Search results for: distributed artificial intelligence

Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: Ji-Won Park, Jong-Wha Kim

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Triploid L. lancifolium have a wide geographic distribution. By contrast, diploid L. lancifolium have limited distributions in the islands and coastal regions of the South and West Korean Peninsula and northern Tsushima Island, Japan. L. lancifolium diploids and triploids are not sympatrically distributed with other lily species or ploidy lines in West Sea and South Sea Islands of the Korean Peninsula. This observation raises the following questions: 'Why have autotriploid L. lancifolium never been observed in those isolated islands?', 'What mechanism excludes the occurrence of autotriploids, if they arise?'. To determine the occurrence and survival of triploid plants in natural diploid populations of tiger lily (Lilium lancifolium), ploidy analysis was conducted on natural open-pollinated seeds produced from plants grown on isolated islands, and on hybrid seeds produced by artificial crossing between plant populations originating on different Korean islands. Normal seeds were classified into five grades depending on the ratio of embryo/endosperm lengths, including 5/5, 4/5, 3/5, 2/5, and 1/5. Triploids were not observed among seedlings produced from natural open pollinations on isolated islands. Triploids were detected only in seedlings of underdeveloped seed grades(3/5 and 2/5) from artificial crosses between populations from different isolated islands. The triploid occurrence frequency was calculated as 0.0 for natural open-pollinated seedlings and 0.000582 for artificial crosses(6 triploids from 10,303 seedlings). Triploids were produced from crosses between isolated populations located at least 70 km apart; no triploids were detected in inter-population crosses of plants originating on the same islands. Triploid seedlings have very low viability in soil. We analyzed factors affecting triploid occurrence and survival in natural diploid populations of L. lancifolium. The results suggest that triploids originate from fertilization between plants that are genetically isolated due to geographical isolation and/or genotypic differences.

Keywords: Lilium lancifolium, autotriploid, natural population, genetic distance, 2n female gamete

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Authors: Sylvie Michel, Emmanuelle Gagnou, Joanne Hamet

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New major ethical dilemmas are posed by artificial intelligence. This article identifies an existing gap between the ethical questions that AI/robotics researchers grapple with in their research practice and those identified by literature review. The objective is to understand which ethical dilemmas are identified or concern AI researchers in order to compare them with the existing literature. This will enable to conduct training and awareness initiatives for AI researchers, encouraging them to consider these questions during the development of AI. Qualitative analyses were conducted based on direct observation of an AI/Robotics research team focused on collaborative robotics over several months. Subsequently, semi-structured interviews were conducted with 16 members of the team. The entire process took place during the first semester of 2023. The observations were analyzed using an analytical framework, and the interviews were thematically analyzed using Nvivo software. While the literature identifies three primary ethical concerns regarding AI—transparency, bias, and responsibility—the results firstly demonstrate that AI researchers are primarily concerned with the publication and valorization of their work, with the initial ethical concerns revolving around this matter. Questions arise regarding the extent to which to "market" publications and the usefulness of some publications. Research ethics are a central consideration for these teams. Secondly, another result shows that the researchers studied adopt a consequentialist ethics (though not explicitly formulated as such). They ponder the consequences of their development in terms of safety (for humans in relation to Robots/AI), worker autonomy in relation to the robot, and the role of work in society (can robots take over jobs?). Lastly, results indicate that the ethical dilemmas highlighted in the literature (responsibility, transparency, bias) do not explicitly appear in AI/Robotics research. AI/robotics researchers raise specific and pragmatic ethical questions, primarily concerning publications initially and consequentialist considerations afterward. Results demonstrate that these concerns are distant from the existing literature. However, the dilemmas highlighted in the literature also deserve to be explicitly contemplated by researchers. This article proposes that the journals these researchers target should mandate ethical reflection for all presented works. Furthermore, results suggest offering awareness programs in the form of short educational sessions for researchers.

Keywords: ethics, artificial intelligence, research, robotics

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Authors: Basem Kamal Abasakhiroun Farag

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Fashion, like many other areas of design, has undergone numerous developments over the centuries. The aim of the article is to recognize and evaluate the importance of advanced technologies in fashion design and to examine how they are transforming the role of contemporary fashion designers by transforming the creative process. It also discusses how contemporary culture is involved in such developments and how it influences fashion design in terms of conceptualization and production. The methodology used is based on examining various examples of the use of technology in fashion design and drawing parallels between what was feasible then and what is feasible today. Comparison of case studies, examples of existing fashion designs and experiences with craft methods; We therefore observe patterns that help us predict the direction of future developments in this area. Discussing the technological elements in fashion design helps us understand the driving force behind the trend. The research presented in the article shows that there is a trend towards significantly increasing interest and progress in the field of fashion technology, leading to the emergence of hybrid artisanal methods. In summary, as fashion technologies advance, their role in clothing production is becoming increasingly important, extending far beyond the humble sewing machine.

Keywords: fashion, identity, such, textiles ambient intelligence, proximity sensors, shape memory materials, sound sensing garments, wearable technology bio textiles, fashion trends, nano textiles, new materials, smart textiles, techno textiles fashion design, functional aesthetics, 3D printing.

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Authors: M. Habab, C. Benachaiba, B. Mazari, H. Madi, C. Benoudjafer

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The use of Distributed Generation (DG) has been increasing in recent years to fill the gap between energy supply and demand. This paper presents the grid connected wind energy system with UPQC based on fuzzy controller to compensate for voltage and current disturbances. The proposed system can improve power quality at the point of installation on power distribution systems. Simulation results show the capability of the DG-UPQC intelligent system to compensate sags voltage and current harmonics at the Point of Common Coupling (PCC).

Keywords: shunt active filter, series active filter, UPQC, power quality, sags voltage, distributed generation, wind turbine

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Authors: Joshua Harrell, Gideon Osei Bonsu, Susan Garza, Clarence Conner, Da’Neisha Harris, Emma Bukoswki, Zohreh Safari

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The Social Vulnerability Index (SVI) stands as a pivotal tool for gauging community resilience amidst diverse stressors, including pandemics like COVID-19. This paper synthesizes recent research and underscores the significance of SVI in elucidating the differential impacts of crises on communities. Drawing on studies by Fox et al. (2023) and Mah et al. (2023), we delve into the application of SVI alongside emerging data sources to uncover nuanced insights into community vulnerability. Specifically, we explore the utilization of SVI in conjunction with mobility data from platforms like SafeGraph to probe the intricate relationship between social vulnerability and mobility dynamics during the COVID-19 pandemic. By leveraging 16 community variables derived from the American Community Survey, including socioeconomic status and demographic characteristics, SVI offers actionable intelligence for guiding targeted interventions and resource allocation. Building upon recent advancements, this paper contributes to the discourse on harnessing AI techniques to mitigate health disparities and fortify public health resilience in the face of pandemics and other crises.

Keywords: social vulnerability index, mobility dynamics, data analytics, health equity, pandemic preparedness, targeted interventions, data integration

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Authors: Nashwah Alnajjar

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This research paper provides an overview of Replika, an AI chatbot application that uses natural language processing technology to engage in conversations with users. The app was developed to provide users with a virtual AI friend who can converse with them on various topics, including mental health. This study explores the experiences of Replika users using quantitative research methodology. A survey was conducted with 12 participants to collect data on their demographics, usage patterns, and experiences with the Replika app. The results showed that Replika has the potential to play a role in mental health support and well-being.

Keywords: Replika, chatbot, mental health, artificial intelligence, natural language processing

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Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis

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Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent, reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.

Keywords: deep reinforcement learning, Python, PyTorch, distributed training, modularity, library

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Authors: Muhammad Zaheer Babar, Amer Kashif, Muhammad Rizwan Javed

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Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile.

Keywords: Distributed generation (DG), Multi Objective Particle Swarm Optimization (MOPSO), particle swarm optimization (PSO), IEEE standard Test System

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Alejandro Tellez-Jurado, Juan C. Seck-Tuoh-Mora, Eva S. Hernandez-Gress, Norberto Hernandez-Romero, Iaina P. Medina-Serna

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Trichoderma harzianum is a fungus that has been utilized as a low-cost fungicide for biological control of pests, and it is important to determine the optimal conditions to produce the highest amount of conidiospores of Trichoderma harzianum. In this work, the conidiospore production of Trichoderma harzianum is modeled and optimized by using Artificial Neural Networks (AANs). In order to gather data of this process, 30 experiments were carried out taking into account the number of hours of culture (10 distributed values from 48 to 136 hours) and the culture humidity (70, 75 and 80 percent), obtained as a response the number of conidiospores per gram of dry mass. The experimental results were used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers, and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The ANN with the best performance was chosen in order to simulate the process and be able to maximize the conidiospores production. The obtained ANN with the highest performance has 2 inputs and 1 output, three hidden layers with 3, 10 and 10 neurons in each layer, respectively. The ANN performance shows an R2 value of 0.9900, and the Root Mean Squared Error is 1.2020. This ANN predicted that 644175467 conidiospores per gram of dry mass are the maximum amount obtained in 117 hours of culture and 77% of culture humidity. In summary, the ANN approach is suitable to represent the conidiospores production of Trichoderma harzianum because the R2 value denotes a good fitting of experimental results, and the obtained ANN model was used to find the parameters to produce the biggest amount of conidiospores per gram of dry mass.

Keywords: Trichoderma harzianum, modeling, optimization, artificial neural network

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Authors: Swasti Singh Ghai

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Creativity is understood as an intersubjective phenomenon shaped by socio-cultural values and economic forces. Creativity as a means to achieve progress is a very modern concept, driven by a global capitalist market economy. The dominant urban, often first-world articulations of creativity, overshadow the rural, local and cultural notions of people in the developing nations. Artisanal practices of making grounded in preindustrial and pre-capitalist contexts hold varying cultural and region-specific concepts and standards for ascribing creativity to a person or product, or process. These notions reflect the underlying philosophy that constitutes their worldview. The process of colonization through western education has blurred or overlapped some of these key philosophical concepts. This article adopts a post-colonial stance to understand the perceptions of skill, intelligence and creativity among tradition rooted textile craft practitioners of Kutch, Gujarat in India. The artisans, while negotiating their space in the contemporary markets, are making efforts to include the modern categories of art, craft, and design in their worldview. The paper will first review theories of creativity that throw light on the link between skill, intelligence and creativity. Then the paper will use secondary research and data from interviews to share crafts person notions of skill, creativity and intelligence and their interrelationship.

Keywords: traditional craft, textile, creativity, skill, intelligence

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Authors: Nisha Harry

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The study discussed in this article sought to assess whether a sense of professional efficacy mediates the relationship between personal factors and career adaptability. A quantitative cross-sectional survey approach was followed. A non–probability sample of (N = 409) of which predominantly early career and permanently employed black females in call centres in Africa participated in this study. In order to assess personal factors, the participants completed sense of meaningfulness and emotional intelligence measures. Measures of professional efficacy and career adaptability were also completed. The results of the mediational analysis revealed that professional efficacy significantly mediates the meaningfulness (sense of coherence) and career adaptability relationship, but not the emotional intelligence–career adaptability relationship. Call centre agents with professional efficacy are likely to be more work engaged as a result of their sense of meaningfulness and emotional intelligence.

Keywords: call centre, professional efficacy, career adaptability, emotional intelligence

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Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

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The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

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Authors: Vinamra Misra

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This paper presents a framework for integrating artificial intelligence (AI/ML] into the underwriting and risk management process for drone insurance. By combining AI-powered flight path/route optimisation, real-time weather data, airspace analytics, and pilot recommendations, we propose a method for dynamically assessing the risk profile of drone flights scheduled. This system can be deployed via a mobile or web application, enabling real-time decision-making for both insurance providers and drone operators using drones for commercial or recreational purposes. The proposed model has the potential to enhance operational efficiency, safety, and the overall quality of insurance products for drones.

Keywords: AI, insurance, drones, ML, aerospace

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Authors: Ashima Rai

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CATV transmission systems have altered from old cable based one-way analog video transmission to two ways hybrid fiber transmission. The use of optical fiber reduces the RF amplifiers in the transmission, high transmission power or lower fiber transmission losses are required to increase system capability. This paper evaluates and compares Distributed Feedback (DFB) laser and Vertical Cavity Surface Emitting Laser (VCSEL) for CATV transmission. The simulation results exhibit the better performer among both lasers taking into consideration the parameters chosen for evaluation.

Keywords: Distributed Feedback (DFB), Vertical Cavity Surface Emitting Laser (VCSEL), Community Access Television (CATV), Composite Second Order (CSO), Composite Triple Beat (CTB), RF

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Authors: Rooma Rani

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The education is really important for the improvement of physical and mental well-being of the school students. It is used to express inner potential, acquire knowledge, develop skills, shape habits, attitudes, values, belief, etc. along with providing strengths and resilience to people to changing situations and allowing them to develop all those capacities which will enable individual to control surrounding environment. Education has a significant effect on the behavior of individuals which helps us in the new situations of everyday life. Educating the child is directing the child’s capacities, attitudes interest, urges, and needs into the most desirable channels. We are the part of 21st century and now a day emotional intelligence is considered more important than intelligence in the success of a person. Success depends on several intelligences and on the control of emotions too. Emotional Intelligence, like general intelligence is the product of one’s heredity and its interaction with his environmental forces. There are certain methods evolved in modern researches. Keeping in view the nature and purpose of the study, the descriptive survey method is preferred. This method is one of the important methods in education research because it describes the current position of the phenomenon under study. The term descriptive survey is generally used for the type of research which proposes to condition of practices of the present time. In the present study, a systematically random sampling method was used to select a representative sample. 50 students were selected from 2 schools. Out of 50 students, 25 were boys and 25 were girls. In the study, a) it has been found a significant difference in the level of adjustment between male and female students; b) it has been found a non-significant difference in the level of emotional intelligence between male and female students; c) it has been found a non-significant relationship between adjustment and emotional intelligence among male students; d) it has been found a significant relationship between adjustment and emotional intelligence among male students. The results of the study indicated that amongst the students those who possess high scores on emotional intelligence tests are high in level of adjustment. Measures should be adopted to improve and sustain the emotional intelligence level of students throughout their studies. Adolescent students are prone to many problems like physical, social and psychological. They need a congenial home atmosphere so that they grow into full-fledged citizens of our country. After understanding these, it helps in the development of personality which leads to a better learning situation and better thinking capacities, in turn, enhances adjustment and achievement along with a better perception of self.

Keywords: adjustment, education, emotional intelligence, students

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Authors: Serena Gomez, Raeesa Tanseen, Netra Shaligram, Nithin Francis, Sandesh B. J.

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The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations.

Keywords: microbes, metabolic modeling, flux balance analysis, artificial bee colony

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Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

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The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Lucie Bartosova

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The rapid advancements in generative artificial intelligence (AI) have introduced transformative possibilities in education, particularly in assessment methodologies. This work provides an overview of the current state of the literature on AI-scoring methodologies for evaluating student-written responses. The focus is on how these innovations can be leveraged within large-scale assessments to address resource constraints such as limited assessors, time, and budget. Drawing from an initiative tied to a language test used for admitting candidates into a pre-service education program in the Faculty of Education at an Ontario university, the review explores the practical and ethical implications of integrating AI-driven tools into assessment processes. These tools are designed to automate the evaluation of learners’ written compositions, provide performance feedback, and support grading procedures. By synthesizing findings from recent research, the review highlights the effectiveness, reliability, and potential biases of AI in scoring, alongside considerations for transparency and fairness. This work emphasizes the dual role of generative AI as both a practical solution for scaling assessments and a subject of critical scrutiny to ensure its responsible implementation. The proposed integration of AI-scoring methodologies in our language test underscores the need to balance innovation with accountability, ensuring that AI tools enhance, rather than compromise, educational equity and rigor. OBJECTIVES OF YOUR RESEARCH To determine which generative AI model is most capable of evaluating written responses for university assessments based on specific criteria and to investigate potential biases within AI models to ensure fair assessments. METHODOLOGIES Evaluating generative AI models to determine their performance in assessing written responses against specific criteria. Collecting responses from previous assessments and annotating them with expert feedback to train and validate the AI models. MAIN CONTRIBUTIONS Introducing a tailored AI model to assess written responses on language tests. Offering a scalable and replicable model that informs broader applications of AI in educational assessments, contributing to policy-making and institutional best practices.

Keywords: artificial intelligence, assessment practices, student written performance, automated essay scoring, language proficiency

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Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

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Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: artificial neural network, bending angle, fuzzy logic, laser forming

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Authors: Muhasin Koyiloth, Sathyanarayana N. Gummadi

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Biological membranes are ordered association of lipids, proteins, and carbohydrates. Lipids except sterols possess asymmetric distribution across the bilayer. Eukaryotic membranes possess a group of lipid translocators called scramblases that disrupt phospholipid asymmetry. Their action is implicated in cell activation during wound healing and phagocytic clearance of apoptotic cells. Cholesterol is one of the major membrane lipids distributed evenly on both the leaflet and can directly influence the membrane fluidity through the ordering effect. The fluidity has an impact on the activity of several membrane proteins. The palmitoylated phospholipid scramblases localized to the lipid raft which is characterized by a higher number of sterols. Here we propose that cholesterol can interact with scramblases through putative CRAC motif and can modulate their activity. To prove this, we reconstituted phospholipid scramblase 1 of C. elegans (SCRM-1) in proteoliposomes containing different amounts of cholesterol (Liquid ordered/Lo). We noted that the presence of cholesterol reduced the scramblase activity of wild-type SCRM-1. The interaction between SCRM-1 and cholesterol was confirmed by fluorescence spectroscopy using NBD-Chol. Also, we observed loss of such interaction when one of I273 in the CRAC motif mutated to Asp. Interestingly, the point mutant has partially retained scramblase activity in Lo vesicles. The current study elucidated the important interaction between cholesterol and SCRM-1 to fine-tune its activity in artificial membranes.

Keywords: artificial membranes, CRAC motif, plasma membrane, PL scramblase

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Kerols Seif Said Botros

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The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Elham Jafari

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The aim of this study was to evaluate the effectiveness of emotional intelligence training on reducing marital conflict and marital adjustment in married students of the University of Tehran. This research is an applied type in terms of purpose and a semi-experimental design of pre-test-post-test type with the control group and with follow-up test in terms of the data collection method. The statistical population of the present study consisted of all married students of the University of Tehran. In this study, 30 married students of the University of Tehran were selected by convenience sampling method as a sample that 15 people in the experimental group and 15 people in the control group were randomly selected. The method of data collection in this research was field and library. The data collection tool in the field section was two questionnaires of marital conflict and marital adjustment. To analyze the collected data, first at the descriptive level, using statistical indicators, the demographic characteristics of the sample were described by SPSS software. In inferential statistics, the statistical method used was the test of analysis of covariance. The results showed that the effect of the independent variable of emotional intelligence on the reduction of marital conflicts is statistically significant. And it can be inferred that emotional intelligence training has reduced the marital conflicts of married students of the University of Tehran in the experimental group compared to the control group. Also, the effect of the independent variable of emotional intelligence on marital adjustment was statistically significant. It can be inferred that emotional intelligence training has adjusted the marital adjustment of married students of the University of Tehran in the experimental group compared to the control group.

Keywords: emotional intelligence, marital conflicts, marital compatibility, married students

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Authors: Dehini Rachid, Ferdi Brahim

Abstract:

The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

Procedia PDF Downloads 389

Authors: Maria Poulou

Abstract:

The study investigated how teachers’ self-rated Emotional Intelligence (EI), competence in implementing Social and Emotional Learning (SEL) skills and teaching efficacy relate to teacher-student relationships and students’ emotional and behavioral difficulties. Participants were 98 elementary teachers from public schools in central Greece. They completed the Self-Rated Emotional Intelligence Scale (SREIS), the Teacher SEL Beliefs Scale, the Teachers’ Sense of Efficacy Scale (TSES), the Student-Teacher Relationships Scale-Short Form (STRS-SF) and the Strengths and Difficulties Questionnaire (SDQ) for 617 of their students, aged 6-11 years old. Structural equation modeling was used to examine an exploratory model of the variables. It was demonstrated that teachers’ emotional intelligence, SEL beliefs and teaching efficacy were significantly related to teacher-student relationships, but they were not related to students’ emotional and behavioral difficulties. Rather, teachers’ perceptions of teacher-students relationships were significantly related to these difficulties. These findings and their implications for research and practice are discussed.

Keywords: emotional intelligence, social and emotional learning, teacher-student relationships, teaching efficacy

Procedia PDF Downloads 442

Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

Abstract:

This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

Procedia PDF Downloads 174