Search results for: feature selection feature subset selection feature extraction/transformation

Authors: Bishakha Majumdar, Ranjeet Nambudiri

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Considerable dichotomy exists in research regarding the role of optimism and self-efficacy in work and career outcomes. Optimism and self-efficacy are related to performance, commitment and engagement, but also are implicated in seeing opportunities outside the firm and switching jobs. There is absence of research capturing these opposing strands of findings in the same model and providing a holistic understanding of how the expectancy beliefs operate in case of the working professional. We attempt to bridge this gap by proposing that career-decision self-efficacy and career outcome expectations affect intention to quit through the competitive mediation pathways of internal and external marketability. This model provides a holistic picture of the role of career expectancy beliefs on career outcomes, by considering perceived career opportunities both inside and outside one’s present organization. The understanding extends the application of career expectancy beliefs in the context of career decision-making by the employed individual. Further, it is valuable for reconsidering the effectiveness of hiring and retention techniques used by a firm, as selection, rewards and training programs need to be supplemented by interventions that specifically strengthen the stability pathway.

Keywords: career decision self-efficacy, career outcome expectations, marketability, intention to quit, job mobility

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Authors: Nadia Belu, Laurenţiu Mihai Ionescu, Agnieszka Misztal

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The automotive industry is one of the most important industries in the world that concerns not only the economy, but also the world culture. In the present financial and economic context, this field faces new challenges posed by the current crisis, companies must maintain product quality, deliver on time and at a competitive price in order to achieve customer satisfaction. Two of the most recommended techniques of quality management by specific standards of the automotive industry, in the product development, are Failure Mode and Effects Analysis (FMEA) and Control Plan. FMEA is a methodology for risk management and quality improvement aimed at identifying potential causes of failure of products and processes, their quantification by risk assessment, ranking of the problems identified according to their importance, to the determination and implementation of corrective actions related. The companies use Control Plans realized using the results from FMEA to evaluate a process or product for strengths and weaknesses and to prevent problems before they occur. The Control Plans represent written descriptions of the systems used to control and minimize product and process variation. In addition Control Plans specify the process monitoring and control methods (for example Special Controls) used to control Special Characteristics. In this paper we propose a computer-aided solution with Genetic Algorithms in order to reduce the drafting of reports: FMEA analysis and Control Plan required in the manufacture of the product launch and improved knowledge development teams for future projects. The solution allows to the design team to introduce data entry required to FMEA. The actual analysis is performed using Genetic Algorithms to find optimum between RPN risk factor and cost of production. A feature of Genetic Algorithms is that they are used as a means of finding solutions for multi criteria optimization problems. In our case, along with three specific FMEA risk factors is considered and reduce production cost. Analysis tool will generate final reports for all FMEA processes. The data obtained in FMEA reports are automatically integrated with other entered parameters in Control Plan. Implementation of the solution is in the form of an application running in an intranet on two servers: one containing analysis and plan generation engine and the other containing the database where the initial parameters and results are stored. The results can then be used as starting solutions in the synthesis of other projects. The solution was applied to welding processes, laser cutting and bending to manufacture chassis for buses. Advantages of the solution are efficient elaboration of documents in the current project by automatically generating reports FMEA and Control Plan using multiple criteria optimization of production and build a solid knowledge base for future projects. The solution which we propose is a cheap alternative to other solutions on the market using Open Source tools in implementation.

Keywords: automotive industry, FMEA, control plan, automotive technology

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Authors: Binbin Chen, Dennis Y. C. Leung

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Aluminium (Al) -air cell holds a high volumetric capacity density of 8.05 Ah cm-3, benefit from the trivalence of Al ions. Additional benefits of Al-air cell are low price and environmental friendliness. Furthermore, the Al energy conversion process is characterized of 100% recyclability in theory. Along with a large base of raw material reserve, Al attracts considerable attentions as a promising material to be integrated within the global energy system. However, despite the early successful applications in military services, several problems exist that prevent the Al-air cells from widely civilian use. The most serious issue is the parasitic corrosion of Al when contacts with electrolyte. To overcome this problem, super-pure Al alloyed with various traces of metal elements are used to increase the corrosion resistance. Nevertheless, high-purity Al alloys are costly and require high energy consumption during production process. An alternative approach is to add inexpensive inhibitors directly into the electrolyte. However, such additives would increase the internal ohmic resistance and hamper the cell performance. So far these methods have not provided satisfactory solutions for the problem within Al-air cells. For the operation of alkaline Al-air cell, there are still other minor problems. One of them is the formation of aluminium hydroxide in the electrolyte. This process decreases ionic conductivity of electrolyte. Another one is the carbonation process within the gas diffusion layer of cathode, blocking the porosity of gas diffusion. Both these would hinder the performance of cells. The present work optimizes the above problems by building an Al-air cell operation system, consisting of four components. A top electrolyte tank containing fresh electrolyte is located at a high level, so that it can drive the electrolyte flow by gravity force. A mechanical rechargeable Al-air cell is fabricated with low-cost materials including low grade Al, carbon paper, and PMMA plates. An electrolyte waste tank with elaborate channel is designed to separate the hydrogen generated from the corrosion, which would be collected by gas collection device. In the first section of the research work, we investigated the performance of the mechanical rechargeable Al-air cell with a constant flow rate of electrolyte, to ensure the repeatability experiments. Then the whole system was assembled together and the feasibility of operating was demonstrated. During experiment, pure hydrogen is collected by collection device, which holds potential for various applications. By collecting this by-product, high utilization efficiency of aluminum is achieved. Considering both electricity and hydrogen generated, an overall utilization efficiency of around 90 % or even higher under different working voltages are achieved. Fluidic electrolyte could remove aluminum hydroxide precipitate and solve the electrolyte deterioration problem. This operation system provides a low-cost strategy for harvesting energy from the abundant secondary Al. The system could also be applied into other metal-air cells and is suitable for emergency power supply, power plant and other applications. The low cost feature implies great potential for commercialization. Further optimization, such as scaling up and optimization of fabrication, will help to refine the technology into practical market offerings.

Keywords: aluminium-air cell, high efficiency, hydrogen, mechanical recharge

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Authors: A. Falavand Jozaei, A. Ghafouri

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Operation enhancement in an air cooler (heat exchanger) depends on the rate of heat transfer, and pressure drop. In this paper, for a given heat duty, study of the effects of FPI (fin per inch) and fin type (circular and hexagonal fins) on two parameters mentioned above is considered in an air cooler in Iran, Arvand petrochemical. A program in EES (Engineering Equations Solver) software moreover, Aspen B-JAC and HTFS+ software are used for this purpose to solve governing equations. At first the simulated results obtained from this program is compared to the experimental data for two cases of FPI. The effects of FPI from 3 to 15 over heat transfer (Q) to pressure drop ratio (Q/Δp ratio). This ratio is one of the main parameters in design, rating, and simulation heat exchangers. The results show that heat transfer (Q) and pressure drop increase with increasing FPI (fin per inch) steadily, and the Q/Δp ratio increases to FPI = 12 (for circular fins about 47% and for hexagonal fins about 69%) and then decreased gradually to FPI = 15 (for circular fins about 5% and for hexagonal fins about 8%), and Q/Δp ratio is maximum at FPI = 12. The FPI value selection between 8 and 12 obtained as a result to optimum heat transfer to pressure drop ratio. Also by contrast, between circular and hexagonal fins results, the Q/Δp ratio of hexagonal fins more than Q/Δp ratio of circular fins for FPI between 8 and 12 (optimum FPI).

Keywords: air cooler, circular and hexagonal fins, fin per inch, heat transfer and pressure drop

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Authors: Yeganeh Jabbari, Sepideh Khalatabad

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This study explores the development of virtual reality (VR) technology for training applications, specifically its the potential benefits of VR technology for employee training and its ability to simulate real-world scenarios in a safe and controlled environment are highlighted, along with the associated cost and time savings. The adoption of VR technology in high-risk industrial organizations such as the oil and gas industry is discussed, with a focus on its ability to improve worker performance. Additionally, the use of VR technology in activities such as simulation and data visualization in the oil and gas industry is explored, leading to enhanced safety measures and collaboration between teams. The integration of advanced technologies such as robotics is mentioned as a way to further promote efficiency and sustainability. Also, the study mentions that the digital transformation of the oil and gas industry is revolutionizing operations and promoting safety, efficiency, and sustainability through the use of VR technology.

Keywords: virtual reality training, virtual reality benefits, high-risk industries, digital transformation

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Authors: Farhad Shafiepour Motlagh, Narges Salehi

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The aim of the present study was to investigate the relationship between inspirational leadership style and perceived social capital through the mediation of organizational knowledge resource development. The research method was descriptive-correlational. The statistical population consisted of all 3537 secondary school teachers in Isfahan. Sample selection was based on Cochran's formula volume formula for 338 people and multi-stage random sampling. The research instruments included a researcher-made inspirational leadership style questionnaire, a perceived social capital questionnaire (Putnam, 1999), and a researcher-made questionnaire of perceived organizational knowledge resources. Kolmogorov statistical tests, Pearson correlation, stepwise multiple regression, and structural equation modeling were used to analyze the data. In general, the results showed that there is a significant relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05. Also, the development of organizational knowledge resources mediates the relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05.

Keywords: inspirational leadership style, perceived social capital, perceived organizational knowledge

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Authors: Shakiera Sallie, Angela James

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Global concern about healthcare-associated infections through the transmission of microorganisms, resulting in outbreaks in overcrowded intensive care units (ICU), is current. Medical equipment and surfaces in the immediate patient zone, the high-touch objects, may become contaminated. A study was conducted across six intensive care units in a healthcare facility to determine the understanding and practice of the cleaning of high-touch objects (HTO), and an intervention program was undertaken. A mixed-method approach with the selection of ICUs, HTOs, and healthcare personnel was undertaken. Data collection included Ultra-Violet instruments, a questionnaire, and an intervention. In the pre-intervention, 41 (52.5%) of the healthcare personnel (n=78) rated their understanding of HTOs as “sufficient”; post-intervention, it was 67 (75%), (n=89), p=0.0015, indicates an improvement. The UV stamp percentage compliance to indicate whether cleaning of the HTOs had taken place across the six intensive care units before the intervention ranged from 0% compliance to 88% compliance, and after, it ranged from 67% to 91%. An intervention program on the cleaning of HTOs and the transmission cycle of microorganisms in the ICUs enhanced the healthcare personnel’s understanding and practices on the importance of environmental cleaning.

Keywords: high touch objects, infections, intensive care units, intervention program, microorganisms

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Authors: Raquel Rossini, Edelvais Caldeira

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The aim of this work is to investigate the motivation for the metaphorical uses of two verb combinations: come out and take over. Drawing from cognitive semantics theories, image schemas and metaphors, it was attempted to demonstrate that: a) the metaphorical senses of both 'come out' and 'take over' extend from both the verbs and the particles central (spatial) senses in such verb combinations; and b) the particles 'out' and 'over' also contribute to the whole meaning of the verb combinations. In order to do so, a random selection of 579 concordance lines for come out and 1,412 for take over was obtained from the Corpus of Contemporary American English – COCA. One of the main procedures adopted in the present work was the establishment of verb and particle central senses. As per the research questions addressed in this study, they are as follows: a) how does the identification of trajector and landmark help reveal patterns that contribute for the identification of the semantic network of these two verb combinations?; b) what is the relationship between the schematic structures attributed to the particles and the metaphorical uses found in empirical data?; and c) what conceptual metaphors underlie the mappings from the source to the target domains? The results demonstrated that not only the lexical verbs come and take, but also the particles out and over play an important whole in the different meanings of come out and take over. Besides, image schemas and conceptual metaphors were found to be helpful in order to establish the motivations for the metaphorical uses of these linguistic structures.

Keywords: cognitive linguistics, English syntax, multi-word verbs, prepositions

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Authors: Jiayun Shen, Lorenz Wyss, Thierry Golliard, Matthias Finger

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Innovation is widely recognized as the key for private enterprises to win the market competition. The state-owned-enterprises need to be innovative to compete in the market after the privatization as well. However, it is a lack of research to study how state-owned-enterprises manage innovation to create new products and services. Swiss Post, a Swiss state-owned-enterprises, has established a department to transform the corporate culture and foster innovation to achieve digital transformation. This paper describes the innovation management process at the Swiss Post and analyzes the impacts of the instruments, the organizational structure, and explores the barriers of innovation. This study used qualitative methods based on a review of the literature on innovation management and semi-structured interviews. Being established for over five years, the Swiss Post’s innovation management department has established a software-assisted modularized platform with systematic instruments to help the internal employees with the different innovation processes. It guides the innovators from idea creation to piloting in markets and supports with a separate financing source, with knowledge inputs and coaching, as well as with connections to external partners through the open innovation and venturing team. The platform also adapts to different business units within the corporate with a customized tailor for the various operational business units. The separate financing instruments enabled the creation and further development of new ideas; the coaching services contribute greatly to the transformation of teams’ innovation culture by providing new knowledge, thinking methods, and use cases for inspiration. It also facilitates organizational learning to help the whole corporate with the digital transformation. However, it is also confronted with a big challenge in twofold. Internally, the disruptive projects often hardly overcome the obstacles of long-established operational processes in the traditional business units; externally, the expectations of the public and restrictions from the federal government have become high hurdles for the company to stay and compete in the innovation track.

Keywords: empirical case study, innovation management, state-owned-enterprise, Swiss Post

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Authors: Shih-Jie Ma, Jui-Hsu Hsiao, Ming-Ying Hsieh, Shin-Yan Yang, Chun-Han Yeh, Kuo-Chun Su

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In Taiwan, social enterprises and non-profit organizations (NPOs) are not familiar with innovative business models, such as live streaming. In 2019, a brand new course called internet celebrity training project is introduced to them by the Social Innovation Lab. The Goal of this paper is to evaluate the effect of this project, to explore the role of new technology (internet live stream) in business process management (BPM), and to analyze how live stream programs can assist social enterprises in creating new business models. Social Innovation, with the purpose to solve social issues in innovative ways, is one of the most popular topics in the world. Social Innovation Lab was established in 2017 by Executive Yuan in Taiwan. The vision of Social Innovation Lab is to exploit technology, innovation and experimental methods to solve social issues, and to maximize the benefits from government investment. Social Innovation Lab aims at creating a platform for both supply and demand sides of social issues, to make social enterprises and start-ups communicate with each other, and to build an eco-system in which stakeholders can make a social impact. Social Innovation Lab keeps helping social enterprises and NPOs to gain better publicity and to enhance competitiveness by facilitating digital transformation. In this project, Social Innovation Lab exerted the influence of social media such as YouTube and Facebook, to make social enterprises and start-ups adjust their business models by using the live stream of social media, which becomes one of the tools to expand their market and diversify their sales channels. Internet live stream training courses were delivered in different regions of Taiwan in 2019, including Taitung, Taichung, Kaohsiung and Hualien. Through these courses, potential groups and enterprises were cultivated to become so-called internet celebrities. With their concern about social issues in mind, these internet celebrities know how to manipulate social media to make a social impact in different fields, such as aboriginal people, food and agriculture, LOHAS (Lifestyles of Health and Sustainability), environmental protection and senior citizens. Participants of live stream training courses in Taiwan are selected to take in-depth interviews and questionnaire surveys. Results indicate that the digital transformation process of social enterprises and NPOs can be successful by implementing business process reengineering, a significant change made by social innovation internet celebrities. Therefore, this project can be the new driving force to facilitate the business model transformation in Taiwan.

Keywords: business process management, digital transformation, live stream, social innovation

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Authors: Andreas Ahrens, Ojaras Purvinis, Jelena Zascerinska

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A successful shopping experience without overcrowded shops and long waiting times undoubtedly leads to the release of happiness hormones and is generally considered the goal of any optimization. Factors influencing the shopping experience can be divided into internal and external ones. External factors are related, e. g. to the arrival of the customers to the shop, whereas internal are linked with the service process itself when checking out (waiting in the queue to the cash register and the scanning of the goods as well as the payment process itself) or any other non-expected delay when changing the status from a visitor to a buyer by choosing goods or items. This paper divides the customer-server interaction into five phases starting with the customer's arrival at the shop, the selection of goods, the buyer waiting in the queue to the cash register, the payment process, and ending with the customer or buyer's departure. Our simulation results show how five phases are intertwined and influence the overall shopping experience. Parameters for measuring the shopping experience are estimated based on the burstiness level in each of the five phases of the customer-server interaction.

Keywords: customers’ burstiness, cash register, customers’ wait-ing time, gap distribution function

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Authors: H. J. T. Kevin Mozes, Dyah Purnamasari

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Background: Lung cancer accounted for the fourth most common cancer in Indonesia. 85% of all lung cancer cases are the Non-Small Cell Lung Cancer (NSCLC). The indistinct signs and symptoms of NSCLC sometimes lead to misdiagnosis. The gold standard assessment for the diagnosis of NSCLC is the histopathological biopsy, which is invasive. Cyfra 21-1 is a tumor marker, which can be found in the intermediate protein structure in the epitel. The accuracy of Cyfra 21-1 in diagnosing NSCLC is not yet known, so this report is made to seek the answer for the question above. Methods: Literature searching is done using online databases. Proquest and Pubmed are online databases being used in this report. Then, literature selection is done by excluding and including based on inclusion criterias and exclusion criterias. The selected literature is then being appraised using the criteria of validity, importance, and validity. Results: From six journals appraised, five of them are valid. Sensitivity value acquired from all five literature is ranging from 50-84.5 %, meanwhile the specificity is 87.8 %-94.4 %. Likelihood the ratio of all appraised literature is ranging from 5.09 -10.54, which categorized to Intermediate High. Conclusion: Serum Cyfra 21-1 is a sensitive and very specific tumor marker for diagnosis of non-small cell lung cancer (NSCLC).

Keywords: cyfra 21-1, diagnosis, nonsmall cell lung cancer, NSCLC, tumor marker

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Shilpesh R. Rajpurohit, Harshit K. Dave

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Additive manufacturing gains the popularity in recent times, due to its capability to create prototype as well functional as end use product directly from CAD data without any specific requirement of tooling. Fused deposition modeling (FDM) is one of the widely used additive manufacturing techniques that are used to create functional end use part of polymer that is comparable with the injection-molded parts. FDM printed part has an application in various fields such as automobile, aerospace, medical, electronic, etc. However, application of FDM part is greatly affected by poor mechanical properties. Proper selection of the process parameter could enhance the mechanical performance of the printed part. In the present study, experimental investigation has been carried out to study the behavior of the mechanical performance of the printed part with respect to process variables. Three process variables viz. raster angle, raster width and layer height have been varied to understand its effect on tensile strength. Further, effect of process variables on fractured surface has been also investigated.

Keywords: 3D Printing, fused deposition modeling, layer height, raster angle, raster width, tensile strength

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Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Anupama Sekhar, Preeti Saryan, Vinita Gowda

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Plants, due to their sedentary lifestyle, have evolved mechanisms to synthesize a huge diversity of complex, specialized chemical metabolites, a majority of them being volatile organic compounds (VOCs). These VOCs are heavily involved in their biotic and abiotic interactions. Since chemical composition could be under the same selection processes as other morphological characters, we test if VOCs can be used to taxonomically distinguish species in the well-studied, fragrant ginger genus -Hedychium (Zingiberaceae). We propose that variations in the volatile profiles are suggestive of adaptation to divergent environments, and their presence could be explained by either phylogenetic conservatism or ecological factors. In this study, we investigate the volatile chemistry within Hedychium, which is endemic to Asian palaeotropics. We used an unsupervised clustering approach which clearly distinguished most taxa, and we used ancestral state reconstruction to estimate phylogenetic signals and chemical trait evolution in the genus. We propose that taxonomically, the chemical composition could aid in species identification, especially in species complexes where taxa are not morphologically distinguishable, and extensive, targeted chemical libraries will help in this effort.

Keywords: chemotaxonomy, dynamic headspace sampling, floral fragrance, floral volatile evolution, gingers, Hedychium

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Authors: Marc S. Paolella, Pawel Polak

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The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

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Authors: Shahid Hussain Abro, F. Mufadi, A. Boodi

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Microstructural study and phase transformation in steels is a basic and important step during the design of structural steel. There are huge efforts and study has been done so far on phase transformations, due to so many steel grades available commercially the phase development in steel has different consequences. In the present work an effort has been made to study the effect of heating rate on microstructural features of cold heading quality steel. The SEM, optical microscopy, and heat treatment techniques have been applied to observe the microstructural features in the experimental steel. It was observed that heating rate has the strong influence on phase transformation of CHQ steel under investigation. Heating rate increases the austenite formation kinetics with respect to holding time, and this austenite has been transformed to martensite upon cooling. Heating rate also plays a vital role on nucleation sites of austenite formation in the experimental steel.

Keywords: CHQ steel, austenite formation, heating rate, nucleation

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Authors: Alfredo R. Roa-Henriquez

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This paper is about organizational-level resilience and decision-making in the face of natural hazards. Research on resilience emerged to explain systems’ ability to absorb and recover in the midst of adversity and uncertainty from natural disasters, crises, and other disruptive events. While interest in resilience has accelerated, research multiplied, and the number of policies and implementations of resilience to natural hazards has increased over the last several years, mainly at the level of communities and regions, there has been a dearth of empirical work on resilience at the level of the firm. This paper uses empirical data and a sample selection model to test some hypotheses related to the firm’s dependence on critical resources, the sharing of resources and its economic resilience. The objective is to understand how the sharing of resources among organizations is related to economic resilience. Empirical results that are obtained from a sample of firms affected by Superstorm Sandy and Hurricane Harvey indicate that there is unobserved heterogeneity that explains the strategic behavior of firms in the post-disaster and that those firms that are more likely to resource share are also the ones that exhibit higher economic resilience. The impact of property damage on the sharing of resources and economic resilience is explored.

Keywords: economic resilience, resource sharing, critical resources, strategic management

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Authors: Khaled M. Khleifat

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An extracellular methanol and ethanol tolerant lipase producing bacterial strain K5b4 was isolated from soil samples contaminated with hydrocarbon residues. It was identified by using morphological and biochemical characteristics and 16srRNA technique as Acinetobacter species. The immobilized lipase from Acinetobacter sp. K5b4 retained more than 98% of its residual activity after incubation with pure methanol and ethanol for 24 hours. The highest hydrolytic activity of the immobilized enzyme was obtained in the presence of 75% (v/v) methanol in the assay solution. In contrary, the enzyme was able to maintain its original activity up to only 25% (v/v) ethanol whereas at elevated concentrations of 50 and 75% (v/v) the enzyme activity was reduced to 10 and 40%, respectively. Maximum lipase activity of 31.5 mU/mL was achieved after 48 hr cultivation when the optimized medium (pH 7.0) that composed of 1.0% (w/v) olive oil, 0.2% (w/v) glycerol, 0.15% (w/v) yeast extract, and 0.05% (w/v) NaCl was inoculated with 0.4% (v/v) seed culture and incubated at 30°C and 150 rpm agitation speed. However, the presence of CaCl2 in the growth media did not show any inhibitory or stimulatory effect on the enzyme production as it compared to the control experiment. Meanwhile, the other mineral salts MgCl2, MnCl2, KCl and CoCl2 were negatively affected the production of lipase enzyme. The inhibition of lipase production from Acinetobacter sp. K5b4 in presence of glucose suggesting that lipase gene expression is prone to catabolic repression.

Keywords: K5B4, methanol and ethanol, acinetobacter, morphological

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Authors: Kailas L. Wasewar

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Biobutanol has been considered as a potential and alternative biofuel relative to the most popular biodiesel and bioethanol. End product toxicity is the major problems in commercialization of fermentation based process which can be reduce to some possible extent by removing biobutanol simultaneously. Several techniques have been investigated for removing butanol from fermentation broth such as stripping, adsorption, liquid–liquid extraction, pervaporation, and membrane solvent extraction. Liquid–liquid extraction can be performed with high selectivity and is possible to carry out inside the fermenter. Conventional solvents have few drawbacks including toxicity, loss of solvent, high cost etc. Hence alternative solvents must be explored for the same. Room temperature ionic liquids (RTILs) composed entirely of ions are liquid at room temperature having negligible vapor pressure, non-flammability, and tunable physiochemical properties for a particular application which term them as “designer solvents”. Ionic liquids (ILs) have recently gained much attention as alternatives for organic solvents in many processes. In particular, ILs have been used as alternative solvents for liquid–liquid extraction. Their negligible vapor pressure allows the extracted products to be separated from ILs by conventional low pressure distillation with the potential for saving energy. Morpholinium, imidazolium, ammonium, phosphonium etc. based ionic liquids have been employed for the separation biobutanol. In present chapter, basic concepts of ionic liquids and application in separation have been presented. Further, type of ionic liquids including, conventional, functionalized, polymeric, supported membrane, and other ionic liquids have been explored. Also the effect of various performance parameters on separation of biobutanol by ionic liquids have been discussed and compared for different cation and anion based ionic liquids. The typical methodology for investigation have been adopted such as contacting the equal amount of biobutanol and ionic liquids for a specific time say, 30 minutes to confirm the equilibrium. Further, biobutanol phase were analyzed using GC to know the concentration of biobutanol and material balance were used to find the concentration in ionic liquid.

Keywords: biobutanol, separation, ionic liquids, sustainability, biorefinery, waste biomass

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Authors: Seoi Lee, Dongjoo Chin, Heewon Kim

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Gamification refers to a technique that applies game elements to non-gaming elements, such as education and exercise, to make people more engaged in these behaviors. The purpose of this study was to identify effective elements in gamification for changing human behaviors. In order to accomplish this purpose, a survey based on learning theory was developed, especially for assessing antecedents and consequences of behaviors, and 8 popular and 8 unpopular games were selected for comparison. A total of 407 adult males and females were recruited via crowdsourcing Internet marketplace and completed the survey, which consisted of 19 questions for antecedent and 14 questions for consequences. Results showed no significant differences in consequence questions between popular and unpopular games. For antecedent questions, popular games are superior to unpopular games in character customization, play type selection, a sense of belonging, patch update cycle, and influence or dominance. This study is significant in that it reveals the elements of gamification based on learning theory. Future studies need to empirically validate whether these factors affect behavioral change.

Keywords: gamification, learning theory, antecedent, consequence, behavior change, behaviorism

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Authors: Sayed Amir Hamzeh Mirkheshti

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Offshore wind energy as a strategic renewable energy, has been growing rapidly due to availability, abundance and clean nature of it. On the other hand, budget of this project is incredibly higher in comparison with other renewable energies and it takes more duration. Accordingly, precise estimation of time and cost is needed in order to promote awareness in the developers and society and to convince them to develop this kind of energy despite its difficulties. Occurrence risks during on project would cause its duration and cost constantly changed. Therefore, to develop offshore wind power, it is critical to consider all potential risks which impacted project and to simulate their impact. Hence, knowing about these risks could be useful for the selection of most influencing strategies such as avoidance, transition, and act in order to decrease their probability and impact. This paper presents an evaluation of the feasibility of 500 MV offshore wind project in the Persian Gulf and compares its situation with uncertainty resources and risk. The purpose of this study is to evaluate time and cost of offshore wind project under risk circumstances and uncertain resources by using Monte Carlo simulation. We analyzed each risk and activity along with their distribution function and their effect on the project.

Keywords: wind energy project, uncertain resources, risks, Monte Carlo simulation

Procedia PDF Downloads 353

Authors: Ram Prasad Baral

Abstract:

A large number of herb materials contain Essential Oils with extensive bioactivities. Acknowledging the importance of plants and its medicinal value, extraction of Essential Oil had been done using Steam Distillation method. In this project, Steam Distillation was used to extract oil from different plant materials like Chamomilla recutita (L.) Rauschert, Artemisia Vulgaris L, Rhododendron anthopogon D. Don, Cymbopogon nardus L, Andropogon nardus, Cinnamomum tamala, Juniperus spp, Cymbopohonflexuosus flexuous, Mantha Arvensia, Nardostachys Jatamansi, Wintergreen Essential Oil, and Valeriana Officinalis. Research has confirmed centuries of practical use of essential oils, and we now know that the 'fragrant pharmacy' contains compounds with an extremely broad range of biochemical effects. Essential oils are so termed as they are believed to represent the very essence of odor and flavor. The recovery of Essential Oil from the raw botanical starting material is very important since the quality of the oil is greatly influenced during this step. There is a variety of methods for obtaining volatile oils from plants. Steam distillation method was found to be one of the promising techniques for the extraction of essential oil from plants as reputable distiller will preserve the original qualities of the plant. The distillation was conducted in Clevenger apparatus in which boiling, condensing, and decantation was done. Analysis of essential oil was done using Gas Chromatography-Mass Spectrometer apparatus, which gives evaluates essential oil qualitatively and quantitatively. The volume of essential oil obtained was changing with respect to temperature and time of heating.

Keywords: Chamomilla recutita (L.) Rauschert, Artemisia Vulgaris L, Rhododendron anthopogon D. Don, Cymbopogon nardus L, Andropogon nardus, Cinnamomum tamala, Juniperus spp, Cymbopohonflexuosus flexuous, Mantha

Procedia PDF Downloads 328

Authors: Chinedu Ejike

Abstract:

The United States is now energy self-sufficient due to the production of shale oil reserves. With more than half of it being tapped daily in the United States, these unconventional reserves are massive and provide immense potential for future energy demands. Drilling horizontal wells and fracking are the primary methods for developing these reserves. Regrettably, recovery efficiency is rarely greater than 10%. As a result, optimizing recuperation offers a significant benefit. Huff and puff gas flooding and cyclic gas injection have all been demonstrated to be more successful than tapping the remaining oil in place. Methane, nitrogen, and carbon (IV) oxide, among other high-pressure gases, can be injected. Operators use Darcy's law to assess a reservoir's productive capacity, but they are unaware that the law may not apply to shale oil reserves. This is due to the fact that, unlike pressure differences alone, diffusion, concentration, and gas selection all play a role in the flow of gas injected into the wellbore. The reservoir drainage and oil sweep efficiency rates are determined by the transport method. This research assesses the parameters that influence the gas injection transport mechanism. Understanding the process causing these factors could accelerate recovery by two to three times, according to peer-reviewed studies and effective field testing.

Keywords: enhanced oil recovery, gas injection, shale oil, transport mechanism, unconventional reserve

Procedia PDF Downloads 174

Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

Abstract:

Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: assembly scheduling, large-scale products, make-to-order, optimization, rescheduling

Procedia PDF Downloads 459

Authors: Faouzi Ahtchi-Ali, Marcus Vitiello

Abstract:

A study was conducted in the east region of the Middle East to assess the constructability of a shoring system for a 12-meter deep excavation. Several shoring systems were considered in this study including secant concrete piling, contiguous concrete piling, and sheet-piling. The excavation was carried out in a very dense sand with the groundwater level located at 3 meters below ground surface. The study included conducting a pilot test for each shoring system listed above. The secant concrete piling included overlapping concrete piles to a depth of 16 meters. Drilling method with full steel casing was utilized to install the concrete piles. The verticality of the piles was a concern for the overlap. The contiguous concrete piling required the installation of micro-piles to seal the gap between the concrete piles. This method revealed that the gap between the piles was not fully sealed as observed by the groundwater penetration to the excavation. The sheet-piling method required pre-drilling due to the high blow count of the penetrated layer of saturated sand. This study concluded that the sheet-piling method with pre-drilling was the most cost effective and recommended a method for the shoring system.

Keywords: excavation, shoring system, middle east, Drilling method

Procedia PDF Downloads 468

Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 165

Authors: I. M. Sur, A. M. Chirila-Babau, T. Gabor, V. Micle

Abstract:

This paper shows researches in order to extract Cr, Cu and Ni from the polluted soils. Research is based on preliminary studies regarding the usage of Thiobacillus ferrooxidans bacterium (9K medium) for bioleaching of soil polluted with heavy metal (Cu, Cr and Ni). The microorganisms (Thiobacillus ferooxidans) selected directly from polluted soil samples were used in this experimental work. Soil samples used in the experimental research were taken from an area polluted with heavy metals from Romania. The soil samples are subjected to the cleaning process using the 9K medium solution (20 mL and 40 mL, respectively), stirred 200 rpm for 20 hours at a controlled temperature (30 ˚C). During the experiment (0, 2, 4, 8 and 20 h), liquid samples have been extracted and analyzed using the Atomic Absorption Spectrophotometer AA-6800 (AAS) in order to determine the Cr, Cu and Ni concentration. Experiments led to the conclusion that these soils can be depolluted by bioleaching, being a biological treatment method involving the use of microorganisms to favor the extraction of Cr, Cu and Ni from polluted soils.

Keywords: bioleaching, extraction, microorganisms, soil, polluted, Thiobacillus ferooxidans

Procedia PDF Downloads 163

Authors: David Pham, Yongfeng Zhang

Abstract:

Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

Procedia PDF Downloads 204