Search results for: real world challenges

Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Ana Sofia Saldanha

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The answer is yes. Globalization is changing the translation world day after day, year after year. The need to know more about new technologies, clients, companies, project management and social networks is becoming more and more demanding and increasingly competitive. The great majority of the recently graduated Translators do not know where to go, what to do or even who to contact to start their careers in translation. It is well known that there are innumerous webinars, books, blogs and webpages with the so-called “tips do become a professional translator” indicating for example, what to do, what not to do, rates, how your resume should look like, etc. but are these pieces of advice coming from real translators? Translators who work daily with clients, who understand their demands, requests, questions? As far as today`s trends, the answer is no. Most of these pieces of advice are just theoretical and coming from “brilliant minds” who are more interested in spreading their word and winning “likes” to become, in some way, “important people in some area. Mentoring is, indeed, a highly important tool to help and guide new translators starting their career. An effective and well oriented Mentoring is a powerful way to orient these translators on how to create their resumes, where to send resumes, how to approach clients, how to answer emails and how to negotiate rates in an efficient way. Mentoring is a crucial tool and even some kind of “psychological trigger”, when properly delivered by professional and experienced translators, to help in the so aimed career development. The advice and orientation sessions which can bem 100% done online, using Skype for example, are almost a “weapon” to destroy the barriers created by opinions, by influences or even by universities. This new orientation trend is the future path for new translators and is the future of the Translation industry and professionals and Universities who must update their way of approaching the real translation world, therefore, minds and spirits need to be opened and engaged in this new trend of developing skills.

Keywords: mentoring, orientation, professional follow-up, translation

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Authors: Gylych Jelilov

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This paper investigates the effect of foreign debt on economic growth. Example has been chosen from Africa, Nigeria. By conducting cointegration test we have tested for a long-run relationship between. GDP = Real gross domestic product, EXTDEBT = External debt, INT = Interest rate, CAB = Current account balance, and EXCHR = Real exchange rate over the period 1990 to 2012. It was found out by the study that there is a negative but insignificant relationship between external debt and real gross domestic product. While a positive relationship exists between external debt and economic growth. Also, showed a negative and significant relationship between interest rate and real gross domestic product and there was a positive but insignificant relationship between current account balance and real gross domestic product.

Keywords: economic growth, foreign debt, Nigeria, sustainable development, economic stability

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: C. Pacheco, A. Cipriano

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A new concept of response system is proposed for filling the gap that exists in reducing vulnerability during immediate response to natural disasters. Real Time Early Response Systems (RTERSs) incorporate real time information as feedback data for closing control loop and for generating real time situation assessment. A review of the state of the art works that fit the concept of RTERS is presented, and it is found that they are mainly focused on manmade disasters. At the same time, in response phase of natural disaster management many works are involved in creating early warning systems, but just few efforts have been put on deciding what to do once an alarm is activated. In this context a RTERS arises as a useful tool for supporting people in their decision making process during natural disasters after an event is detected, and also as an innovative context for applying well-known automation technologies and automatic control concepts and tools.

Keywords: disaster management, emergency response system, natural disasters, real time

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Authors: S. K. Ashiquer Rahman

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Innovation, sustainability, and higher education are vital issues of the knowledge economy and society. In fact, the concentration on these issues, educators and researchers convinced the learners to prepare productive citizens for the knowledge economy and society, and many initiatives have been launched worldwide. The concept of a knowledge economy requires simultaneous and balanced progress in three dimensions (Innovation, Education and Sustainability) which are totally interdependent and correlated. The paper discusses the importance of an integrated approach to the knowledge economy and society from the perspective of higher education institutions. It remarks on the advent of a knowledge-based economy and society and the need for the combination of Innovation, sustainability, and education. This paper introduces nine (9) important issues or challenges of higher education institutions that have emphasized, cross-linked each other, and combined in a new education system that can form a new generation for the completive world as well as able to manage the knowledge-based economy and societal system. Moreover, the education system must be the foundation for building the necessary knowledge-based economy and society, which must manage the innovation process through a more sustainable world. In this viewpoint, Innovation, sustainability and higher education are becoming more and more central in our economy and society, and it is directly associated with the possibility of global wealth distribution to the economy and society. The objective of this research is to demonstrate the knowledge-based economy and social paradigm in order to create the opportunity for higher education institutions' development. The paper uses the collective action methodologies to examine “the mechanisms and strategies” used by higher education institutions’ authority to accommodate an integrated pattern as per connecting behaviors of knowledge economy and society. The paper accomplishes that the combination of Innovation, sustainability and education is a very helpful approach to building a knowledge-based economy and society for practicing the higher education institution’s challenges.

Keywords: education, innovation, knowledge economy, sustainability

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Authors: Mohsin Ali Shaikh, Weiguo Song, Rehmat Karim, Muhammad Kashan Surahio, Muhammad Usman Shahid

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Fire investigations face challenges due to the complexity of fire development, and real-world accidents lack repeatability, making it difficult to apply standardized approaches. The unpredictable nature of fires and the unique conditions of each incident contribute to the complexity, requiring innovative methods and tools for effective analysis and reconstruction. This study proposes to provide the modern accident analysis model through numerical reconstruction for fire investigation in textile buildings. This method employs computer simulation to enhance the overall effectiveness of textile-building investigations. The materials and evidence collected from past incidents reconstruct fire occurrences, progressions, and catastrophic processes. The approach is demonstrated through a case study involving a tragic textile factory fire in Karachi, Pakistan, which claimed 257 lives. The reconstruction method proves invaluable for determining fire origins, assessing losses, establishing accountability, and, significantly, providing preventive insights for complex fire incidents.

Keywords: fire investigation, numerical simulation, fire safety, fire incident, textile building

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Authors: Miroslav Čulík, Petr Gurný

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This paper is aimed at the financial assessment of the hard coal mining in a given region by real option methodology application. Hard coal mining in this mine makes net loss for the owner during the last years due to the long-term unfavourable mining conditions and significant drop in the coal prices during the last years. Management is going to shut down the operation and abandon the project to reduce the loss of the company. The goal is to assess whether the shutting down the operation is the only and correct solution of the problem. Due to the uncertainty in the future hard coal price evolution, the production might be again restarted if the price raises enough to cover the cost of the production. For the assessment, real option methodology is applied, which captures two important aspect of the financial decision-making: risk and flexibility. The paper is structured as follows: first, current state is described and problem is analysed. Next, methodology of real options is described. At last, project is evaluated by applying real option methodology. The results are commented and recommendations are provided.

Keywords: real option, investment, option to abandon, option to shut down and restart, risk, flexibility

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Authors: Sai Shruthi Sridhar, A. Madhumidha, Kreethika Guru, Priyanka Sekar, Ananthi Malayappan

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Numerous changes in technology and its recent development are structuring long withstanding effect to our world, one among them is the emergence of “Internet of Things” (IoT). Similar to Technology world, one industry stands out in everyday life–healthcare. Attention to “quality of health care” is an increasingly important issue in a global economy and for every individual. As per WHO (World Health Organization) it is estimated to be less than 50% adhere to the medication provided and only about 20% get their medicine on time. Medication adherence is one of the top problems in healthcare which is fixable by use of technology. In recent past, there were minor provisions for elderly and specially-skilled to get motivated and to adhere medicines prescribed. This paper proposes a novel solution that uses IOT based RFID Medication Reminder Solution to provide personal health care services. This employs real time tracking which offer quick counter measures. The proposed solution builds on the recent digital advances in sensor technologies, smart phones and cloud services. This novel solution is easily adoptable and can benefit millions of people with a direct impact on the nation’s health care expenditure with innovative scenarios and pervasive connectivity.

Keywords: cloud services, IoT, RFID, sensors

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Authors: Vicky Khasandi-Telewa, Sinfree Makoni

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This paper examines the lived experiences of Kenyans caught between the two main varieties of English and the communication challenges they often face. The paper aims to provide evidence for the challenges that Kenyan speakers of English have experienced as a result of the confluence between British English (BrE) and American English (AmE). The study is explorative thus, qualitative and a descriptive research design was used. The objectives were to describe the communication challenges Kenyans encounter due to the differences in grammar, pronunciation, vocabulary, and pragmatics between AmE and BrE and, to illuminate these variations to enable the Kenyan English learner to communicate appropriately. The purposive sample consisted of sixty five subjects, all who were from a top girls national high school in Kenya, therefore, many are in high-end positions nationally and internationally and well-travelled; in fact, seven are based in the USA, two in the United Kingdom and one lives in Australia. Using interviews, they were asked about their experiences with the different varieties of English, and their responses recorded. Autoethnography, a qualitative research method that involves introspection and cultural analysis, was also used. Thematic content analysis was used to extract the themes and group them for data analysis. The theoretical framework used is World Englishes by Kachru. The results indicate that there is indeed some communication breakdown due to linguistic varieties, but the differences are largely understood by speakers of the different varieties. Attitude plays a major role in communication challenges between the different Englishes, especially among teachers. The study urges acceptance of different varieties of English so long as communication takes place. This has implications for teaching English to Kenyan and speakers of other languages to whom English is taught as a Second Language or Foreign language.

Keywords: American english, british english, kenyan english, englishes, teaching of english to speakers of other languages (TeSOL)

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Authors: Taru Singh, Shukla Das, V. G. Ramachandran

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Objectives: To develop SYBR green based real-time PCR assay to detect carbapenemases (NDM, IMP) genes in E. coli. Methods: A total of 40 E. coli from stool samples were tested. Six were previously characterized as resistant to carbapenems and documented by PCR. The remaining 34 isolates previously tested susceptible to carbapenems and were negative for these genes. Bacterial RNA was extracted using manual method. The real-time PCR was performed using the Light Cycler III 480 instrument (Roche) and specific primers for each carbapenemase target were used. Results: Each one of the two carbapenemase gene tested presented a different melting curve after PCR amplification. The melting temperature (Tm) analysis of the amplicons identified was as follows: blaIMP type (Tm 82.18°C), blaNDM-1 (Tm 78.8°C). No amplification was detected among the negative samples. The results showed 100% concordance with the genotypes previously identified. Conclusions: The new assay was able to detect the presence of two different carbapenemase gene type by real-time PCR.

Keywords: resistance, b-lactamases, E. coli, real-time PCR

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Authors: Jekaterina Novikova, Leon Watts

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In the emerging world of human-robot interaction, people and robots will interact socially in real-world situations. This paper presents the results of an experimental study probing the interaction between situational context and emotional body language in robots. 34 people rated video clips of robots performing expressive behaviours in different situational contexts both for emotional expressivity on Valence-Arousal-Dominance dimensions and by selecting a specific emotional term from a list of suggestions. Results showed that a contextual information enhanced a recognition of emotional body language of a robot, although it did not override emotional signals provided by robot expressions. Results are discussed in terms of design guidelines on how an emotional body language of a robot can be used by roboticists developing social robots.

Keywords: social robotics, non-verbal communication, situational context, artificial emotions, body language

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Authors: Julia Langen

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This article describes the challenges of a wireless charging system for a cab waiting lane in a public space and presents a concept for solving them. In this concept, multiple cabs can be charged simultaneously and during stopping and rolling. Particular technical challenges are a coil topology that meets the EMF requirements and an intelligent control concept that allows the individual coil segments to be switched on and off. The charging concept explained here is currently being implemented as a pilot project, so that initial results on the operation can be presented.

Keywords: charge lane, inductive charging solution, smart city, wireless power transfer

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Krish Batra

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The advent of open banking has revolutionized the financial services industry by fostering innovation, enhancing customer experience, and promoting competition. However, this paradigm shift towards more open and interconnected banking ecosystems has introduced complex cybersecurity challenges. This research paper delves into the multifaceted cybersecurity landscape of open banking, highlighting the vulnerabilities and threats inherent in sharing financial data across a network of banks and third-party providers. Through a detailed analysis of recent data breaches, phishing attacks, and other cyber incidents, the paper assesses the current state of cybersecurity within the open banking framework. It examines the effectiveness of existing security measures, such as encryption, API security protocols, and authentication mechanisms, in protecting sensitive financial information. Furthermore, the paper explores the regulatory response to these challenges, including the implementation of standards such as PSD2 in Europe and similar initiatives globally. By identifying gaps in current cybersecurity practices, the research aims to propose a set of robust, forward-looking strategies that can enhance the security and resilience of open banking systems. This includes recommendations for banks, third-party providers, regulators, and consumers on how to mitigate risks and ensure a secure open banking environment. The ultimate goal is to provide stakeholders with a comprehensive understanding of the cybersecurity implications of open banking and to outline actionable steps for safeguarding the financial ecosystem in an increasingly interconnected world.

Keywords: open banking, financial services industry, cybersecurity challenges, data breaches, phishing attacks, encryption, API security protocols, authentication mechanisms, regulatory response, PSD2, cybersecurity practices

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: H. Maita

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This paper addresses the issue of the speaking challenges that the Algerian PhD students experience during their studies abroad, particularly in UK territory; more specifically, this study describes how these students may deal with such challenges and whether the cultural differences is one core reason in such dilemma or not. To this end, an understanding and interpretation of what actually encompasses both linguistic interference and cultural differences are required. Throughout the paper there is an attempt to explain the theoretical basis of the interpretive research and to theoretically discuss the pivotal use of the interview, as a data collection tool, in interpretive research. Thus, the central issue of this study is to frame the theoretical perspective of the interpretive research through the discussion of PhD Algerian’s communication and interaction challenges in the EFL context. This study is a corner stone for other research studies to further investigate the issue related to communication challenges because no specific findings will be pointed out in this research.

Keywords: communication, EFL, interaction, linguistic interference

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Authors: Fatai O. Bakare

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This paper x-rays the liquidity problems exposed to by Islamic banks in terms of challenges in managing surplus as well as deficit liquidity positions and the attendant effects in the contemporary system of Islamic banking. Effective liquidity management is understood to be a cardinal consideration for sustainability of Islamic/non-interest banking in Nigeria and the world over. While a background is laid by considering the general situations at a global scale, a particular attention is devoted to the peculiar circumstances of the non-interest banking in Nigeria. In bring home the points various efforts of major notable supra-national institutions in bridging liquidity management gap in Islamic banks are presented. While it is believed that a good lesson could be learnt from the developmental phases of Malaysian Islamic banking system and the approaches to meeting its liquidity management problems, much emphasis is laid in maintaining that, although in the absence of political will to provide systemic support for non-interest banking in Nigeria, the challenge of liquidity management is not unsurmountable.

Keywords: deficit, liquidity management, non-interest, surplus

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

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In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: real-time spatial big data, quality of service, vertical partitioning, horizontal partitioning, matching algorithm, hamming distance, stream query

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Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

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Oil spills from tankers can cause significant harm to the environment and local communities, as well as have economic consequences. Early predictions of oil spills can help to minimize these impacts. Our proposed system uses machine learning and neural networks to predict potential oil spills by monitoring data from ship Automatic Identification Systems (AIS). The model analyzes ship movements, speeds, and changes in direction to identify patterns that deviate from the norm and could indicate a potential spill. Our approach not only identifies anomalies but also predicts spills before they occur, providing early detection and mitigation measures. This can prevent or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model's performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real-world oil spills, highlighting areas for improvement and further research.

Keywords: Anomaly Detection, Oil Spill Prediction, Machine Learning, Image Processing, Graph Neural Network (GNN)

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Authors: Scholastic Nangila Adeli, Francisca Mbutitia Ngithi

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Stigma is commonly associated with mental health patients and may act as a barrier to individuals who may seek or engage in treatment services. Stigmatization among university students is common whether they know someone with a mental health problem, or have a good knowledge and experience of mental health issues. The objective of this study was to establish the various barriers that prevent university students who have mental health challenges from seeking treatment and care. The study was a descriptive in nature where 320 respondents helped to establish the barriers to treatment or participation in mental health care among university students. A questionnaire was used to help establish the barriers and attitude towards mental illness among the respondents. Results from this study revealed that mental illnesses are common among university students and they are manifested in different forms like; anxiety and panic attacks, mood and eating disorders, Impulse control leading to gambling, alcohol and drug addictions, anger and depression leading to loneliness. Mental stigma (both social and self) was the major barrier with 62% of the respondents stating that social stigma was worse than self-stigma. This is because of the social discrimination towards the victim of mental challenges. On issues of attitude, 71% of the respondents said that they can never admit that they have a mental issue and would rather secretly seek clinical or psychological help for fear of being discriminated or excluded by peers. This view is informed by the societal belief that people with mental health challenges were dangerous (associating them with criminal behavior) and hard to socialize with or help. From the findings of this study, it is concluded that mental health problems are real among university students in Kenya and it is important for the university environment to minimize or eradicate stigma within the social circles. Stigma can be minimized or eradicated by creating awareness among university students and fostering social inclusion so that the students who have mental health challenges can experience a sense of belonging and acceptance hence build their self-esteem.

Keywords: disorders, impulse control, mental health problems, stigma

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Authors: Arshia Aflaki, Hadis Karimipour, Anik Islam

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Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent.

Keywords: generative adversarial attack, deep reinforcement learning, deep learning, IIoT, generative adversarial networks, power system

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Authors: Christine K. Fulmer

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Social work education is competency based in nature. There is an expectation that graduates of social work programs throughout the world are to be prepared to practice at a level of competence, which is beneficial to both the well-being of individuals and community. Experiential learning is one way to prepare students for competent practice. The use of Problem-Based Learning (PBL) is a form experiential education that has been successful in a number of disciplines to bridge the gap between the theoretical concepts in the classroom to the real world. PBL aligns with the constructivist theoretical approach to learning, which emphasizes the integration of new knowledge with the beliefs students already hold. In addition, the basic tenants of PBL correspond well with the practice behaviors associated with social work practice including multi-disciplinary collaboration and critical thinking. This paper makes an argument for utilizing PBL in social work education.

Keywords: social work education, problem-based learning, pedagogy, experiential learning, constructivist theoretical approach

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Authors: Shakir Ullah

Abstract:

This article reviews social science understanding to explore the challenges megaprojects face before and after implementation. It also sheds light on the problems directly and indirectly caused by mega development projects in the project implemented areas. By Using a qualitative approach such as thematic analysis, the article uses recent literature such as published articles, government reports, and books to cite examples of different mega projects worldwide. The study report that mega development projects are a necessary element of the modern-day infrastructural development process as they represent the perfect example of urban socioeconomic development. They are introduced and implemented by multinational companies with the support of state authorities to produce the common good. However, they are not devoid of their critical challenges and bring implicit and explicit problems to the targeted localities. The article takes insights from social science research for suggestions on how to reduce the challenges faced by project implementers and problems received by local people due to the fault lines of such projects.

Keywords: development, mega-projects, challenges, problems

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Authors: Tamara Matevosyan

Abstract:

In this globalized world, intercultural communication is becoming essential for understanding communication among people, for developing understanding of cultures, to appreciate the opportunities and challenges that each culture presents to people. Moreover, it plays an important role in developing an ideal personification to understand different behaviors in different cultures. Native speakers assimilate sociolinguistic knowledge in natural conditions, while it is a great problem for language learners, and in this context feature films reveal cultural peculiarities and involve students in real communication. As we know nowadays the key role of language learning is the development of intercultural competence as communicating with someone from a different cultural background can be exciting and scary, frustrating and enlightening. Intercultural competence is important in FL learning classroom and here feature films can perform as essential tools to develop this competence and overcome the intercultural gap that foreign students face. Current proposal attempts to reveal the correlation of the given culture and language through feature films. To ensure qualified, well-organized and practical classes on Intercultural Communication for language learners a number of methods connected with movie watching have been implemented. All the pre-watching, while watching and post-watching methods and techniques are aimed at developing students’ communicative competence. The application of such activities as Climax, Role-play, Interactive Language, Daily Life helps to reveal and overcome mistakes of cultural and pragmatic character. All the above-mentioned activities are directed at the assimilation of the language vocabulary with special reference to the given culture. The study dwells into the essence of culture as one of the core concepts of intercultural communication. Sometimes culture is not a priority in the process of language learning which leads to further misunderstandings in real life communication. The application of various methods and techniques with feature films aims at developing students’ cultural competence, their understanding of norms and values of individual cultures. Thus, feature film activities will enable learners to enlarge their knowledge of the particular culture and develop a fundamental insight into intercultural communication.

Keywords: climax, intercultural competence, interactive language, role-play

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Authors: Zeliha Sayar

Abstract:

Central banks all over the world appear to be focusing first and foremost on retail central bank digital currency CBDC), i.e., digital cash/money. This approach is predicated on the belief that the use of cash has decreased, owing primarily to technological advancements and pandemic restrictions, and that a suitable foundation for the transition to a cashless society has been revealed. This study aims to contribute to the debate over whether digital money/CBDC can be a substitute or supplement to physical cash by examining the potential effects on cash demand. For this reason, this paper compares two emerging countries, Turkey, and Thailand, to demystify the impact of e-payment and COVID-19 restrictions on cash demand by employing fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and the canonical cointegrating regression (CCR). The currency in circulation in two emerging countries, Turkey and Thailand, was examined in order to estimate the elasticity of different types of retail payments. The results demonstrate that real internet and mobile, cart, contactless payment, and e-money are long-term determinants of real cash demand in these two developing countries. Furthermore, with the exception of contactless payments in Turkey, there is a positive relationship between the currency in circulation and the various types of retail payments. According to findings, COVID-19 restrictions encourage the demand for cash, resulting in cash hoarding.

Keywords: CCR, DOLS, e-money, FMOLS, real cash

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Authors: Emma Louise McKinney

Abstract:

There is a global move to integrate children who are Deaf and Hearing Impaired into regular classrooms with their hearing peers with an inclusive education framework. This paper examines the current education situation for children who are Deaf and Hearing Impaired in South Africa, Madagascar, Malawi, Zimbabwe, and Namibia. Qualitative data for this paper was obtained from the author’s experiences working as the Southern African Education Advisor for an international organization funding disability projects. It examines some of the challenges facing these children and their teachers relating to education. Challenges include cultural stigma relating to disability and deafness, a lack of hearing screening and early identification of deafness, schools in rural areas, special schools, specialist teacher training, equipment, understanding of how to implement policy, support, appropriate teaching methodologies, and sign language training and proficiency. On the other hand, in spite of the challenges some teachers are able to provide quality education to children who are Deaf and Hearing Impaired. This paper examines both the challenges as well as what teachers are doing to overcome these.

Keywords: education of children who are deaf and hearing impaired, Southern African experiences, challenges, triumphs

Procedia PDF Downloads 240