Search results for: industrial networks

Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Xiaoling Ren, Guidong Yang

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As one of the largest industrial synthetic chemicals in the world, ammonia has the advantages of high energy density, easy liquefaction, and easy transportation, which is widely used in agriculture, chemical industry, energy storage, and other fields. The industrial Haber-Bosch method process for ammonia synthesis is generally conducted under severe conditions. It is essential to develop a green, sustainable strategy for ammonia production to meet the growing demand. In this direction, photocatalytic nitrogen reduction has huge advantages over the traditional, well-established Haber-Bosch process, such as the utilization of natural sun light as the energy source and significantly lower pressure and temperature to affect the reaction process. However, the high activation energy of nitrogen and the low efficiency of photo-generated electron-hole separation in the photocatalyst result in low ammonia production yield. Many researchers focus on improving the catalyst. In addition to modifying the catalyst, improving the dispersion of the catalyst and making full use of active sites are also means to improve the overall catalytic activity. Few studies have been carried out on this, which is the aim of this work. In this work, by making full use of the nitrogen activation ability of WO3-x with defective sites, small size WO3-x photocatalyst with high dispersibility was constructed, while the growth of WO3-x was restricted by using a high specific surface area mesoporous SBA-15 molecular sieve with the regular pore structure as a template. The morphology of pure SBA-15 and WO3-x/SBA-15 was characterized byscanning electron microscopy (SEM). Compared with pure SBA-15, some small particles can be found in the WO3-x/SBA-15 material, which means that WO3-x grows into small particles under the limitation of SBA-15, which is conducive to the exposure of catalytically active sites. To elucidate the chemical nature of the material, the X-ray diffraction (XRD) analysis was conducted. The observed diffraction pattern inWO3-xis in good agreement with that of the JCPDS file no.71-2450. Compared with WO3-x, no new peaks appeared in WO3-x/SBA-15.It can be concluded that WO3-x/SBA-15 was synthesized successfully. In order to provide more active sites, the mass content of WO3-x was optimized. Then the photocatalytic nitrogen reduction performances of above samples were performed with methanol as a hole scavenger. The results show that the overall ammonia production performance of WO3-x/SBA-15 is improved than pure bulk WO3-x. The above results prove that making full use of active sites is also a means to improve overall catalytic activity.This work provides material basis for the design of high-efficiency photocatalytic nitrogen reduction catalysts.

Keywords: ammonia, photocatalytic, nitrogen reduction, WO3-x, high dispersibility

Procedia PDF Downloads 159

Authors: Tharntip Tawnie Chutikulrungsee, Oliver Kisalay Burmeister, Maumita Bhattacharya, Dragana Calic

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Sharing on social network sites (SNSs) has rapidly emerged as a new social norm and has become a global phenomenon. Billions of users reveal not only their own information (self disclosure) but also information about others (other-generated disclosure), resulting in a risk and a serious threat to either personal or informational privacy. Self-disclosure (SD) has been extensively researched in the literature, particularly regarding control of individual and existing privacy management. However, far too little attention has been paid to other-generated disclosure (OGD), especially by insiders. OGD has a strong influence on self-presentation, self-image, and electronic word of mouth (eWOM). Moreover, OGD is more credible and less likely manipulated than SD, but lacks privacy control and legal protection to some extent. This article examines OGD in depth, ranging from motivation to both online and offline impacts, based upon lived experiences from both ‘the disclosed’ and ‘the discloser’. Using purposive sampling, this phenomenological study involves an online survey and in-depth interviews. The findings report the influence of peer disclosure as well as users’ strategies to mitigate privacy issues. This article also calls attention to the challenge of OGD privacy and inadequacies in the law related to privacy protection in the digital domain.

Keywords: facebook, online privacy, other-generated disclosure, social networks sites (SNSs)

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Authors: Tizi Hayet, Berrama Tarek, Bounif Nadia

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Phenol and its derivatives are organic compounds utilized in the chemical industry. They are introduced into the environment by accidental spills and the illegal release of industrial and municipal wastewater. Phenols are organic intermediaries that are considered potential pollutants. Adsorption is one of the purification and separation techniques used in this area. Algeria annually produces 131000 tons of fig; therefore, a large amount of fig leaves is generated, and the conversion of this waste into adsorbent allows the valorization of agricultural residue. The main purpose of this present work is to describe an application of a statistical method for modeling and to optimize the conditions of the phenol adsorption from agricultural by-products, locally available (fig leaves). The best experimental performance of phenol elimination on the adsorbent was obtained with: Adsorbent concentration (X₂) = 200 mg L⁻¹; Initial concentration (X₃) = 150 mg L⁻¹; Speed agitation (X₁) = 300 rpm.

Keywords: low-cost adsorbents, adsorption, fig leaves, phenol, factorial design

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Authors: Vanaja Rao

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In the recent times, the need for the integration of Renewable Energy Sources (RES) in a Smart Grid is on the rise. As a result of this, associated energy storage systems are known to play important roles in sustaining the efficient operation of such RES like wind power and solar power. This paper investigates the importance of location selection of Energy Storage Systems (ESSs) in a Smart Grid. Three scenarios of ESS location is studied and analyzed in a Smart Grid, which are – 1. Near the generation/source, 2. In the middle of the Grid and, 3. Near the demand/consumption. This is explained with the aim of assisting any Distribution Network Operator (DNO) in deploying the ESSs in a power network, which will significantly help reduce the costs and time of planning and avoid any damages incurred as a result of installing them at an incorrect location of a Smart Grid. To do this, the outlined scenarios mentioned above are modelled and analyzed with the National Grid’s datasets of energy generation and consumption in the UK power network. As a result, the outcome of this analysis aims to provide a better overview for the location selection of the ESSs in a Smart Grid. This ensures power system stability and security along with the optimum usage of the ESSs.

Keywords: distribution networks, energy storage system, energy security, location planning, power stability, smart grid

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Nasir Haruna Soba

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In addition to sustaining poverty and inequality, the issue of out-of-school children impedes efforts to accomplish the sustainable development goals (SDGs), especially Goal 4, which is to guarantee inclusive, egalitarian, and high-quality education for everyone. However, a number of social, cultural, and infrastructure barriers mean that millions of children in Nigeria are denied this privilege. This paper presents the findings of a case study conducted in Nigeria. The findings of this study revealed that out of school children in Nigeria are the most common causes of poverty; inadequate school facilities, long distances to schools, and poor road networks make it difficult for children, especially in rural areas, to access education. Social Disparities: Social inequality is sustained by differences in education, especially when it comes to financing, governance, and coordination amongst stakeholders. These differences are especially pronounced along gender and socioeconomic lines. The study recommended that policymakers and stakeholders should consider addressing the root causes, enhancing existing interventions, and implementing targeted policy measures. Nigeria can make significant strides towards ensuring inclusive and quality education for all children, thereby fostering sustainable development and reducing poverty.

Keywords: poverty, inequality, funding, education, development

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Authors: Peter Carter

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Comprehensive climate change indicators and trends inform the state of the climate (system) with respect to present and future climate change scenarios and the urgency of mitigation and adaptation. With data records now going back for many decades, indicator trends can complement model projections. They are provided as datasets by several climate monitoring centers, reviewed by state of the climate reports, and documented by the IPCC assessments. Up-to-date indicators are provided here. Rates of change are instructive, as are extremes. The indicators include greenhouse gas (GHG) emissions (natural and synthetic), cumulative CO2 emissions, atmospheric GHG concentrations (including CO2 equivalent), stratospheric ozone, surface ozone, radiative forcing, global average temperature increase, land temperature increase, zonal temperature increases, carbon sinks, soil moisture, sea surface temperature, ocean heat content, ocean acidification, ocean oxygen, glacier mass, Arctic temperature, Arctic sea ice (extent and volume), northern hemisphere snow cover, permafrost indices, Arctic GHG emissions, ice sheet mass, sea level rise, and stratospheric and surface ozone. Global warming is not the most reliable single metric for the climate state. Radiative forcing, atmospheric CO2 equivalent, and ocean heat content are more reliable. Global warming does not provide future commitment, whereas atmospheric CO2 equivalent does. Cumulative carbon is used for estimating carbon budgets. The forcing of aerosols is briefly addressed. Indicator interactions are included. In particular, indicators can provide insight into several crucial global warming amplifying feedback loops, which are explained. All indicators are increasing (adversely), most as fast as ever and some faster. One particularly pressing indicator is rapidly increasing global atmospheric methane. In this respect, methane emissions and sources are covered in more detail. In their application, indicators used in assessing safe planetary boundaries are included. Indicators are considered with respect to recent published papers on possible catastrophic climate change and climate system tipping thresholds. They are climate-change-policy relevant. In particular, relevant policies include the 2015 Paris Agreement on “holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels” and the 1992 UN Framework Convention on Climate change, which has “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.”

Keywords: climate change, climate change indicators, climate change trends, climate system change interactions

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Authors: Mustafa Sahin, İlkay Yavrucuk

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This paper presents a wind turbine envelope protection control algorithm that protects Variable Speed Variable Pitch (VSVP) wind turbines from damage during operation throughout their below and above rated regions, i.e. from cut-in to cut-out wind speed. The proposed approach uses a neural network that can adapt to turbines and their operating points. An algorithm monitors instantaneous wind and turbine states, predicts a wind speed that would push the turbine to a pre-defined envelope limit and, when necessary, realizes an avoidance action. Simulations are realized using the MS Bladed Wind Turbine Simulation Model for the NREL 5 MW wind turbine equipped with baseline controllers. In all simulations, through the proposed algorithm, it is observed that the turbine operates safely within the allowable limit throughout the below and above rated regions. Two example cases, adaptations to turbine operating points for the below and above rated regions and protections are investigated in simulations to show the capability of the proposed envelope protection system (EPS) algorithm, which reduces excessive wind turbine loads and expectedly increases the turbine service life.

Keywords: adaptive envelope protection control, limit detection and avoidance, neural networks, ultimate load reduction, wind turbine power control

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Authors: Gowthamraj Vungarala

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This paper presents the experimental investigation on the bond strength, waterproofing abilities and flexibility of tile joint when Ordinary Portland Cement (OPC) or White Portland Cement (WPC) CEM II A-LL 42.5N and porcelain powder graded between 200 microns and 75 microns is mixed with vinyl acetate monomer (VAM), hydroxypropyl methyl cellulose ether, ethylene co-polymer rubber powder and Styrene butyl rubber (SBR). Use of porcelain powder which is tough to decompose as a form of industrial refuse which helps environmental safety and waste usage.

Keywords: styrene butane rubber, hydroxypropyl methyl cellulose ether, vinyl acetate monomer, polymer modified cement, polyethylene, porcelain powder

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Authors: Agi Sunday, Agih Ukuru Agih

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In Nigeria, the food processing sector is dominated by small and medium enterprises, as well as multinational food companies. Quality standards are usually related to improving the safety of food products suitable for consumption in accordance to specifications by food regulatory bodies. These standards are essential elements for local and international businesses which contribute to economic progress through industrial development and trade. This review takes a critical look on the Nigerian food industry development in terms of quality standards that are necessary to be given consideration in the production of food and also ways of improving food production in Nigeria through the use of Total Quality Management (TQM) technique and the use of computerized systems to produce high quality and high value products while at the same time reducing production time and cost.

Keywords: food industry, quality assurance, Nigeria, TQM, computerized systems

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Authors: Adithya Jaikumar, Sudarsan Jayasingh

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The dramatic technology led changes that continue to take place at the market place has led to the emergence and implication of online brand pages on social media networks. The Facebook brand page has become extremely popular among different brands. The primary aim of this study was to identify the impact of post formats and content type on customer engagement in Facebook brand pages. Methodology used for this study was to analyze and categorize 9037 content messages posted by 20 automobile brands in India during April 2014 to March 2015 and the customer activity it generated in return. The data was obtained from Fanpage karma- an online tool used for social media analytics. The statistical technique used to analyze the count data was negative binomial regression. The study indicates that there is a statistically significant relationship between the type of post and the customer engagement. The study shows that photos are the most posted format and highest engagement is found to be related to videos. The finding also reveals that social events and entertainment related content increases engagement with the message.

Keywords: content analysis, customer engagement, digital engagement, facebook brand pages, social media

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Authors: C. Fúnez Guerra, B. Nieto Calderón, M. Jaén Caparrós, L. Reyes-Bozo, A. Godoy-Faúndez, E. Vyhmeister

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The global energy system is experiencing a change of scenery. Unstable energy markets, an increasing focus on climate change and its sustainable development is forcing businesses to pursue new solutions in order to ensure future economic growth. This has led to the interest in using hydrogen as an energy carrier in transportation and industrial applications. As an energy carrier, hydrogen is accessible and holds a high gravimetric energy density. Abundant in hydrocarbons, hydrogen can play an important role in the shift towards low-emission fossil value chains. By combining hydrogen production by natural gas reforming with carbon capture and storage, the overall CO2 emissions are significantly reduced. In addition, the flexibility of hydrogen as an energy storage makes it applicable as a stabilizer in the renewable energy mix. The recent development in hydrogen fuel cells is also raising the expectations for a hydrogen powered transportation sector. Hydrogen value chains exist to a large extent in the industry today. The global hydrogen consumption was approximately 50 million tonnes (7.2 EJ) in 2013, where refineries, ammonia, methanol production and metal processing were main consumers. Natural gas reforming produced 48% of this hydrogen, but without carbon capture and storage (CCS). The total emissions from the production reached 500 million tonnes of CO2, hence alternative production methods with lower emissions will be necessary in future value chains. Hydrogen from electrolysis is used for a wide range of industrial chemical reactions for many years. Possibly, the earliest use was for the production of ammonia-based fertilisers by Norsk Hydro, with a test reactor set up in Notodden, Norway, in 1927. This application also claims one of the world’s largest electrolyser installations, at Sable Chemicals in Zimbabwe. Its array of 28 electrolysers consumes 80 MW per hour, producing around 21,000 Nm3/h of hydrogen. These electrolysers can compete if cheap sources of electricity are available and natural gas for steam reforming is relatively expensive. Because electrolysis of water produces oxygen as a by-product, a system of Autothermal Reforming (ATR) utilizing this oxygen has been analyzed. Replacing the air separation unit with electrolysers produces the required amount of oxygen to the ATR as well as additional hydrogen. The aim of this paper is to evaluate the technical and economic potential of large-scale production of hydrogen for oil refining industry. Sensitivity analysis of parameters such as investment costs, plant operating hours, electricity price and sale price of hydrogen and oxygen are performed.

Keywords: autothermal reforming, electrolyser, hydrogen, natural gas, steam methane reforming

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Abubakar Rashid, Muhammad Saad, Faheem Ahmed

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This paper provides a comprehensive study pertaining to diagnosis of intermittent high vibrations on an industrial gas turbine using detailed vibrations analysis, followed by its rectification. Engro Polymer & Chemicals Limited, a Chlor-Vinyl complex located in Pakistan has a captive combined cycle power plant having two 28 MW gas turbines (make Hitachi) & one 15 MW steam turbine. In 2018, the organization faced an issue of high vibrations on one of the gas turbines. These high vibration peaks appeared intermittently on both compressor’s drive end (DE) & turbine’s non-drive end (NDE) bearing. The amplitude of high vibration peaks was between 150-170% on the DE bearing & 200-300% on the NDE bearing from baseline values. In one of these episodes, the gas turbine got tripped on “High Vibrations Trip” logic actuated at 155µm. Limited instrumentation is available on the machine, which is monitored with GE Bently Nevada 3300 system having two proximity probes installed at Turbine NDE, Compressor DE &at Generator DE & NDE bearings. Machine’s transient ramp-up & steady state data was collected using ADRE SXP & DSPI 408. Since only 01 key phasor is installed at Turbine high speed shaft, a derived drive key phasor was configured in ADRE to obtain low speed shaft rpm required for data analysis. By analyzing the Bode plots, Shaft center line plot, Polar plot & orbit plots; rubbing was evident on Turbine’s NDE along with increased bearing clearance of Turbine’s NDE radial bearing. The subject bearing was then inspected & heavy deposition of carbonized coke was found on the labyrinth seals of bearing housing with clear rubbing marks on shaft & housing covering at 20-25 degrees on the inner radius of labyrinth seals. The collected coke sample was tested in laboratory & found to be the residue of lube oil in the bearing housing. After detailed inspection & cleaning of shaft journal area & bearing housing, new radial bearing was installed. Before assembling the bearing housing, cleaning of bearing cooling & sealing air lines was also carried out as inadequate flow of cooling & sealing air can accelerate coke formation in bearing housing. The machine was then taken back online & data was collected again using ADRE SXP & DSPI 408 for health analysis. The vibrations were found in acceptable zone as per ISO standard 7919-3 while all other parameters were also within vendor defined range. As a learning from subject case, revised operating & maintenance regime has also been proposed to enhance machine’s reliability.

Keywords: ADRE, bearing, gas turbine, GE Bently Nevada, Hitachi, vibration

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Authors: Burak Köken, Hatice D. Arslan, Bilgehan Y. Çakmak

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Analyzing the relation networks between the hospital buildings which have complex structure and distinctive spatial relationships is quite difficult. The hospital buildings which require specialty in spatial relationship solutions during design and self-innovation through the developing technology should survive and keep giving service even after the disasters such as earthquakes. In this study, a hospital building where the load-bearing system was strengthened because of the insufficient earthquake performance and the construction of an additional building was required to meet the increasing need for space was discussed and a comparative spatial evaluation of the hospital building was made with regard to its status before the change and after the change. For this reason, spatial organizations of the building before change and after the change were analyzed by means of Space Syntax method and the effects of the change on space organization parameters were searched by applying an analytical procedure. Using Depthmap UCL software, connectivity, visual mean depth, beta and visual integration analyses were conducted. Based on the data obtained after the analyses, it was seen that the relationships between spaces of the building increased after the change and the building has become more explicit and understandable for the occupants. Furthermore, it was determined according to findings of the analysis that the increase in depth causes difficulty in perceiving the spaces and the changes considering this problem generally ease spatial use.

Keywords: architecture, hospital building, space syntax, strengthening

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Authors: Sh. Foroughi, V. Karamzadeh, M. Packirisamy

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This paper presents design and analysis of an electrothermally symmetrical actuated microgripper applicable for performing micro assembly or biological cell manipulation. Integration of micro-optics with microdevice leads to achieve extremely precise control over the operation of the device. Geometry, material, actuation, control, accuracy in measurement and temperature distribution are important factors which have to be taken into account for designing the efficient microgripper device. In this work, analyses of four different geometries are performed by means of COMSOL Multiphysics 5.2 with implementing Finite Element Methods. Then, temperature distribution along the fingertip, displacement of gripper site as well as optical efficiency vs. displacement and electrical potential are illustrated. Results show in addition to the industrial application of this device, the usage of that as a cell manipulator is possible.

Keywords: electro thermal actuator, MEMS, microgripper, MOEMS

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Authors: Jung-Hun Cho, Tae-Heon Moon, Jin-Hak Lee

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Land price contains the comprehensive characteristics of urban space, representing the social and economic features of the city. Accordingly, land price can be utilized as an indicator, which can identify the changes of spatial structure and socioeconomic variations caused by urban development. This study attempted to explore the changes in land price by a new road construction. Methodologically, it adopted Space Syntax, which can interpret urban spatial structure comprehensively, to identify the relationship between the forms of road networks and land price. The result of the regression analysis showed the ‘integration index’ of Space Syntax is statistically significant and has a strong correlation with land price. If the integration value is high, land price increases proportionally. Subsequently, using regression equation, it tried to predict the land price changes of each of the lots surrounding the roads that are newly opened. The research methods or study results have the advantage of predicting the changes in land price in an easy way. In addition, it will contribute to planners and project managers to establish relevant polices and smoothing urban regeneration projects through enhancing residents’ understanding by providing possible results and advantages in their land price before the execution of urban regeneration and development projects.

Keywords: space syntax, urban regeneration, spatial structure, official land price

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Colette Faucher

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In modern asymmetric conflicts, the Armed Forces generally have to intervene in countries where the internal peace is in danger. They must make the local population an ally in order to be able to deploy the necessary military actions with its support. For this purpose, psychological operations (PSYOPs) are used to shape people’s behaviors and emotions by the modification of their attitudes in acting on their perceptions. PSYOPs aim at elaborating and spreading a message that must be read, listened to and/or looked at, then understood by the info-targets in order to get from them the desired behavior. A message can generate in the info-targets, reasoned thoughts, spontaneous emotions or reflex behaviors, this effect partly depending on the means of conveyance used to spread this message. In this paper, we focus on psychological operations that generate emotions. We present a method based on the Intergroup Emotion Theory, that determines, from the characteristics of the conveyed message and of the people from the population directly reached by the means of conveyance (direct info-targets), the emotion likely to be triggered in them and we simulate the propagation of the effects of such a message on indirect info-targets that are connected to them through the social networks that structure the population.

Keywords: military psychological operations, social identity, social network, emotion propagation

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Authors: A. Chinig, J. Zbitou, A. Errkik, L. Elabdellaoui, A. Tajmouati, A. Tribak, M. Latrach

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This paper presents a new structure of microstrip band pass filter (BPF) based on coupled stepped impedance resonators. Each filter consists of two coupled stepped impedance resonators connected to microstrip feed lines. The coupled junction is utilized to connect the two BPFs to the antenna. This two band pass filters are designed and simulated to operate for the digital communication system (DCS) and Industrial Scientific and Medical (ISM) bands at 1.8 GHz and 2.45 GHz respectively. The proposed circuit presents good performances with an insertion loss lower than 2.3 dB and isolation between the two channels greater than 21 dB. The prototype of the optimized diplexer have been investigated numerically by using ADS Agilent and verified with CST microwave software.

Keywords: band pass filter, coupled junction, coupled stepped impedance resonators, diplexer, insertion loss, isolation

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Authors: Khaoula Hidouri, Ali Benhmidene, Bechir Chouachi

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The purification of brackish seawater becomes a necessity and not a choice against demographic and industrial growth especially in third world countries. Two models can be used in this work: simple solar still and simple solar still coupled with a heat pump. In this research, the productivity of water by Simple Solar Distiller (SSD) and Simple Solar Distiller Hybrid Heat Pump (SSDHP) was determined by the orientation, the use of heat pump, the simple or double glass cover. The productivity can exceed 1.2 L/m²h for the SSDHP and 0.5 L/m²h for SSD model. The result of the global efficiency is determined for two models SSD and SSDHP give respectively 30%, 50%. The internal efficiency attained 35% for SSD and 60% of the SSDHP models. Convective heat coefficient can be determined by attained 2.5 W/m²°C and 0.5 W/m²°C respectively for SSDHP and SSD models.

Keywords: productivity, efficiency, convective heat coefficient, SSD model, SSDHPmodel

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Authors: Radostina Popova

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The analysis of the competitiveness of the innovation of furniture enterprises in Bulgaria requires distinguishing types of innovation and innovation activities to determine the results. Main object of the analysis are the results of innovative activities with a high degree of novelty that determine competitiveness and high innovation. It is focused on innovative research in the EU in the last three-year period and its level is directly related to the forms of protection of industrial property, which enterprises use. The results are from studies of innovation of furniture enterprises in Bulgaria during the period - 2006-2012 on a sample of more than 600 furniture companies. The results from studies are focused on product and process (technological) innovations with a high degree of novelty.

Keywords: innovations, competitiveness, furniture enterprises, Bulgaria

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Authors: Sheikh, R. Author, Bhardwaj S. Author, Jr.

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The emphasis of this paper is to investigate the identity shifts experienced within the Indian community and among Muslim refugee women in Sydney. Using Goffman’s paradigm of everyday interactions, attention is paid to how migrants navigate and perform their multiple identities in their daily life. By focusing on narratives of the migrant- migration is understood as processual instead of a one time decision of re-location. The paper aims to highlight how individuals choose and re-adapt their cultural and social practices within the context of Australia. Migrant narratives are rooted in specific socio-cultural settings of one’s own community as well as the nature of migration to a specific country. Differences and similarities will be observed within the Indian community, and among Muslim refugee women in terms of how identity is negotiated, social networks are re-established in Australia. Some attention will also be paid to difficulties that are being faced by migrants-especially in terms of Muslim identity for Refugee women, particularly in terms of assimilation, building on Ghassan Hage’s use of appraisal theory and how a diversity of language and religion is accommodated within the Indian community. By using two diverse groups, it would be able to identify and contrast migrant experiences.

Keywords: identity, migrant, refugee, women, assimilation, narratives

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Authors: Tizi Hayet, Berrama Tarek, Bounif Nadia

Abstract:

Phenol and its derivatives are organic compounds utilized in the chemical industry. They are introduced into the environment by accidental spills and illegal release of industrial and municipal wastewater. Phenols are organic intermediaries that considered as potential pollutants. Adsorption is one of the purification and separation techniques used in this area. Algeria produces annually 131000 tones of fig; therefore, a large amount of fig leaves is generated, and the conversion of this waste into adsorbent allows the valorization of agricultural residue. The main purpose of this present work is to describe an application of the statistical method for modeling and optimization of the conditions of the phenol (Ph) adsorption from agricultural by-product locally available (fig leaves). The best experimental performance of Ph elimination on the adsorbent was obtained with: Adsorbent concentration (X2) = 0.2 g L-1; Initial concentration (X3) = 150 mg L-1; Speed agitation (X1) = 300 rpm.

Keywords: low-cost adsorbents, fig leaves, full factorial design, phenol, biosorption

Procedia PDF Downloads 97

Authors: Oscar Vega Camacho, Andrea Vargas Guevara, Ellery Rowina Ariza

Abstract:

This paper presents the application of finite dynamic programming, specifically the "Markov Chain" model, as part of the decision making process of a company in the cosmetics sector located in the vicinity of Bogota DC. The objective of this process was to decide whether the company should completely reconstruct its wastewater treatment plant or instead optimize the plant through the addition of equipment. The goal of both of these options was to make the required improvements in order to comply with parameters established by national legislation regarding the treatment of waste before it is released into the environment. This technique will allow the company to select the best option and implement a solution for the processing of waste to minimize environmental damage and the acquisition and implementation costs.

Keywords: decision making, Markov chain, optimization, wastewater

Procedia PDF Downloads 487

Authors: S. Werrlich, E. Eichstetter, K. Nitsche, G. Notni

Abstract:

Augmented Reality (AR) is a strong growing research topic in different training domains such as medicine, sports, military, education and industrial use cases like assembly and maintenance tasks. AR claims to improve the efficiency and skill-transfer of training tasks. This paper gives a comprehensive overview of evaluations using AR for assembly and maintenance training tasks published between 1992 and 2017. We search in a structured way in four different online databases and get 862 results. We select 17 relevant articles focusing on evaluating AR-based training applications for assembly and maintenance tasks. This paper also indicates design guidelines which are necessary for creating a successful application for an AR-based training. We also present five scientific limitations in the field of AR-based training for assembly tasks. Finally, we show our approach to solve current research problems using Design Science Research (DSR).

Keywords: assembly, augmented reality, survey, training

Procedia PDF Downloads 279

Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

Abstract:

The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

Procedia PDF Downloads 75