Search results for: virtual machine migration

Authors: Mulindwa Gerald

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Forced Migration is believed to be the most pressing issue in migration studies today, it therefore makes it of paramount importance that we examine the efficacy of the prevailing laws, treaties, conventions and global policies of refugee management. It suffices to note that the existing policies are vague and ambiguous encouraging the hospitality but not assessing the social economic impact to not only the refugees but also their host communities. The commentary around the Off-shore arrangements like one of UK-Rwanda and the legal implications of the same, make it even more fascinating. These are issues that need to be amplified and captured in the Migration Policies. In Uganda, a small landlocked country in East Africa, there always appeared new faces who were refugees from the Congo and Rwanda the neighboring countries to the West and South West respectively. The refugees would migrate to Uganda with absolutely no idea whatsoever how they were going to meet the daily needs of life, no food, no shelter, no clothing. It interest’s one’s mind to conscientiously interrogate the policy issues surrounding refugee management. The 1951 convention sets a number of obligations to states and the conundrum, faced by citizens of the universe interested in Migration studies is ensuring maximum compliance to these obligations considering the resource challenges. States have a duty to protect refugees in accordance with Article 14 of the Universal Declaration for Human Rights which was adopted by the 1951 convention, these speak to rights like the most important right of refugees known as the Principle of Non-Refoulement, which prohibits expulsion or return of refugees or asylum seekers The International Organization for Migrations projection of the number of migrants globally by 2050 was overwhelmingly surpassed by 2019 due to wars, conflicts that have been experienced in different parts of the globe. This is also due natural calamities and tough economic conditions. It is a descriptive analysis that encompasses a qualitative design research based on a case study involving both desk research and field study. The use of qualitative research approaches like interview guides, document review and direct observation methods helped to bring in the experience, social, behavioral and cultural aspects of the respondents into the study, and since qualitative research uses subjective information and not limited to the rigidly definable variables, thus it helped to explore the research area of the study. it therefore verily believe that this paper is going to trigger perspectives and spark a conversation on this really pressing global issue of refugees and asylum seekers, it is suggesting viable solutions to the management challenges while making recommendations like the ensuring that no refugees or asylum seekers are closed at any borders on the globe for instance a concerted effort of all global players to ensure that refugees are protected efficiently.

Keywords: management, migration, refugees, rights

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Authors: Wenlong Feng, Zhenchun Du, Jianguo Yang

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To decrease the grating scale thermal expansion error, a novel method which based on multiple temperature detections is proposed. Several temperature sensors are installed on the grating scale and the temperatures of these sensors are recorded. The temperatures of every point on the grating scale are calculated by interpolating between adjacent sensors. According to the thermal expansion principle, the grating scale thermal expansion error model can be established by doing the integral for the variations of position and temperature. A novel compensation method is proposed in this paper. By applying the established error model, the grating scale thermal expansion error is decreased by 90% compared with no compensation. The residual positioning error of the grating scale is less than 15um/10m and the accuracy of the machine tool is significant improved.

Keywords: thermal expansion error of grating scale, error compensation, machine tools, integral method

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Rahaf Alasiri, Tarek Alghamdi, Abdullah Zarkan

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Background: Due to measures such as school closure, social distancing, and virtual teaching during the pandemic, primary school children's psychological well-being is greatly affected. These measures have short and long-term consequences on the children's well-being and mental health. Identifying these consequences is important. Aim: This study aimed to evaluate mood and behavior changes in children who attended school virtually. Subjects and methods: This is a cross-sectional study conducted among children and their parents who visited the outpatient clinic. A self-administered questionnaire was given to the parents of children aged between 6 to 14 years. The questionnaire includes socio-demographic characteristics, Conor's modifies scale to assess the attention deficit hyperactivity disorder (ADHD) of children, and the parental stress scale (PSS) to assess the stress symptoms of the parents. Results: Of the 66 surveyed children, 60.6% were aged between 10 to 14 years old, with the female being dominant (77.3%). The most common medical condition was asthma (7.6%), and nearly two-thirds (63.6%) indicated good health conditions during the pandemic. There was a significant inverse correlation observed between ADHD score and PSS score (r=-0.387). No significant differences are in ADHD and PSS scores in relation to the socio-demographic characteristics of the children, including age, gender, and having an associated medical condition (p>0.05). Conclusion: During the pandemic, children who attended virtual classes did not seem to affect even with restrictions. Most children indicated good health conditions during the pandemic. However, it is surprising to know that in spite of children’s high spirits during the pandemic, their parents were seen to have an increased level of stress. Strategies to address parents’ psychological disorders during the pandemic are warranted.

Keywords: children's mood, COVID-19, ADHD, parental stress

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Lanlan Xiao, Yang Liu, Shuo Chen, Bingmei Fu

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Most cancer-related deaths are due to metastasis. Metastasis is a complex, multistep processes including the detachment of cancer cells from the primary tumor and the migration to distant targeted organs through blood and/or lymphatic circulations. During hematogenous metastasis, the emigration of tumor cells from the blood stream through the vascular wall into the tissue involves arrest in the microvasculature, adhesion to the endothelial cells forming the microvessel wall and transmigration to the tissue through the endothelial barrier termed as extravasation. The narrow slit between endothelial cells that line the microvessel wall is the principal pathway for tumor cell extravasation to the surrounding tissue. To understand this crucial step for tumor hematogenous metastasis, we used Dissipative Particle Dynamics method to investigate an individual cell passing through a narrow slit numerically. The cell membrane was simulated by a spring-based network model which can separate the internal cytoplasm and surrounding fluid. The effects of the cell elasticity, cell shape and cell surface area increase, and slit size on the cell transmigration through the slit were investigated. Under a fixed driven force, the cell with higher elasticity can be elongated more and pass faster through the slit. When the slit width decreases to 2/3 of the cell diameter, the spherical cell becomes jammed despite reducing its elasticity modulus by 10 times. However, transforming the cell from a spherical to ellipsoidal shape and increasing the cell surface area only by 3% can enable the cell to pass the narrow slit. Therefore the cell shape and surface area increase play a more important role than the cell elasticity in cell passing through the narrow slit. In addition, the simulation results indicate that the cell migration velocity decreases during entry but increases during exit of the slit, which is qualitatively in agreement with the experimental observation.

Keywords: dissipative particle dynamics, deformability, surface area increase, cell migration

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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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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Authors: Jacob Medupe

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Life as we know it has changed since the global outbreak of Coronavirus Disease 2019 (COVID-19) and business as usual will not continue. The human impact of the COVID-19 crisis is already immeasurable. Moreover, COVID-19 has already negatively impacted economies, livelihoods and disrupted food systems around the world. The disruptive nature of the Corona virus has affected every sphere of life including the culture and teaching and learning. Right now the majority of education research is based around classroom management techniques that are no longer necessary with digital delivery. Instead there is a great need for new data about how to make the best use of the one-on-one attention that is now becoming possible (Diamandis & Kotler, 2014). The COVID-19 pandemic has necessitated an environment where the South African learners are focused to adhere to social distancing in order to minimise the wild spread of the Corona virus. This arrangement forces the student to utilise the online classroom technologies to continue with the lessons. The historical reality is that the country has not made much strides on the closing of the digital divide and this is particularly a common status quo in the deep rural areas. This will prove to be a toll order for most of the learners affected by the Corona Virus to be able to have a seamless access to the online learning facilities. The paper will seek to look deeply into this reality and how the Corona virus has brought us to the reality that South Africa remains a deeply unequal society in every sphere of life. The study will also explore the state of readiness for education system around the online classroom environment.

Keywords: virtual learning, virtual classroom, COVID-19, Corona virus, internet connectivity, blended learning, online learning, distance education, e-learning, self-regulated Learning, pedagogy, digital literacy

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Authors: Saeedeh Bakhtiari, Johannes Depessemier, Stijn Hertelé, Wim De Waele

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High cycle fatigue comprising up to 10⁷ load cycles has been the subject of many studies, and the behavior of many materials was recorded adequately in this regime. However, many applications involve larger numbers of load cycles during the lifetime of machine components. In this ultra-high cycle regime, other failure mechanisms play, and the concept of a fatigue endurance limit (assumed for materials such as steel) is often an oversimplification of reality. When machine component design demands a high geometrical complexity, cast iron grades become interesting candidate materials. Grey cast iron is known for its low cost, high compressive strength, and good damping properties. However, the ultra-high cycle fatigue behavior of cast iron is poorly documented. The current work focuses on the ultra-high cycle fatigue behavior of EN-GJL-250 (GG25) grey cast iron by developing an ultrasonic (20 kHz) fatigue testing system. Moreover, the testing machine is instrumented to measure the temperature and the displacement of  the specimen, and to control the temperature. The high resonance frequency allowed to assess the  behavior of the cast iron of interest within a matter of days for ultra-high numbers of cycles, and repeat the tests to quantify the natural scatter in fatigue resistance.

Keywords: GG25, cast iron, ultra-high cycle fatigue, ultrasonic test

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Cristian Crespo

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Conditional cash transfers (CCTs) have been targeted towards the poor. Thus, their targeting assessments check whether these schemes have been allocated to low-income households or individuals. However, CCTs have more than one goal and target group. An additional goal of CCTs is to increase school enrolment. Hence, students at risk of dropping out of school also are a target group. This paper analyses whether one of the most common targeting mechanisms of CCTs, a proxy means test (PMT), is suitable to identify the poor and future school dropouts. The PMT is compared with alternative approaches that use the outputs of a predictive model of school dropout. This model was built using machine learning algorithms and rich administrative datasets from Chile. The paper shows that using machine learning outputs in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts. This joint targeting approach increases effectiveness in different scenarios except when the social valuation of the two target groups largely differs. In these cases, the most likely optimal approach is to solely adopt the targeting mechanism designed to find the highly valued group.

Keywords: conditional cash transfers, machine learning, poverty, proxy means tests, school dropout prediction, targeting

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: N. P. Patidar, J. Earnest, Laxmikant Nagar, Akshay Sharma

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This paper describes the diligence of a new input signal based fuzzy power system stabilizer in multi-machine power system. Instead of conventional input pairs like speed deviation (∆ω) and derivative of speed deviation i.e. acceleration (∆ω ̇) or speed deviation and accelerating power deviation of each machine, in this paper, deviation of active power through the tie line colligating two areas is used as one of the inputs to the fuzzy logic controller in concurrence with the speed deviation. Fuzzy Logic has the features of simple concept, easy effectuation, and computationally efficient. The advantage of this input is that, the same signal can be fed to each of the fuzzy logic controller connected with each machine. The simulated system comprises of two fully symmetrical areas coupled together by two 230 kV lines. Each area is equipped with two superposable generators rated 20 kV/900MVA and area-1 is exporting 413 MW to area-2. The effectiveness of the proposed control scheme has been assessed by performing small signal stability assessment and transient stability assessment. The proposed control scheme has been compared with a conventional PSS. Digital simulation is used to demonstrate the performance of fuzzy logic controller.

Keywords: Power System Stabilizer (PSS), small signal stability, inter-area oscillation, fuzzy logic controller, membership function, rule base

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Kevin T. Inks

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India’s Brahmaputra River Valley has, since independence, experienced consistent low-intensity guerrilla warfare between ethnic and religious groups. These groups are often organized around perceived ethnic territoriality, and target civilians, communities, and especially migrants belonging to other ethnic and religious groups. Intense flooding and erosion have led to widespread displacement, and disaster relief funds are largely tied to legal land tenure. Displaced residents of informal settlements receive little or no resettlement aid, and their subsequent migration strategies and risk from guerrilla violence are poorly understood. Semi-structured interviews and comprehensive surveys focused on perceptions of risk, efficacy of disaster relief, and migration and adaptation strategies were conducted with households identified as being ‘at-risk’ of catastrophic flooding and erosion in Majuli District, Assam. Interviews with policymakers and government workers were conducted to assess disaster relief efforts in informal settlements, and remote sensing methods were used to identify informal settlement and hydrogeomorphic change. The results show that various ethnic and religious groups have differential strategies and preferences for resettlement. However, these varying strategies are likely to lead to differential levels of risk from guerrilla violence. Members of certain ethnic groups residing in informal settlements, in the absence of resettlement assistance, are more likely to seek out unofficial settlement on land far from the protection of the state and experience greater risk of becoming victims of political violence. As climate change and deforestation are likely to increase the severity of the displacement crisis in the Brahmaputra River Valley, more comprehensive disaster relief and surveying efforts are vital for limiting migration and informal settlement in potential sites of guerrilla warfare.

Keywords: climate, displacement, flooding, India, violence

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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: N. Dumitru, S. Dumitru, C. Copilusi, N. Ploscaru

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On this research, experimental analyses have been performed in order to determine the oil pump mechanism dynamics and stability from an oil unit mechanical structure. The experimental tests were focused on the vibrations which occur inside of the rod element during functionality of the oil pump unit. The oil pump mechanism dynamic parameters were measured and also determined through numerical computations. Entire research is based on the oil pump unit mechanical system virtual prototyping. For a complete analysis of the mechanism, the frequency dynamic response was identified, mainly for the mechanism driven element, based on two methods: processing and virtual simulations with MSC Adams aid and experimental analysis. In fact, through this research, a complete methodology is presented where numerical simulations of a mechanism with deformed elements are developed on a dynamic mode and these can be correlated with experimental tests.

Keywords: modal dynamic analysis, oil pump, vibrations, flexible elements, frequency response

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: C. Slim

Abstract:

This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

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Authors: Partha P. Gopmandal, S. Bhattacharyya

Abstract:

We investigate the thermal end effect on the pseudo-steady state behavior of the isotachophoretic transport of ionic species in a 2-D microchannel. Both ends of the channel are kept at a constant temperature which may lead to significant changes in electrophoretic migration speed. A mathematical model based on Nernst-Planck equations for transport of ions coupled with the equation for temperature field is considered. In addition, the charge conservation equations govern the potential field due to the external electric field. We have computed the equations for ion transport, potential and temperature in a coupled manner through the finite volume method. The diffusive terms are discretized via central difference scheme, while QUICK (Quadratic Upwind Interpolation Convection Kinematics) scheme is used to discretize the convective terms. We find that the thermal end effect has significant effect on the isotachophoretic (ITP) migration speed of the analyte. Our result shows that the ITP velocity for temperature dependent case no longer varies linearly with the applied electric field. A detailed analysis has been made to provide a range of the key parameters to minimize the Joule heating effect on ITP transport of analytes.

Keywords: finite volume method, isotachophoresis, QUICK scheme, thermal effect

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Authors: Maierhaba Mijiti

Abstract:

Objective：The aim is to understand the relationship between the expression level of the long-non-coding RNA LINC AC008063 and the clinicopathological parameters of patients with head and neck squamous cell carcinoma (HNSCC), and to clarify the biological function of LINC AC008063 in HNSCC cells. Moreover, it provides a potential biomarker for the diagnosis, treatment, and prognosis evaluation of HNSCC. Methods: The expression level of LINC AC008063 in the HNSCC was analyzed using transcriptome sequencing data from the TCGA (The cancer genome atlas) database. The expression levels of LINC AC008063 in human embryonic lung diploid cells 2BS, human immortalized keratinocytes HACAT, HNSCC cell lines CAL-27, Detroit562, AMC-HN-8, FD-LSC-1, FaDu and WSU-HN30 were determined by real-time quantitative PCR (qPCR). RNAi (RNA interference) was introduced for LINC AC008063 knockdown in HNSCC cell lines, the localization and abundance analysis of LINC AC008063 was determined by RT-qPCR, and the biological functions were examined by CCK-8, clone formation, flow cytometry, transwell invasion and migration assays, Seahorse assay. Results: LINC AC008063 was upregulated in HNSCC tissue (P<0.001), and verified b CCK-8, clone formation, flow cytometry, transwell invasion and migration assays, Seahorse assayy qPCR in HNSCC cell lines. The survival analysis revealed that the overall survival rate (OS) of patients with high LINC AC008063 expression group was significantly lower than that in the LINC AC008063 expression group, the median survival times for the two groups were 33.10 months and 61.27 months, respectively (P=0.002). The clinical correlation analysis revealed that its expression was positively correlated with the age of patients with HNSCC (P<0.001) and positively correlated with pathological state (T3+T4＞T1+T2, P=0.03). The RT-qPCR results showed that LINC AC008063 was mainly enriched in cytoplasm (P=0.01). Knockdown of LINC AC008063 inhibited proliferation, colony formation, migration and invasion; the glycolytic capacity was significantly decreased in HNSCC cell lines (P＜0.05). Conclusion: High level of LINC AC008063 was associated with the malignant progression of HNSCC as well as promoting the important biological functions of proliferation, colony formation, migration and invasion; in particular, the glycolytic capacity was decreased in HNSCC cells. Therefore, LINC AC008063 may serve as a potential biomarker for HNSCC and a distinct molecular target to inhibit glycolysis.

Keywords: head and neck squamous cell carcinoma, oncogene, long non-coding RNA, LINC AC008063, invasion and metastasis

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

Abstract:

This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

Abstract:

With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Victor Radich, Tania Basso, Regina Moraes

Abstract:

Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.

Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring

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

Abstract:

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

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

Procedia PDF Downloads 195