Search results for: deep networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4583

Search results for: deep networks

2693 Strategies to Achieve Deep Decarbonisation in Power Generation: A Review

Authors: Abdullah Alotaiq

Abstract:

The transition to low-carbon power generation is essential for mitigating climate change and achieving sustainability. This process, however, entails considerable costs, and understanding the factors influencing these costs is critical. This is necessary to cater to the increasing demand for low-carbon electricity across the heating, industry, and transportation sectors. A crucial aspect of this transition is identifying cost-effective and feasible paths for decarbonization, which is integral to global climate mitigation efforts. It is concluded that hybrid solutions, combining different low-carbon technologies, are optimal for minimizing costs and enhancing flexibility. These solutions also address the challenges associated with phasing out existing fossil fuel-based power plants and broadening the spectrum of low-carbon power generation options.

Keywords: review, power generation, energy transition, decarbonisation

Procedia PDF Downloads 58
2692 Impact of PV Distributed Generation on Loop Distribution Network at Saudi Electricity Company Substation in Riyadh City

Authors: Mohammed Alruwaili‬

Abstract:

Nowadays, renewable energy resources are playing an important role in replacing traditional energy resources such as fossil fuels by integrating solar energy with conventional energy. Concerns about the environment led to an intensive search for a renewable energy source. The Rapid growth of distributed energy resources will have prompted increasing interest in the integrated distributing network in the Kingdom of Saudi Arabia next few years, especially after the adoption of new laws and regulations in this regard. Photovoltaic energy is one of the promising renewable energy sources that has grown rapidly worldwide in the past few years and can be used to produce electrical energy through the photovoltaic process. The main objective of the research is to study the impact of PV in distribution networks based on real data and details. In this research, site survey and computer simulation will be dealt with using the well-known computer program software ETAB to simulate the input of electrical distribution lines with other variable inputs such as the levels of solar radiation and the field study that represent the prevailing conditions and conditions in Diriah, Riyadh region, Saudi Arabia. In addition, the impact of adding distributed generation units (DGs) to the distribution network, including solar photovoltaic (PV), will be studied and assessed for the impact of adding different power capacities. The result has been achieved with less power loss in the loop distribution network from the current condition by more than 69% increase in network power loss. However, the studied network contains 78 buses. It is hoped from this research that the efficiency, performance, quality and reliability by having an enhancement in power loss and voltage profile of the distribution networks in Riyadh City. Simulation results prove that the applied method can illustrate the positive impact of PV in loop distribution generation.

Keywords: renewable energy, smart grid, efficiency, distribution network

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2691 The Right of Taiwanese Individuals with Mental Illnesses to Participate in Medical Decision-Making

Authors: Ying-Lun Tseng Chiu-Ying Chen

Abstract:

Taiwan's Mental Health Act was amended at the end of 2022; they added regulations regarding refusing compulsory treatment by patients with mental illnesses. In addition, not only by an examination committee, the judge must also assess the patient's need for compulsory treatment. Additionally, the maximum of compulsory hospitalization has been reduced from an unlimited period to a maximum of 60 days. They aim to promote the healthcare autonomy of individuals with mental illnesses in Taiwan and prevent their silenced voice in medical decision-making while they still possess rationality. Furthermore, they plan to use community support and social care networks to replace the current practice of compulsory treatment in Taiwan. This study uses qualitative research methodology, utilizing interview guidelines to inquire about the experiences of Taiwanese who have undergone compulsory hospitalization, compulsory community treatment, and compulsory medical care. The interviews aimed to explore their feelings when they were subjected to compulsory medical intervention, the inside of their illness, their opinions after treatments, and whether alternative medical interventions proposed by them were considered. Additionally, participants also asked about their personal life history and their support networks in their lives. We collected 12 Taiwanese who had experienced compulsory medical interventions and were interviewed 14 times. The findings indicated that participants still possessed rationality during the onset of their illness. However, when they have other treatments to replace compulsory medical, they sometimes diverge from those of the doctors and their families. Finally, doctors prefer their professional judgment and patients' families' option. Therefore, Taiwanese mental health patients' power of decision-making still needs to improve. Because this research uses qualitative research, so difficult to find participants, and the sample size rate was smaller than Taiwan's population, it may have biases in the analysis. So, Taiwan still has significant progress in enhancing the decision-making rights of participants in the study.

Keywords: medical decision making, compulsory treatment, medical ethics, mental health act

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2690 Faculty Use of Geospatial Tools for Deep Learning in Science and Engineering Courses

Authors: Laura Rodriguez Amaya

Abstract:

Advances in science, technology, engineering, and mathematics (STEM) are viewed as important to countries’ national economies and their capacities to be competitive in the global economy. However, many countries experience low numbers of students entering these disciplines. To strengthen the professional STEM pipelines, it is important that students are retained in these disciplines at universities. Scholars agree that to retain students in universities’ STEM degrees, it is necessary that STEM course content shows the relevance of these academic fields to their daily lives. By increasing students’ understanding on the importance of these degrees and careers, students’ motivation to remain in these academic programs can also increase. An effective way to make STEM content relevant to students’ lives is the use of geospatial technologies and geovisualization in the classroom. The Geospatial Revolution, and the science and technology associated with it, has provided scientists and engineers with an incredible amount of data about Earth and Earth systems. This data can be used in the classroom to support instruction and make content relevant to all students. The purpose of this study was to find out the prevalence use of geospatial technologies and geovisualization as teaching practices in a USA university. The Teaching Practices Inventory survey, which is a modified version of the Carl Wieman Science Education Initiative Teaching Practices Inventory, was selected for the study. Faculty in the STEM disciplines that participated in a summer learning institute at a 4-year university in the USA constituted the population selected for the study. One of the summer learning institute’s main purpose was to have an impact on the teaching of STEM courses, particularly the teaching of gateway courses taken by many STEM majors. The sample population for the study is 97.5 of the total number of summer learning institute participants. Basic descriptive statistics through the Statistical Package for the Social Sciences (SPSS) were performed to find out: 1) The percentage of faculty using geospatial technologies and geovisualization; 2) Did the faculty associated department impact their use of geospatial tools?; and 3) Did the number of years in a teaching capacity impact their use of geospatial tools? Findings indicate that only 10 percent of respondents had used geospatial technologies, and 18 percent had used geospatial visualization. In addition, the use of geovisualization among faculty of different disciplines was broader than the use of geospatial technologies. The use of geospatial technologies concentrated in the engineering departments. Data seems to indicate the lack of incorporation of geospatial tools in STEM education. The use of geospatial tools is an effective way to engage students in deep STEM learning. Future research should look at the effect on student learning and retention in science and engineering programs when geospatial tools are used.

Keywords: engineering education, geospatial technology, geovisualization, STEM

Procedia PDF Downloads 255
2689 Prediction of Terrorist Activities in Nigeria using Bayesian Neural Network with Heterogeneous Transfer Functions

Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

Abstract:

Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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2688 Assessment of Image Databases Used for Human Skin Detection Methods

Authors: Saleh Alshehri

Abstract:

Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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2687 Nanofluidic Cell for Resolution Improvement of Liquid Transmission Electron Microscopy

Authors: Deybith Venegas-Rojas, Sercan Keskin, Svenja Riekeberg, Sana Azim, Stephanie Manz, R. J. Dwayne Miller, Hoc Khiem Trieu

Abstract:

Liquid Transmission Electron Microscopy (TEM) is a growing area with a broad range of applications from physics and chemistry to material engineering and biology, in which it is possible to image in-situ unseen phenomena. For this, a nanofluidic device is used to insert the nanoflow with the sample inside the microscope in order to keep the liquid encapsulated because of the high vacuum. In the last years, Si3N4 windows have been widely used because of its mechanical stability and low imaging contrast. Nevertheless, the pressure difference between the inside fluid and the outside vacuum in the TEM generates bulging in the windows. This increases the imaged fluid volume, which decreases the signal to noise ratio (SNR), limiting the achievable spatial resolution. With the proposed device, the membrane is fortified with a microstructure capable of stand higher pressure differences, and almost removing completely the bulging. A theoretical study is presented with Finite Element Method (FEM) simulations which provide a deep understanding of the membrane mechanical conditions and proves the effectiveness of this novel concept. Bulging and von Mises Stress were studied for different membrane dimensions, geometries, materials, and thicknesses. The microfabrication of the device was made with a thin wafer coated with thin layers of SiO2 and Si3N4. After the lithography process, these layers were etched (reactive ion etching and buffered oxide etch (BOE) respectively). After that, the microstructure was etched (deep reactive ion etching). Then the back side SiO2 was etched (BOE) and the array of free-standing micro-windows was obtained. Additionally, a Pyrex wafer was patterned with windows, and inlets/outlets, and bonded (anodic bonding) to the Si side to facilitate the thin wafer handling. Later, a thin spacer is sputtered and patterned with microchannels and trenches to guide the nanoflow with the samples. This approach reduces considerably the common bulging problem of the window, improving the SNR, contrast and spatial resolution, increasing substantially the mechanical stability of the windows, allowing a larger viewing area. These developments lead to a wider range of applications of liquid TEM, expanding the spectrum of possible experiments in the field.

Keywords: liquid cell, liquid transmission electron microscopy, nanofluidics, nanofluidic cell, thin films

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2686 The Role of Artificial Intelligence Algorithms in Psychiatry: Advancing Diagnosis and Treatment

Authors: Netanel Stern

Abstract:

Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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2685 Analyzing Impacts of Road Network on Vegetation Using Geographic Information System and Remote Sensing Techniques

Authors: Elizabeth Malebogo Mosepele

Abstract:

Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

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2684 American Sign Language Recognition System

Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

Abstract:

The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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2683 Algorithmic Skills Transferred from Secondary CSI Studies into Tertiary Education

Authors: Piroska Biró, Mária Csernoch, János Máth, Kálmán Abari

Abstract:

Testing the first year students of Informatics at the University of Debrecen revealed that students start their tertiary studies in programming with a low level of programming knowledge and algorithmic skills. The possible reasons which lead the students to this very unfortunate result were examined. The results of the test were compared to the students’ results in the school leaving exams and to their self-assessment values. It was found that there is only a slight connection between the students’ results in the test and in the school leaving exams, especially at intermediate level. Beyond this, the school leaving exams do not seem to enable students to evaluate their own abilities.

Keywords: deep and surface approaches, metacognitive abilities, programming and algorithmic skills, school leaving exams, tracking code

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2682 Sustainable Tourism Management in Taiwan: Using Certification and KPI Indicators to Development Sustainable Tourism Experiences

Authors: Shirley Kuo

Abstract:

The main purpose of this study is to develop sustainable indicators for Taiwan, and using the Delphi method to find that our tourist areas can progress in a sustainable way. We need a lot of infrastructures and policies to develop tourist areas, and with proper KPI indicators can reduce the destruction of the natural and ecological environment. This study will first study the foreign certification experiences, because Taiwan is currently in the development stage, and then the methodology will explain in-depth interviews using the Delphi method, and then there is discussion about which KPI indicators Taiwan currently needs. In this study current progress is a deep understanding of national sustainable tourism certification and KPI indicators.

Keywords: sustainable tourism, certification, KPI indicators, Delphi method

Procedia PDF Downloads 335
2681 Comparing Deep Architectures for Selecting Optimal Machine Translation

Authors: Despoina Mouratidis, Katia Lida Kermanidis

Abstract:

Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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2680 Comparison of Machine Learning-Based Models for Predicting Streptococcus pyogenes Virulence Factors and Antimicrobial Resistance

Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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2679 Numerical Investigation of Multiphase Flow Structure for the Flue Gas Desulfurization

Authors: Cheng-Jui Li, Chien-Chou Tseng

Abstract:

This study adopts Computational Fluid Dynamics (CFD) technique to build the multiphase flow numerical model where the interface between the flue gas and desulfurization liquid can be traced by Eulerian-Eulerian model. Inside the tower, the contact of the desulfurization liquid flow from the spray nozzles and flue gas flow can trigger chemical reactions to remove the sulfur dioxide from the exhaust gas. From experimental observations of the industrial scale plant, the desulfurization mechanism depends on the mixing level between the flue gas and the desulfurization liquid. In order to significantly improve the desulfurization efficiency, the mixing efficiency and the residence time can be increased by perforated sieve trays. Hence, the purpose of this research is to investigate the flow structure of sieve trays for the flue gas desulfurization by numerical simulation. In this study, there is an outlet at the top of FGD tower to discharge the clean gas and the FGD tower has a deep tank at the bottom, which is used to collect the slurry liquid. In the major desulfurization zone, the desulfurization liquid and flue gas have a complex mixing flow. Because there are four perforated plates in the major desulfurization zone, which spaced 0.4m from each other, and the spray array is placed above the top sieve tray, which includes 33 nozzles. Each nozzle injects desulfurization liquid that consists of the Mg(OH)2 solution. On each sieve tray, the outside diameter, the hole diameter, and the porosity are 0.6m, 20 mm and 34.3%. The flue gas flows into the FGD tower from the space between the major desulfurization zone and the deep tank can finally become clean. The desulfurization liquid and the liquid slurry goes to the bottom tank and is discharged as waste. When the desulfurization solution flow impacts the sieve tray, the downward momentum will be converted to the upper surface of the sieve tray. As a result, a thin liquid layer can be developed above the sieve tray, which is the so-called the slurry layer. And the volume fraction value within the slurry layer is around 0.3~0.7. Therefore, the liquid phase can't be considered as a discrete phase under the Eulerian-Lagrangian framework. Besides, there is a liquid column through the sieve trays. The downward liquid column becomes narrow as it interacts with the upward gas flow. After the flue gas flows into the major desulfurization zone, the flow direction of the flue gas is upward (+y) in the tube between the liquid column and the solid boundary of the FGD tower. As a result, the flue gas near the liquid column may be rolled down to slurry layer, which developed a vortex or a circulation zone between any two sieve trays. The vortex structure between two sieve trays results in a sufficient large two-phase contact area. It also increases the number of times that the flue gas interacts with the desulfurization liquid. On the other hand, the sieve trays improve the two-phase mixing, which may improve the SO2 removal efficiency.

Keywords: Computational Fluid Dynamics (CFD), Eulerian-Eulerian Model, Flue Gas Desulfurization (FGD), perforated sieve tray

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2678 Resale Housing Development Board Price Prediction Considering Covid-19 through Sentiment Analysis

Authors: Srinaath Anbu Durai, Wang Zhaoxia

Abstract:

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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2677 Housing Precarity and Pathways: Lived Experiences Among Bangladeshi Migrants in Dublin

Authors: Mohammad Altaf Hossain

Abstract:

A growing body of literature in urban studies has presented that urban precarity has been a lived experience for low-income groups of people in the cities of the Global South. It does not necessarily mean that cities in the Global North, where advanced capitalist economies exist, avoided the adverse realities of urban precarity. As a multifaceted condition, it creates other associated precariousness in lives -for example, economic deprivation, mental stress, and housing precarity. The interrelations between urbanity and precarity have been ubiquitous regardless of the developed and developing countries. People, mainly manual labourers with low incomes, go through uncertainties in every aspect of life. By analysing qualitative data and embracing structure-agency interaction, this paper intends to present how Bangladeshi migrants experience housing precarity in Dublin. Continued population growth and political economy factors such as labour market inequality, financialisation of the private rental sector, and the impact of cuts to government funding for social housing provision are combined to produce a housing supply crisis, affordability, and access in the city. As a result, low-income people practice informality in securing jobs and housing. The macro-structural components of this analysis include the Irish housing policy, the European labour market, the immigration policy, and the financialised housing market. The micro-structural components of South Asian communities’ experiences include social networks and social class. Access to social networks and practices of informality play a significant role in enabling them to negotiate urban precarity, including housing crises and income insecurity. In some cases, the collective agency of ethnic diaspora communities plays a vital role in negotiating with structural constraints.

Keywords: housing precarity, housing pathways, migration, agency, Dublin

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2676 Efficient Backup Protection for Hybrid WDM/TDM GPON System

Authors: Elmahdi Mohammadine, Ahouzi Esmail, Najid Abdellah

Abstract:

This contribution aims to present a new protected hybrid WDM/TDM PON architecture using Wavelength Selective Switches and Optical Line Protection devices. The objective from using these technologies is to improve flexibility and enhance the protection of GPON networks.

Keywords: Wavlenght Division Multiplexed Passive Optical Network (WDM-PON), Time Division Multiplexed PON (TDM-PON), architecture, Protection, Wavelength Selective Switches (WSS), Optical Line Protection (OLP)

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2675 Protection Plan of Medium Voltage Distribution Network in Tunisia

Authors: S. Chebbi, A. Meddeb

Abstract:

The distribution networks are often exposed to harmful incidents which can halt the electricity supply of the customer. In this context, we studied a real case of a critical zone of the Tunisian network which is currently characterized by the dysfunction of its plan of protection. In this paper, we were interested in the harmonization of the protection plan settings in order to ensure a perfect selectivity and a better continuity of service on the whole of the network.

Keywords: distribution network Gabes-Tunisia, continuity of service, protection plan settings, selectivity

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2674 3D Model Completion Based on Similarity Search with Slim-Tree

Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

Abstract:

With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

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2673 Debate, Discontent and National Identity in a Secular State

Authors: Man Bahadur Shahu

Abstract:

The secularism is a controversial, debatable and misinterpreted issue since its endorsement in the 2007 constitution in Nepal. The unprecedented acts have been seen favoring and disfavoring against the secularism within the public domain—which creates the fallacies and suspicions in the rationalization and modernization process. This paper highlights three important points: first, the secularization suddenly ruptures the silence and institutional decline of religion within the state. Second, state effort on secularism simultaneously fosters the state neutrality and state separation from religious institutions that amplify the recognition of all religious groups through the equal treatment in their festivity, rituals, and practices. Third, no state would completely secular because of their deep-rooted mindset and disposition with their own religious faiths and beliefs that largely enhance intergroup conflict, dispute, riot and turbulence in post-secular period in the name of proselytizing and conversion.

Keywords: conflict, proselytizing, religion, secular

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2672 Modeling and Simulation of Underwater Flexible Manipulator as Raleigh Beam Using Bond Graph

Authors: Sumit Kumar, Sunil Kumar, Chandan Deep Singh

Abstract:

This paper presents modeling and simulation of flexible robot in an underwater environment. The underwater environment completely contrasts with ground or space environment. The robot in an underwater situation is subjected to various dynamic forces like buoyancy forces, hydrostatic and hydrodynamic forces. The underwater robot is modeled as Rayleigh beam. The developed model further allows estimating the deflection of tip in two directions. The complete dynamics of the underwater robot is analyzed, which is the main focus of this investigation. The control of robot trajectory is not discussed in this paper. Simulation is performed using Symbol Shakti software.

Keywords: bond graph modeling, dynamics. modeling, rayleigh beam, underwater robot

Procedia PDF Downloads 588
2671 Older Consumer’s Willingness to Trust Social Media Advertising: An Australian Case

Authors: Simon J. Wilde, David M. Herold, Michael J. Bryant

Abstract:

Social media networks have become the hotbed for advertising activities, due mainly to their increasing consumer/user base, and secondly, owing to the ability of marketers to accurately measure ad exposure and consumer-based insights on such networks. More than half of the world’s population (4.8 billion) now uses social media (60%), with 150 million new users having come online within the last 12 months (to June 2022). As the use of social media networks by users grows, key business strategies used for interacting with these potential customers have matured, especially social media advertising. Unlike other traditional media outlets, social media advertising is highly interactive and digital channel-specific. Social media advertisements are clearly targetable, providing marketers with an extremely powerful marketing tool. Yet despite the measurable benefits afforded to businesses engaged in social media advertising, recent controversies (such as the relationship between Facebook and Cambridge Analytica in 2018) have only heightened the role trust and privacy play within these social media networks. The purpose of this exploratory paper is to investigate the extent to which social media users trust social media advertising. Understanding this relationship will fundamentally assist marketers in better understanding social media interactions and their implications for society. Using a web-based quantitative survey instrument, survey participants were recruited via a reputable online panel survey site. Respondents to the survey represented social media users from all states and territories within Australia. Completed responses were received from a total of 258 social media users. Survey respondents represented all core age demographic groupings, including Gen Z/Millennials (18-45 years = 60.5% of respondents) and Gen X/Boomers (46-66+ years = 39.5% of respondents). An adapted ADTRUST scale, using a 20 item 7-point Likert scale, measured trust in social media advertising. The ADTRUST scale has been shown to be a valid measure of trust in advertising within traditional different media, such as broadcast media and print media, and more recently, the Internet (as a broader platform). The adapted scale was validated through exploratory factor analysis (EFA), resulting in a three-factor solution. These three factors were named reliability, usefulness and affect, and the willingness to rely on. Factor scores (weighted measures) were then calculated for these factors. Factor scores are estimates of the scores survey participants would have received on each of the factors had they been measured directly, with the following results recorded (Reliability = 4.68/7; Usefulness and Affect = 4.53/7; and Willingness to Rely On = 3.94/7). Further statistical analysis (independent samples t-test) determined the difference in factor scores between the factors when age (Gen Z/Millennials vs. Gen X/Boomers) was utilised as the independent, categorical variable. The results showed the difference in mean scores across all three factors to be statistically significant (p<0.05) for these two core age groupings: Gen Z/Millennials Reliability = 4.90/7 vs Gen X/Boomers Reliability = 4.34/7; Gen Z/Millennials Usefulness and Affect = 4.85/7 vs Gen X/Boomers Usefulness and Affect = 4.05/7; and Gen Z/Millennials Willingness to Rely On = 4.53/7 vs Gen X/Boomers Willingness to Rely On = 3.03/7. The results clearly indicate that older social media users lack trust in the quality of information conveyed in social media ads, when compared to younger, more social media-savvy consumers. This is especially evident with respect to Factor 3 (Willingness to Rely On), whose underlying variables reflect one’s behavioural intent to act based on the information conveyed in advertising. These findings can be useful to marketers, advertisers, and brand managers in that the results highlight a critical need to design ‘authentic’ advertisements on social media sites to better connect with these older users, in an attempt to foster positive behavioural responses from within this large demographic group – whose engagement with social media sites continues to increase year on year.

Keywords: social media advertising, trust, older consumers, online

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2670 Electrochemical Corrosion and Mechanical Properties of Structural Materials for Oil and Gas Applications in Simulated Deep-Sea Well Environments

Authors: Turin Datta, Kisor K. Sahu

Abstract:

Structural materials used in today’s oil and gas exploration and drilling of both onshore and offshore oil and gas wells must possess superior tensile properties, excellent resistance to corrosive degradation that includes general, localized (pitting and crevice) and environment assisted cracking such as stress corrosion cracking and hydrogen embrittlement. The High Pressure and High Temperature (HPHT) wells are typically operated at temperature and pressure that can exceed 300-3500F and 10,000psi (69MPa) respectively which necessitates the use of exotic materials in these exotic sources of natural resources. This research investigation is focussed on the evaluation of tensile properties and corrosion behavior of AISI 4140 High-Strength Low Alloy Steel (HSLA) possessing tempered martensitic microstructure and Duplex 2205 Stainless Steel (DSS) having austenitic and ferritic phase. The selection of this two alloys are primarily based on economic considerations as 4140 HSLA is cheaper when compared to DSS 2205. Due to the harsh aggressive chemical species encountered in deep oil and gas wells like chloride ions (Cl-), carbon dioxide (CO2), hydrogen sulphide (H2S) along with other mineral organic acids, DSS 2205, having a dual-phase microstructure can mitigate the degradation resulting from the presence of both chloride ions (Cl-) and hydrogen simultaneously. Tensile properties evaluation indicates a ductile failure of DSS 2205 whereas 4140 HSLA exhibit quasi-cleavage fracture due to the phenomenon of ‘tempered martensitic embrittlement’. From the potentiodynamic polarization testing, it is observed that DSS 2205 has higher corrosion resistance than 4140 HSLA; the former exhibits passivity signifying resistance to localized corrosion while the latter exhibits active dissolution in all the environmental parameters space that was tested. From the Scanning Electron Microscopy (SEM) evaluation, it is understood that stable pits appear in DSS 2205 only when the temperature exceeds the critical pitting temperature (CPT). SEM observation of the corroded 4140 HSLA specimen tested in aqueous 3.5 wt.% NaCl solution reveals intergranular cracking which appears due to the adsorption and diffusion of hydrogen during polarization, thus, causing hydrogen-induced cracking/hydrogen embrittlement. General corrosion testing of DSS 2205 in acidic brine (pH~3.0) solution at ambient temperature using coupons indicate no weight loss even after three months whereas the corrosion rate of AISI 4140 HSLA is significantly higher after one month of testing.

Keywords: DSS 2205, polarization, pitting, SEM

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2669 A Socio-Spatial Analysis of Financialization and the Formation of Oligopolies in Brazilian Basic Education

Authors: Gleyce Assis Da Silva Barbosa

Abstract:

In recent years, we have witnessed a vertiginous growth of large education companies. Daughters of national and world capital, these companies expand both through consolidated physical networks in the form of branches spread across the territory and through institutional networks such as business networks through mergers, acquisitions, creation of new companies and influence. They do this by incorporating small, medium and large schools and universities, teaching systems and other products and services. They are also able to weave their webs directly or indirectly in philanthropic circles, limited partnerships, family businesses and even in public education through various mechanisms of outsourcing, privatization and commercialization of products for the sector. Although the growth of these groups in basic education seems to us a recent phenomenon in peripheral countries such as Brazil, its diffusion is closely linked to higher education conglomerates and other sectors of the economy forming oligopolies, which began to expand in the 1990s with strong state support and through political reforms that redefined its role, transforming it into a fundamental agent in the formation of guidelines to boost the incorporation of neoliberal logic. This expansion occurred through the objectification of education, commodifying it and transforming students into consumer clients. Financial power combined with the neo-liberalization of state public policies allowed the profusion of social exclusion, the increase of individuals without access to basic services, deindustrialization, automation, capital volatility and the indetermination of the economy; in addition, this process causes capital to be valued and devalued at rates never seen before, which together generates various impacts such as the precariousness of work. Understanding the connection between these processes, which engender the economy, allows us to see their consequences in labor relations and in the territory. In this sense, it is necessary to analyze the geographic-economic context and the role of the facilitating agents of this process, which can give us clues about the ongoing transformations and the directions of education in the national and even international scenario since this process is linked to the multiple scales of financial globalization. Therefore, the present research has the general objective of analyzing the socio-spatial impacts of financialization and the formation of oligopolies in Brazilian basic education. For this, the survey of laws, data, and public policies on the subject in question was used as a methodology. As a methodology, the work was based on some data from these companies available on websites for investors. Survey of information from global and national companies that operate in Brazilian basic education. In addition to mapping the expansion of educational oligopolies using public data on the location of schools. With this, the research intends to provide information about the ongoing commodification process in the country. Discuss the consequences of the oligopolization of education, considering the impacts that financialization can bring to teaching work.

Keywords: financialization, oligopolies, education, Brazil

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2668 GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts

Authors: Lin Cheng, Zijiang Yang

Abstract:

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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2667 New Outlook of the World in the Perspective of Huayan School of Buddhism

Authors: Kao-Cheng Huang

Abstract:

In the Huayan School, the Buddhist contemplations of true emptiness (Sanskrit: Sunyata), non-obstruction of noumena and phenomena, and universal coherence offer a deep framework for integrating environmental sustainability with spiritual development. Those teachings point to a way in which ecological awareness and spiritual enlightenment are not independent goals but complementary dimensions of realizing the true nature of reality. The contemplation of true emptiness reveals that neither environmental systems nor human consciousness have an independent existence but arise in dependence on interdependent conditions. This insight would challenge any materialistic exploitation of nature and any purely transcendent spirituality, pointing to the middle way where sustainable practices emerge from a deep recognition of our inseparability from the natural world. Just as form cannot be separated from emptiness, human development cannot be separated from ecological harmony. Next, the non-obstruction of noumena and phenomena illuminates how universal principles (like ecological laws and spiritual truths) and their specific manifestations (like ecosystems and individual spiritual practices) mutually reveal and fulfill each other. It teaches us that authentic spiritual development necessarily includes environmental stewardship, while sustainable practices can serve as vehicles for spiritual awakening. Like waves revealing the nature of water, our interaction with nature can reveal the ultimate truth. Then, contemplation of universal coherence, especially through its perspectives of mutual containment and interpenetration, reveals how individual spiritual transformation and collective environmental action are inherently united. Just like every dust particle contains the whole universe, every sustainable action reflects and contributes to both ecological harmony and spiritual awakening. This ultimately leads to universal fusion, whereby environmental responsibility and spiritual practice become indistinguishable aspects of recognizing reality's interconnected nature. These reflections provide a theoretical basis for approaches to sustainability that integrate outer transformation with inner development, suggesting true solutions to environmental challenges have to come from and lead to a deeper spiritual understanding of our fundamental interconnectedness with all phenomena. This paper also outlines practical applications of spiritual enlightenment in environmental sustainability across three levels: individual, community, and scientific domains. These interconnected approaches create a synergistic model where spiritual insights enhance environmental action while environmental stewardship deepens spiritual understanding. This reciprocal relationship reflects a fundamental truth: we are simultaneously part of the world, and the world is part of us.

Keywords: Huayan, sustainability, Sunyata, non-obstruction of noumena and phenomena

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2666 Effect of Base Coarse Layer on Load-Settlement Characteristics of Sandy Subgrade Using Plate Load Test

Authors: A. Nazeri, R. Ziaie Moayed, H. Ghiasinejad

Abstract:

The present research has been performed to investigate the effect of base course application on load-settlement characteristics of sandy subgrade using plate load test. The main parameter investigated in this study was the subgrade reaction coefficient. The model tests were conducted in a 1.35 m long, 1 m wide, and 1 m deep steel test box of Imam Khomeini International University (IKIU Calibration Chamber). The base courses used in this research were in three different thicknesses of 15 cm, 20 cm, and 30 cm. The test results indicated that in the case of using base course over loose sandy subgrade, the values of subgrade reaction coefficient can be increased from 7  to 132 , 224 , and 396  in presence of 15 cm, 20 cm, and 30 cm base course, respectively.

Keywords: modulus of subgrade reaction, plate load test, base course, sandy subgrade

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2665 The Kafrah Dam (The Oldest Dam in History)

Authors: Mohamed Bekhit Gad Khalil

Abstract:

This dam is the oldest dam in history. It was built by the ancient Egyptian around (2650 B.C) control flooding. It is believed to have been built between the third and fourth dynasties .It contains the oldest dam in history. Many studies have been conducted for the dam. This report was prepared under my supervision and in cooperation with the Ministry of Tourism and Antiquities. The dam was re-documented and photographed again. The dam on the northern side Consists of irregularly shaped stones of varying sizes used randomly. Sand and soil fill the gaps between the stones. creating layers to form the body of the dam. The eastern. side of the dam Consists of a series of regular shaped stones that have been cut and constructed into a stepped pyramid-like structure with width of (15,7) meters and height of (10) meters. The surface has significant erosion and wear on the stones due to weather Conditions. which has resulted in deep cavities in most of the stone blocks forming the surface.

Keywords: ministry of tourism and antiquities, excavations, registration, documentation

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2664 Development of Evolutionary Algorithm by Combining Optimization and Imitation Approach for Machine Learning in Gaming

Authors: Rohit Mittal, Bright Keswani, Amit Mithal

Abstract:

This paper provides a sense about the application of computational intelligence techniques used to develop computer games, especially car racing. For the deep sense and knowledge of artificial intelligence, this paper is divided into various sections that is optimization, imitation, innovation and combining approach of optimization and imitation. This paper is mainly concerned with combining approach which tells different aspects of using fitness measures and supervised learning techniques used to imitate aspects of behavior. The main achievement of this paper is based on modelling player behaviour and evolving new game content such as racing tracks as single car racing on single track.

Keywords: evolution algorithm, genetic, optimization, imitation, racing, innovation, gaming

Procedia PDF Downloads 650