steps per minute) for healthy adults.

Keywords: cardiovascular disease, moderate intensity, older adults, walking cadence

Procedia PDF Downloads 444

Authors: O. E. Akinyemi, P. O. Alege, O. O. Ajayi, L. A. Amaghionyediwe, A. A. Ogundipe

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Nigeria as an oil-producing developing country in Africa is one of the many countries that had been subsidizing consumption of fossil fuel. Despite the numerous advantage of this policy ranging from increased energy access, fostering economic and industrial development, protecting the poor households from oil price shocks, political considerations, among others; they have been found to impose economic cost, wasteful, inefficient, create price distortions discourage investment in the energy sector and contribute to environmental pollution. These negative consequences coupled with the fact that the policy had not been very successful at achieving some of its stated objectives, led to a number of organisations and countries such as the Group of 7 (G7), World Bank, International Monetary Fund (IMF), International Energy Agency (IEA), Organisation for Economic Co-operation and Development (OECD), among others call for global effort towards reforming fossil fuel subsidies. This call became necessary in view of seeking ways to harmonise certain existing policies which may by design hamper current effort at tackling environmental concerns such as climate change. This is in addition to driving a green growth strategy and low carbon development in achieving sustainable development. The energy sector is identified to play a vital role. This study thus investigates the prospects of using fuel subsidy reform as a viable tool in driving an economy that de-emphasizes carbon growth in Nigeria. The method used is the Johansen and Engle-Granger two-step Co-integration procedure in order to investigate the existence or otherwise of a long-run equilibrium relationship for the period 1971 to 2011. Its theoretical framework is rooted in the Environmental Kuznet Curve (EKC) hypothesis. In developing three case scenarios (case of subsidy payment, no subsidy payment and effective subsidy), findings from the study supported evidence of a long run sustainable equilibrium model. Also, estimation results reflected that the first and the second scenario do not significantly influence the indicator of environmental quality. The implication of this is that in reforming fuel subsidy to drive environmental quality for an economy like Nigeria, strong and effective regulatory framework (measure that was interacted with fuel subsidy to yield effective subsidy) is essential.

Keywords: environmental quality, fuel subsidy, green growth, low carbon growth strategy

Procedia PDF Downloads 331

Authors: Lenka Krsnakova, Petr Jirsak

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Prior research on facility location in supply chain is mostly focused on improvement of mathematical models. It is due to the fact that supply chain design has been for the long time the area of operational research that underscores mainly quantitative criteria. Qualitative criteria are still highly neglected within the supply chain design research. Facility location in the supply chain has become multi-criteria decision-making problem rather than single criteria decision due to changes of market conditions. Thus, both qualitative and quantitative criteria have to be included in the decision making process. The aim of this study is to emphasize the importance of qualitative criteria as key parameters of relevant mathematical models. We examine which criteria are taken into consideration when Czech companies decide about their facility location. A literature review on criteria being used in facility location decision making process creates a theoretical background for the study. The data collection was conducted through questionnaire survey. Questionnaire was sent to manufacturing and business companies of all sizes (small, medium and large enterprises) with the representation in the Czech Republic within following sectors: automotive, toys, clothing industry, electronics and pharmaceutical industry. Comparison of which criteria prevail in the current research and which are considered important by companies in the Czech Republic is made. Despite the number of articles focused on supply chain design, only minority of them consider qualitative criteria and rarely process supply chain design as a multi-criteria decision making problem. Preliminary results of the questionnaire survey outlines that companies in the Czech Republic see the qualitative criteria and their impact on facility location decision as crucial. Qualitative criteria as company strategy, quality of working environment or future development expectations are confirmed to be considered by Czech companies. This study confirms that the qualitative criteria can significantly influence whether a particular location could or could not be right place for a logistic facility. The research has two major limitations: researchers who focus on improving of mathematical models mostly do not mention criteria that enter the model. Czech supply chain managers selected important criteria from the group of 18 available criteria and assign them importance weights. It does not necessarily mean that these criteria were taken into consideration when the last facility location was chosen, but how they perceive that today. Since the study confirmed the necessity of future research on how qualitative criteria influence decision making process about facility location, the authors have already started in-depth interviews with participating companies to reveal how the inclusion of qualitative criteria into decision making process about facility location influence the company´s performance.

Keywords: criteria influencing facility location, Czech Republic, facility location decision-making, qualitative criteria

Procedia PDF Downloads 329

Authors: Diana Islam, Juan Fang, Vito Fanelli, Bing Han, Julie Khang, Jianfeng Wu, Arthur S. Slutsky, Haibo Zhang

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Introduction: MSC delivery in preclinical models of ARDS has demonstrated significant improvements in lung function and recovery from acute injury. However, the role of MSC delivery in ARDS associated pulmonary fibrosis is not well understood. Some animal studies using bleomycin, asbestos, and silica-induced pulmonary fibrosis show that MSC delivery can suppress fibrosis. While other animal studies using radiation induced pulmonary fibrosis, liver, and kidney fibrosis models show that MSC delivery can contribute to fibrosis. Hypothesis: The beneficial and deleterious effects of MSC in ARDS are modulated by the lung microenvironment at the time of MSC delivery. Methods: To induce ARDS a two-hit mouse model of Hydrochloric acid (HCl) aspiration (day 0) and mechanical ventilation (MV) (day 2) was used. HCl and injurious MV generated fibrosis within 14-28 days. 0.5x106 mouse MSCs were delivered (via both intratracheal and intravenous routes) either in the active inflammatory phase (day 2) or during the remodeling phase (day 14) of ARDS (mouse fibroblasts or PBS used as a control). Lung injury accessed using inflammation score and elastance measurement. Pulmonary fibrosis was accessed using histological score, tissue collagen level, and collagen expression. In addition alveolar epithelial (E) and mesenchymal (M) marker expression profile was also measured. All measurements were taken at day 2, 14, and 28. Results: MSC delivery 2 days after HCl exacerbated lung injury and fibrosis compared to HCl alone, while the day 14 delivery showed protective effects. However in the absence of HCl, MSC significantly reduced the injurious MV-induced fibrosis. HCl injury suppressed E markers and up-regulated M markers. MSC delivery 2 days after HCl further amplified M marker expression, indicating their role in myofibroblast proliferation/activation. While with 14-day delivery E marker up-regulation was observed indicating their role in epithelial restoration. Conclusions: Early MSC delivery can be protective of injurious MV. Late MSC delivery during repair phase may also aid in recovery. However, early MSC delivery during the exudative inflammatory phase of HCl-induced ARDS can result in pro-fibrotic profiles. It is critical to understand the interaction between MSC and the lung microenvironment before MSC-based therapies are utilized for ARDS.

Keywords: acute respiratory distress syndrome (ARDS), mesenchymal stem cells (MSC), hydrochloric acid (HCl), mechanical ventilation (MV)

Procedia PDF Downloads 673

Authors: Titir Bhattacharya, Tanika Chakraborty, Prabal K. De

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Maternal and child health continue to be a significant policy focus in developing countries, including India. An emerging model in health care is the creation of public and private partnerships. Since the construction of physical infrastructure is costly, governments at various levels have tried to implement social health insurance schemes where a trust calculates insurance premiums and medical payments. Typically, qualifying families get full subsidization of the premium and get access to private hospitals, in addition to low cost public hospitals, for their tertiary care needs. We analyze one such pioneering social insurance scheme in the Indian state of Andhra Pradesh (AP). The Rajiv Aarogyasri program (RA) was introduced by the Government of AP on a pilot basis in 2007 and implemented in 2008. In this paper, we first examine the extent to which access to reproductive health care changed. For example, the RA scheme reimburses hospital deliveries leading us to expect an increase in institutional deliveries, particularly in private hospitals. Second, we expect an increase in institutional deliveries to also improve child health outcomes. Hence, we estimate if the program improved infant and child mortality. We use District Level Health Survey data to create annual birth cohorts from 2000-2015. Since AP was the only state in which such a state insurance program was implemented, the neighboring states constituted a plausible control group. Combined with the policy timing, and the year of birth, we employ a difference-indifference strategy to identify the effects of RA on the residents of AP. We perform several checks against threats to identification, including testing for pre-treatment trends between the treatment and control states. We find that the policy significantly lowered infant and child mortality in AP. We also find that deliveries in private hospitals increased, and government hospitals decreased, showing a substitution effect of the relative price change. Finally, as expected, out-of-pocket costs declined for the treatment group. However, we do not find any significant effects for usual preventive care such as vaccination, showing that benefits of insurance schemes targeted at the tertiary level may not trickle down to the primary care level.

Keywords: public health insurance, maternal and child health, public-private choice

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Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

Procedia PDF Downloads 463

Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

Procedia PDF Downloads 129

Authors: Adem Peker, Yüksel Eroğlu, İsmail Ay

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As the information and communication technologies have become embedded in everyday life of adolescents, both their possible benefits and risks to adolescents are being identified. The information and communication technologies provide opportunities for adolescents to connect with peers and to access to information. However, as with other social connections, users of information and communication devices have the potential to meet and interact with in harmful ways. One emerging example of such interaction is cyber bullying. Cyber bullying occurs when someone uses the information and communication technologies to harass or embarrass another person. Cyber bullying can take the form of malicious text messages and e-mails, spreading rumours, and excluding people from online groups. Cyber bullying has been linked to psychological problems for cyber bullies and victims. Therefore, it is important to determine how internet addiction contributes to cyber bullying. Building on this question, this study takes a closer look at the relationship between internet addiction and cyber bullying. For this purpose, in this study, based on descriptive relational model, it was hypothesized that loss of control, excessive desire to stay online, and negativity in social relationships, which are dimensions of internet addiction, would be associated positively with cyber bullying and victimization. Participants were 383 high school students (176 girls and 207 boys; mean age, 15.7 years). Internet addiction was measured by using Internet Addiction Scale. The Cyber Victim and Bullying Scale was utilized to measure cyber bullying and victimization. The scales were administered to the students in groups in the classrooms. In this study, stepwise regression analyses were utilized to examine the relationships between dimensions of internet addiction and cyber bullying and victimization. Before applying stepwise regression analysis, assumptions of regression were verified. According to stepwise regression analysis, cyber bullying was predicted by loss of control (β=.26, p<.001) and negativity in social relationships (β=.13, p<.001). These variables accounted for 9 % of the total variance, with the loss of control explaining the higher percentage (8 %). On the other hand, cyber victimization was predicted by loss of control (β=.19, p<.001) and negativity in social relationships (β=.12, p<.001). These variables altogether accounted for 8 % of the variance in cyber victimization, with the best predictor loss of control (7 % of the total variance). The results of this study demonstrated that, as expected, loss of control and negativity in social relationships predicted cyber bullying and victimization positively. However, excessive desire to stay online did not emerge a significant predictor of both cyberbullying and victimization. Consequently, this study would enhance our understanding of the predictors of cyber bullying and victimization since the results proposed that internet addiction is related with cyber bullying and victimization.

Keywords: cyber bullying, internet addiction, adolescents, regression

Procedia PDF Downloads 315