Search results for: artificial communication

Authors: Adebomi M. Oyewumi, Sunday Amaize

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The study was designed to examine the barriers to marital expectations among unmarried persons with hearing impairment in Oyo State, Nigeria. Descriptive survey research design was adopted. Purposive sampling technique was used to select one hundred participants made up forty-four (44) males and fifty-six (56) females, all with varying degrees of hearing impairment. Eight research questions were raised and answered. The instrument used was Marital Expectations Scale with reliability coefficient of 0.86. Data was analyzed using descriptive statistics tools of frequency count and simple percentage as well as inferential statistics tools of T-TEST and ANOVA. The findings revealed that there was a significant relationship existing among the main identified barriers (environmental barrier, communication barrier, hearing loss, unemployment and poor sexuality education) to the marital expectations of unmarried persons with hearing impairment. The joint contribution of the independent variables (identified barriers) to the dependent variable (marital expectations) was significant, F = 5.842, P < 0.05, accounting for about 89% of the variance. The relative contribution of the identified barriers to marital expectations of unmarried persons with hearing impairment is as follows: environmental barrier (β = 0.808, t = 5.176, P < 0.05), communication barrier (β = 0.533, t = 3.305, P < 0.05), hearing loss (β = 0.550, t = 2.233, P < 0.05), unemployment (β = 0.431, t = 2.102, P < 0.05), poor sexuality education (β = 0.361, t = 1.985, P < 0.05). Environmental barrier proved to be the most potent contributor to the poor marital expectations among unmarried persons with hearing impairment. Therefore, it is recommended that society dismantles the nagging environmental barrier through positive identification with individuals suffering from hearing impairment. In this connection, members of society should change their negative attitudes and do away with all the wrong notions about the marital ability of individuals with hearing impairment.

Keywords: environmental barrier, hearing impairment, marriage, marital expectations

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Hasna Mekkaoui Alaoui, Mariem Mekkaoui Alaoui

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In a context marked by a growing and pressing demand for online training within Moroccan universities, massive open online courses (Moocs) are undergoing constant evolution, amplified by the widespread use of digital technology and accentuated by the Coronavirus pandemic. However, despite their growing popularity and expansion, these courses are still lacking in terms of tools, enabling teachers and researchers to carry out a fine-grained analysis of the learning processes taking place within them. What's more, the circulation and sharing of knowledge within these environments is becoming increasingly important. The crucial aspect of traceability emerges here, as MOOCs record and generate traces from the most minute to the most visible. This leads us to consider traceability as a valuable approach in the field of educational research, where the trace is envisaged as a research tool in its own right. In this exploratory research project, we are looking at aspects of community knowledge sharing based on traces observed in the "Communication des organisations" Mooc. Focusing in particular on the mediating trace and its impact in identifying knowledge circulation processes in this learning space, we have mobilized the traces of video capsules as an index of knowledge circulation in the Mooc device. Our study uses a methodological approach based on thematic analysis, and although the results show that learners reproduce knowledge from different video vignettes in almost identical ways, they do not limit themselves to the knowledge provided to them. This research offers concrete perspectives for improving the dynamics of online devices, with a potentially positive impact on the quality of online university teaching.

Keywords: circulation, index, digital environments, mediation., trace

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Juan Pablo Bertuzzi, Mauro Chiarella

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Employing a communication studies perspective and a socio-technological approach, this paper introduces a theoretical framework for understanding the concept of hybrid world; the avatarization phenomena; and the communicational archetype of co-hybridization. This analysis intends to make a contribution to future design of virtual reality experimental applications. Ultimately, this paper presents an ongoing research project that proposes the study of human-avatar interactions in digital educational environments, as well as an innovative reflection on inner digital communication. The aforementioned project presents the analysis of human-avatar interactions, through the development of an interactive experience in virtual reality. The goal is to generate an innovative communicational dimension that could reinforce the hypotheses presented throughout this paper. Being thought for its initial application in educational environments, the analysis and results of this research are dependent and have been prepared in regard of a meticulous planning of: the conception of a 3D digital platform; the interactive game objects; the AI or computer avatars; the human representation as hybrid avatars; and lastly, the potential of immersion, ergonomics and control diversity that can provide the virtual reality system and the game engine that were chosen. The project is divided in two main axes: The first part is the structural one, as it is mandatory for the construction of an original prototype. The 3D model is inspired by the physical space that belongs to an academic institution. The incorporation of smart objects, avatars, game mechanics, game objects, and a dialogue system will be part of the prototype. These elements have all the objective of gamifying the educational environment. To generate a continuous participation and a large amount of interactions, the digital world will be navigable both, in a conventional device and in a virtual reality system. This decision is made, practically, to facilitate the communication between students and teachers; and strategically, because it will help to a faster population of the digital environment. The second part is concentrated to content production and further data analysis. The challenge is to offer a scenario’s diversity that compels users to interact and to question their digital embodiment. The multipath narrative content that is being applied is focused on the subjects covered in this paper. Furthermore, the experience with virtual reality devices proposes users to experiment in a mixture of a seemingly infinite digital world and a small physical area of movement. This combination will lead the narrative content and it will be crucial in order to restrict user’s interactions. The main point is to stimulate and to grow in the user the need of his hybrid avatar’s help. By building an inner communication between user’s physicality and user’s digital extension, the interactions will serve as a self-guide through the gameworld. This is the first attempt to make explicit the avatarization phenomena and to further analyze the communicational archetype of co-hybridization. The challenge of the upcoming years will be to take advantage from these forms of generalized avatarization, in order to create awareness and establish innovative forms of hybridization.

Keywords: avatar, hybrid worlds, socio-technology, virtual reality

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Authors: Silvia Moreno, Banu Iyisan, Hannes Gumz, Brigitte Voit, Dietmar Appelhans

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Polymersomes are materials which are considered as artificial counterparts of natural vesicles. The nanotechnology of such smart nanovesicles is very useful to enhance the efficiency of many therapeutic and diagnostic drugs. Those compounds show a higher stability, flexibility, and mechanical strength to the membrane compared to natural liposomes. In addition, they can be designed in detail, the permeability of the membrane can be controlled by different stimuli, and the surface can be functionalized with different biological molecules to facilitate monitoring and target. For this purpose, this study demonstrates the formation of multifunctional and pH sensitive polymersomes and their functionalization with different reactive groups or biomolecules inside and outside of polymersomes´ membrane providing by crossing the membrane and docking/undocking processes for biomedical applications. Overall, they are highly versatile and thus present new opportunities for the design of targeted and selective recognition systems, for example, in mimicking cell functions and in synthetic biology.

Keywords: multifunctionalized, pH stimulus, controllable release, cellular uptake

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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Authors: Akber Oumer Abdurezak

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Road traffic accident is a major problem in Ethiopia, resulting in the deaths of many people and potential injuries and crash every year and loss of properties. According to the Federal Transport Authority, one of the main causes of traffic accident and crash in Ethiopia is over speeding. Implementation of different technologies is used to monitor the speed of vehicles in order to minimize accidents and crashes. This research aimed at designing a speed monitoring system to monitor the speed of travelling vehicles and movements, reporting illegal speeds or overspeeding vehicles to the concerned bodies. The implementation of the system is through a wireless sensor network. The proposed system can sense and detect the movement of vehicles, process, and analysis the data obtained from the sensor and the cloud system. The data is sent to the central controlling server. The system contains accelerometer and gyroscope sensors to sense and collect the data of the vehicle. Arduino to process the data and Global System for Mobile Communication (GSM) module for communication purposes to send the data to the concerned body. When the speed of the vehicle exceeds the allowable speed limit, the system sends a message to database as “over speeding”. Both accelerometer and gyroscope sensors are used to collect acceleration data. The acceleration data then convert to speed, and the corresponding speed is checked with the speed limit, and those above the speed limit are reported to the concerned authorities to avoid frequent accidents. The proposed system decreases the occurrence of accidents and crashes due to overspeeding and can be used as an eye opener for the implementation of other intelligent transport system technologies. This system can also integrate with other technologies like GPS and Google Maps to obtain better output.

Keywords: accelerometer, IOT, GSM, gyroscope

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Sarah Alnafea, Basmah Alalsheikh, Hadab Alkathiri

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For over 30 years, AutoCAD, a powerful CAD software developed by Autodesk, has been an imperative need for design in industries such as engineering, building, and architecture. With the emerge of advanced technology, AutoCAD has undergone a revolutionary change with the involvement of artificial intelligence capabilities that have enhanced the productivity and efficiency at work and quality in the design for the users. This paper investigates the role AI in AutoCAD, especially in intelligent automation, generative design, automated design ideas, natural language processing, and predictive analytics. To identify further, A survey among users was also conducted to assess the adoption and satisfaction of AI features and identify areas for improvement. The Competitive standing of AutoCAD is further crosschecked against other AI-enabled CAD software packages, including SolidWorks, Fusion 360, and Rhino.In this paper, an overview of the current impacts of AI in AutoCAD is given, along with some recommendations for the future road of AI development to meet users’ requirements

Keywords: artificail inteligence, natural language proccesing, intelligent automation, generative design

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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Authors: Colin Schmidt, Adrien Lecossier, Pascal Crubleau, Philippe Blanchard, Simon Richir

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Introduction:The widespread availability of artificial intelligence, exemplified by Generative Pre-trained Transformers (GPT) relying on large language models (LLM), has caused a seismic shift in the realm of knowledge. Everyone now has the capacity to swiftly learn how these models can either serve them well or not. Today, conversational AI like ChatGPT is grounded in neural transformer models, a significant advance in natural language processing facilitated by the emergence of renowned LLMs constructed using neural transformer architecture. Inventiveness of an LLM : OpenAI's GPT-3 stands as a premier LLM, capable of handling a broad spectrum of natural language processing tasks without requiring fine-tuning, reliably producing text that reads as if authored by humans. However, even with an understanding of how LLMs respond to questions asked, there may be lurking behind OpenAI’s seemingly endless responses an inventive model yet to be uncovered. There may be some unforeseen reasoning emerging from the interconnection of neural networks here. Just as a Soviet researcher in the 1940s questioned the existence of Common factors in inventions, enabling an Under standing of how and according to what principles humans create them, it is equally legitimate today to explore whether solutions provided by LLMs to complex problems also share common denominators. Theory of Inventive Problem Solving (TRIZ) : We will revisit some fundamentals of TRIZ and how Genrich ALTSHULLER was inspired by the idea that inventions and innovations are essential means to solve societal problems. It's crucial to note that traditional problem-solving methods often fall short in discovering innovative solutions. The design team is frequently hampered by psychological barriers stemming from confinement within a highly specialized knowledge domain that is difficult to question. We presume ChatGPT Utilizes TRIZ 40. Hence, the objective of this research is to decipher the inventive model of LLMs, particularly that of ChatGPT, through a comparative study. This will enhance the efficiency of sustainable innovation processes and shed light on how the construction of a solution to a complex problem was devised. Description of the Experimental Protocol : To confirm or reject our main hypothesis that is to determine whether ChatGPT uses TRIZ, we will follow a stringent protocol that we will detail, drawing on insights from a panel of two TRIZ experts. Conclusion and Future Directions : In this endeavor, we sought to comprehend how an LLM like GPT addresses complex challenges. Our goal was to analyze the inventive model of responses provided by an LLM, specifically ChatGPT, by comparing it to an existing standard model: TRIZ 40. Of course, problem solving is our main focus in our endeavours.

Keywords: artificial intelligence, Triz, ChatGPT, inventiveness, problem-solving

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Authors: G. C. Almiranez, J. Mercado, L. U. Aumentado, J. M. Mahaguay, J. P. Cruz, M. L. Saballe

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This research project focused on the development of an application that aids the university administrators to establish an efficient and effective system in managing faculty attendance and discourage unnecessary absences. The Faculty Attendance Management System (FAMS) is a web based and mobile application which is proven to be efficient and effective in handling and recording data, generating updated reports and analytics needed in managing faculty attendance. The FAMS can facilitate not only a convenient and faster way of gathering and recording of data but it can also provide data analytics, immediate feedback system mechanism and analysis. The software database architecture uses MySQL for web based and SQLite for mobile applications. The system includes different modules that capture daily attendance of faculty members, generate faculty attendance reports and analytics, absences notification system for faculty members, chairperson and dean regarding absences, and immediate communication system concerning the absences incurred. Quantitative and qualitative evaluation showed that the system satisfactory meet the stakeholder’s requirements. The functionality, usability, reliability, performance, and security all turned out to be above average. System testing, integration testing and user acceptance testing had been conducted. Results showed that the system performed very satisfactory and functions as designed. Performance of the system is also affected by Internet infrastructure or connectivity of the university. The faculty analytics generated from the system may not only be used by Deans and Chairperson in their evaluation of faculty performance but as well as the individual faculty to increase awareness on their attendance in class. Hence, the system facilitates effective communication between system stakeholders through FAMS feedback mechanism and up to date posting of information.

Keywords: faculty attendance management system, MySQL, SQLite, FAMS, analytics

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Jinil Patel, Sarthak Patel, Sarthak Thakkar, Deepti Saraswat

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Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

Keywords: federated learning, COVID-19, CT-scan, homomorphic encryption, ResNet50, VGG-19, MobileNetv2, DLA

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Authors: Seyed Reza Naghibulsadat

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The nature of communication includes various forms of media productions, which include film and theater. In the current situation, since social networks have emerged, they have brought their own communication capabilities and have features that show speed, public access, lack of media organization and the production of extensive content, and the development of critical thinking; Also, they contain capabilities to develop access to all kinds of media productions, including movies and theater shows; Of course, this works differently in different conditions and communities. In terms of the scale of exploitation, the film has a more general audience, and the theater has a special audience. The film industry is more developed based on more modern technologies, but the theater, based on the older ways of communication, contains more intimate and emotional aspects. ; But in general, the main focus is the development of access to movies and theater shows, which is emphasized by those involved in this field due to the capabilities of social networks. In this research, we will look at these 2 areas and the relevant components for both areas through social networks and also the common points of both types of media production. The main goal of this research is to know the strengths and weaknesses of using social networks for the marketing of movies and theater shows and, at the same time are, also considered the opportunities and threats of this field. The attractions of these two types of media production, with the emergence of social networks, and the ability to change positions, can provide the opportunity to become a media with greater exploitation and higher profitability; But the main consideration is the opinions about these capabilities and the ability to use them for film and theater marketing. The main question of the research is, what are the marketing components for movies and theaters using social media capabilities? What are its strengths and weaknesses? And what opportunities and threats are facing this market? This research has been done with two methods SWOT and meta-analysis. Non-probability sampling has been used with purposeful technique. The results show that a recent approach is an approach based on eliminating threats and weaknesses and emphasizing strengths, and exploiting opportunities in the direction of developing film and theater marketing based on the capabilities of social networks within the framework of local cultural values and presenting achievements on an international scale or It is universal. This introduction leads to the introduction of authentic Iranian culture and foreign enthusiasts in the framework of movies and theater art. Therefore, for this issue, the model for using the capabilities of social networks for movie or theater marketing, according to the results obtained from Respondents, is a model based on SO strategies and, in other words, offensive strategies so that it can take advantage of the internal strengths and made maximum use of foreign situations and opportunities to develop the use of movies and theater performances.

Keywords: marketing, movies, theatrical show, social network potentials

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Authors: Mingxi Xiao

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Children’s experiences in the early years build and shape the brain. The early years learning environment plays a significantly important role in children’s development. A well-constructed environment will facilitate children’s physical and mental well-being. This case study used an early learning centre in Australia called SDN Hurstville as an example, describing the learning environment in the centre, as well as analyzing the functions of the affordances. In addition, this report talks about the sustainability of learning in the centre, and how the environment supports cultural diversity and indigenous learning. The early years for children are significant. Different elements in the early childhood centre should work together to help children develop better. This case study found that the natural environment and the artificial environment are both critical to children; only when they work together can children have better development in physical and mental well-being and have a sense of belonging when playing and learning in the centre.

Keywords: early childhood center, early childhood education, learning environment, Australia

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Authors: Aastha Chaudhry

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This continuous structuring of the ego through the developmental ages, starting with the body, has been understood through various perspectives from the object-relations world. Klein, Ogden, Winnicott to name a few, have been masters at helping mark a trajectory for the self to come to fruition. However, what constitutes those states, those relational structures, the dynamics of transference and the concept of inner objects has been more or less left unexplored in the psychoanalytic developmental theory. In this paper, through the help of a case study, Ogden’s ideas of an autistic contagious position and Kleinian theory of object relations is proposed to visualize a lens that helps to understand the relationship of the autistic self and body and allows us to take a look at object relations through countertransference. With the help of case vignettes, an understanding of experience is seen as dominated in the autistic contagious position with the help of defensive structuring that is not only self-fulfilling and sensorial oriented, but is also a pre symbolic mode of relating to the other. The aim of this clinical, experiential study is to better understand the self-body and the self-other relationships, or the absence thereof, in the autistic world and states. The goal of the study was to find such a relationship between play, body, structuring of experience and an autistic self in these individuals through that. Aim being that psychotherapy is brought to fore in the world of autism. The method was case study with one on one intervention, that was psychodynamically informed and play therapy based. Some of the findings after a year of work with these individuals were that: in the absence of a shared vocabulary, communication in two contrasting individuals happens primarily through the assistance of the body. Somatic countertransference, for instance, is how one can be with someone in a therapeutic relationship – and with autistic adolescents it is a further complicated relationship. With a mind somewhere in infanthood, and body experiencing adulthood, it becomes a challenge for the therapist to meet the client where they are. With pre-verbal states, play becomes such a potential space where two individuals could meet – a safe ground for forces to be contained. Play, then, becomes a mode of communication with such a population.

Keywords: autism, psychoanalytic, play, self

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a hybrid distributed algorithm has been suggested for multi-objective dynamic flexible job shop scheduling problem. The proposed algorithm is high level, in which several algorithms search the space on different machines simultaneously also it is a hybrid algorithm that takes advantages of the artificial intelligence, evolutionary and optimization methods. Distribution is done at different levels and new approaches are used for design of the algorithm. Apache spark and Hadoop frameworks have been used for the distribution of the algorithm. The Pareto optimality approach is used for solving the multi-objective benchmarks. The suggested algorithm that is able to solve large-size problems in short times has been compared with the successful algorithms of the literature. The results prove high speed and efficiency of the algorithm.

Keywords: distributed algorithms, apache-spark, Hadoop, flexible dynamic job shop scheduling, multi-objective optimization

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Authors: Nwamaka Patricia Ibeme, Musa Zakari

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E-learning is made more effective because; it is enable student to students to easily interact, share, and collaborate across time and space with the help of e-governance platform. Zoom and the Microsoft classroom team can invite students from all around the world to join a conversation on a certain subject simultaneously. E-governance may be able to work on problem solving skills, as well as brainstorming and developing ideas. As a result of the shared experiences and knowledge, students are able to express themselves and reflect on their own learning." For students, e-governance facilities provide greater opportunity for students to build critical (higher order) thinking abilities through constructive learning methods. Students' critical thinking abilities may improve with more time spent in an online classroom. Students' inventiveness can be enhanced through the use of computer-based instruction. Discover multimedia tools and produce products in the styles that are easily available through games, Compact Disks, and television. The use of e-learning has increased both teaching and learning quality by combining student autonomy, capacity, and creativity over time in developed countries." Teachers are catalysts for the integration of technology through Information and Communication Technology, and e-learning supports teaching by simplifying access to course content." Creating an Information and Communication Technology class will be much easier if educational institutions provide teachers with the assistance, equipment, and resources they need. The study adopted survey research design. The populations of the study are Students and staff. The study adopted a simple random sampling technique to select a representative population. Both primary and secondary method of data collection was used to obtain the data. A chi-square statistical technique was used to analyze. Finding from the study revealed that e-learning has increase accesses to universities educational by public servants in Nigeria. Public servants in Nigeria have utilized e-learning and Online Distance Learning (ODL) programme to into various degree programmes. Finding also shows that E-learning plays an important role in teaching because it is oriented toward the use of information and communication technologies that have become a part of the everyday life and day-to-day business. E-learning contributes to traditional teaching methods and provides many advantages to society and citizens. The study recommends that the e-learning tools and internet facilities should be upgrade to foster any network challenges in the online facilitation and lecture delivery system.

Keywords: E-governance, E-learning, online distance learning, university education public servants, Nigeria

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Authors: Coriolano Salvini

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The increasing utilization of renewable energy sources for electric power production calls for the introduction of energy storage systems to match the electric demand along the time. Although many countries are pursuing as a final goal a “decarbonized” electrical system, in the next decades the traditional fossil fuel fed power plant still will play a relevant role in fulfilling the electric demand. Presently, such plants provide grid ancillary services (frequency control, grid balance, reserve, etc.) by adapting the output power to the grid requirements. An interesting option is represented by the possibility to use traditional plants to improve the grid storage capabilities. The present paper is addressed to small-medium size systems suited for distributed energy storage. The proposed Energy Storage System (ESS) is based on a Compressed Air Energy Storage (CAES) integrated into a Gas-Steam Combined Cycle (GSCC) or a Gas Turbine based CHP plants. The systems can be incorporated in an ex novo built plant or added to an already existing one. To avoid any geological restriction related to the availability of natural compressed air reservoirs, artificial storage is addressed. During the charging phase, electric power is absorbed from the grid by an electric driven intercooled/aftercooled compressor. In the course of the discharge phase, the compressed stored air is sent to a heat transfer device fed by hot gas taken upstream the Heat Recovery Steam Generator (HRSG) and subsequently expanded for power production. To maximize the output power, a staged reheated expansion process is adopted. The specific power production related to the kilogram per second of exhaust gas used to heat the stored air is two/three times larger than that achieved if the gas were used to produce steam in the HRSG. As a result, a relevant power augmentation is attained with respect to normal GSCC plant operations without additional use of fuel. Therefore, the excess of output power can be considered “fuel free” and the storage system can be compared to “pure” ESSs such as electrochemical, pumped hydro or adiabatic CAES. Representative cases featured by different power absorption, production capability, and storage capacity have been taken into consideration. For each case, a technical optimization aimed at maximizing the storage efficiency has been carried out. On the basis of the resulting storage pressure and volume, number of compression and expansion stages, air heater arrangement and process quantities found for each case, a cost estimation of the storage systems has been performed. Storage efficiencies from 0.6 to 0.7 have been assessed. Capital costs in the range of 400-800 €/kW and 500-1000 €/kWh have been estimated. Such figures are similar or lower to those featuring alternative storage technologies.

Keywords: artificial air storage reservoir, compressed air energy storage (CAES), gas steam combined cycle (GSCC), techno-economic analysis

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Authors: Bataineh Abdallah, Alabdallah Ghaith, Alkharabshe Abdalhameed

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Social media is an umbrella term for different types of online communication channels. The most prominent forms can be divided into four categories: Collaborative projects (e.g. Wikipedia, comparison-shopping sites), blogs (e.g. Twitter), content communities (e.g. Youtube), social networking sites (e.g. Facebook) social media allow consumers to share their opinions, criticisms and suggestions in public. Facebook launched in 2004, initially targeted college students and later started including everyone has become the most popular sites amongst the young generation for connecting with friends and relatives and for the communication of ideas. In 2013 Facebook penetration rate reached 41.4% of the population making it the most popular social networking site in Jordan. Accordingly, the purpose of this research is to examine the impact of perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment and subjective norms on users' satisfaction and continuance intention to use Facebook in Jordan. Using a structured questionnaire, the primary data was collected from 584 users who have an active Facebook accounts. Multiple regression analysis was employed to test the research model and hypotheses. The research findings indicate that perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment, and subjective norms have a positive and significant effect on users' satisfaction and continuance intention to use Facebook. The findings also indicated that the strongest predictors, based on beta values, on both users' satisfaction and continuance intention to use Facebook is subjective norms and respectively, perceived enjoyment, perceived usefulness, perceived ease of us, and perceived trust. Research results, recommendations, and future research opportunities are also discussed.

Keywords: perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment, perceived subjective norms, users' satisfaction, continuance intention, Facebook

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Authors: Surah Aldakhl, Dafer Alali, Mohamed Zohdy

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The next generation of electricity grid infrastructure, known as the "smart grid," integrates smart ICT (information and communication technology) into existing grids in order to alleviate the drawbacks of existing one-way grid systems. Future power systems' efficiency and dependability are anticipated to significantly increase thanks to the Smart Grid, especially given the desire for renewable energy sources. The security of the Smart Grid's cyber infrastructure is a growing concern, though, as a result of the interconnection of significant power plants through communication networks. Since cyber-attacks can destroy energy data, beginning with personal information leaking from grid members, they can result in serious incidents like huge outages and the destruction of power network infrastructure. We shall thus propose a secure smart energy management system based on the Blockchain as a remedy for this problem. The power transmission and distribution system may undergo a transformation as a result of the inclusion of optical fiber sensors and blockchain technology in smart grids. While optical fiber sensors allow real-time monitoring and management of electrical energy flow, Blockchain offers a secure platform to safeguard the smart grid against cyberattacks and unauthorized access. Additionally, this integration makes it possible to see how energy is produced, distributed, and used in real time, increasing transparency. This strategy has advantages in terms of improved security, efficiency, dependability, and flexibility in energy management. An in-depth analysis of the advantages and drawbacks of combining blockchain technology with optical fiber is provided in this paper.

Keywords: smart grids, blockchain, fiber optic sensor, security

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Authors: Ali Atashbar Orang, Carlo Massimo Casciola

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A novel numerical approach for the steady incompressible turbulent flows is presented in this paper. The artificial compressibility method (ACM) is applied to the Reynolds Averaged Navier-Stokes (RANS) equations. A new Characteristic-Based Turbulent (CBT) scheme is developed for the convective fluxes. The well-known Spalart–Allmaras turbulence model is employed to check the effectiveness of this new scheme. Comparing the proposed scheme with previous studies, it is found that the present CBT scheme demonstrates accurate results, high stability and faster convergence. In addition, the local time stepping and implicit residual smoothing are applied as the convergence acceleration techniques. The turbulent flows past a backward facing step, circular cylinder, and NACA0012 hydrofoil are studied as benchmarks. Results compare favorably with those of other available schemes.

Keywords: incompressible turbulent flow, method of characteristics, finite volume, Spalart–Allmaras turbulence model

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Authors: Chiara De Amicis, Sonia Falconieri, Mesut Tastan

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In this study, we analyze the language employed by Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) during earnings conference calls from a gender perspective. We find evidences that conference calls held by female CEOs and/or CFOs exhibit a higher level of optimism compared to conference calls held by male CEOs and/or CFOs. Moreover, female managers tend to present and discuss firm performances with less vagueness as compared to their male colleagues. We then observe the market reaction around each earnings conference call: while manager optimism is perceived as a good signal by investors, manager vagueness significantly dampens the market reaction around the call. Whether the gender of the CEO and/or the CFO delivering the conference call affects investors’ perceptions about the firm performance is still an open question. Some evidences show that the language employed by female managers conveys more valuable information for market participants as compared to the language employed by their male counterparts. This study contributes to a growing literature in finance and accounting that uses textual analysis to assess the informativeness of corporate disclosure. To our knowledge, this is the first paper that aims at answering the question whether the gender of firm’s top managers does matter when it comes to assess the informativeness of corporate spoken communication. We believe that our results will be of relevance for future research in the field. Moreover, our evidence may be used in support of the debate if a larger participation by women in the management of companies should be encouraged or not.

Keywords: conference calls, even study, gender, market reaction, textual analysis

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Authors: Nesrine Boussaoui

Abstract:

Background: International students often encounter challenges while studying in the UK, including communication and language barriers, lack of social networks, and socio-cultural differences that adversely impact on their mental health. For Algerian international students (AISs), these challenges may be heightened as English is not their first language and the culture of their homeland is substantially different from British culture, yet research has to incorporate their experiences and perspectives. Aim: The current study aimed to explore AISs’ 1) understandings of mental health; 2) issues of disclosure for mental health difficulties; and 3) mental health help-seeking and coping strategies. Method: In-depth, audio recorded semi-structured interviews (n = 20) with AISs in UK universities were conducted. An inductive, reflective thematic approach analysis was used. Finding: The following themes and associated sub-themes were developed: (1) Algerian cultural influences on mental health understanding(socio-cultural comparisons); (2) the paradox of the family (pressure vs. support); (3) stigma and fear of disclosure; (4) Barriers to formal help-seeking (informal disclosure as first step to seeking help); (5) Communication barriers (resort to mother tongue to disclose); (6) Self-reliance and religious coping. Conclusion: Recognising and understanding the challenges faced by AISs in terms of disclosure and mental health help-seeking is essential to reduce barriers to formal help-seeking. Informal disclosure among peers is often the first step to seeking help. Enhancing practitioners’ cultural competences and awareness of diverse understandings of mental health and the role of religious coping among AISs’ may have transferable benefits to a wider international student population.

Keywords: mental health, stegma, coping, disclosure

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: Mohammad Ridzwan Bin Abd Rahim, Siegfried Schmauder, Yupiter HP Manurung, Peter Binkele, Meor Iqram B. Meor Ahmad, Kiarash Dogahe

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This paper deals with the short crack initiation of the material P91 under cyclic loading at two different temperatures, concluded with the estimation of the short crack initiation Wöhler (S/N) curve. An artificial but representative model microstructure was generated using Voronoi tessellation and the Finite Element Method, and the non-uniform stress distribution was calculated accordingly afterward. The number of cycles needed for crack initiation is estimated on the basis of the stress distribution in the model by applying the physically-based Tanaka-Mura model. Initial results show that the number of cycles to generate crack initiation is strongly correlated with temperature.

Keywords: short crack initiation, P91, Wöhler curve, Voronoi tessellation, Tanaka-Mura model

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

Procedia PDF Downloads 260