Search results for: artificial intelligence in semiconductor manufacturing

Authors: Cristina Costa-Lobo, Carla B. Vestena, Filomena E. Ponte

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Creativity and intelligence are concepts that have aroused very expressive interest in the field of educational sciences and the field of psychological science since the middle of the last century since they have a great impact on the potential and well-being of individuals. However, due to progress in cognitive and positive psychology, there has been a growing interest in the psychoeducational domain of intelligence and creativity in the last decade. In this theoretical work, are analyzed comparatively the theoretical models that relate the intelligence and the creativity, are analyzed several psychoeducational intervention programs that have been implemented with a view to the promotion of creativity and signal possibilities, realities and ironies around the psychological evaluation of intelligence and creativity. In order to reach a broad perspective on creativity, the evidence is presented that points the need to evaluate different psychological domains. The psychoeducational intervention programs addressed have, with a common characteristic, the full stimulation of the creative potential of the participants, assumed as a highly valued capacity at the present time. The results point to the systematize that all interventions in the ambit of creativity have two guiding principles: all individuals can be creative, and creativity is a capacity that can be stimulated. This work refers to the importance of stimulus creativity in educational contexts, to the usefulness and pertinence of the creation, the implementation, and monitoring of flexible curricula, adapted to the educational needs of students, promoting a collaborative work among teachers, parents, students, psychologists, managers and educational administrators.

Keywords: creativity, intelligence, psychoeducational intervention programs, psychological evaluation, educational contexts

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Authors: Peter Mc Coll, Alberto Caro, Chiara Gandolfo, Montserrat Labbe, Francisca Schnaidt, Michela Palazzi

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Self-esteem and emotional intelligence are variables that are related to people's nutritional status. Self-esteem may be at low levels in people living with obesity, while emotional intelligence can play an important role in the way people living with obesity cope. The objective of the study was to measure the association between self-esteem and emotional intelligence to nutritional status in adolescent population. Methodology: A cross-sectional study was carried out with 179 adolescent schoolchildren between 13 and 19 years old from a public school. The objective was to evaluate nutritional status; weight and height were measured by calculating the body mass index and Z score. Self-esteem was evaluated using the Coopersmith Self-esteem Inventory adapted by Brinkmann and Segure. Emotional intelligence was measured using the Emotional Quotient Inventory: short, by Bar On, adapted questionnaire, translated into Spanish by López Zafra. For statistical analysis: Pearson's Chi-square test, Pearson's correlation, and odd ratio calculation were used, with a p value at a significance level < 5%. Results: The study group was composed of 71% female and 29% male. The nutritional status was distributed as eutrophic 41.9%, overweight 20.1%, and obesity 21.1%. In relation to self-esteem, 44.1% presented low and very low levels, without differences by gender. Emotional intelligence was distributed: low 3.4%, medium 81%, and high 13.4% -no differences according to gender. The association between nutritional status (overweight and obesity) with low and very low self-esteem, an odds ratio of 2.5 (95% CI 1.12 – 5.59) was obtained with a p-value = 0.02. The correlation analysis between the intrapersonal sub-dimension emotional intelligence scores and the Z score of nutritional status presented a negative correlation of r = - 0.209 with a p-value < 0.005. The correlation between emotional intelligence subdimension stress management with Z score presented a positive correlation of r = 0.0161 with a p-value < 0.05. In conclusion, the group of adolescents studied had a high prevalence of overweight and obesity, a high prevalence of low self-esteem, and a high prevalence of average emotional intelligence. Overweight and obese adolescents were 2.5 times more likely to have low self-esteem. As overweight and obesity increase, self-esteem decreases, and the ability to manage stress increases.

Keywords: self-esteem, emotional intelligence, obesity, adolescent, nutritional status

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Authors: Sean Goltz, Michael Mayo

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The purpose of this research is to use Global-Regulation.com database of the world laws, focusing on tax treaties between countries, in order to create an AI-driven tax simulator that will run an AI agent through potential tax scenarios across countries. The AI agent goal is to identify the scenario that will result in minimum tax liability based on tax treaties between countries. The results will be visualized by a three dimensional matrix. This will be an online web application. Multinational corporations are running their business through multiple countries. These countries, in turn, have a tax treaty with many other countries to regulate the payment of taxes on income that is transferred between these countries. As a result, planning the best tax scenario across multiple countries and numerous tax treaties is almost impossible. This research propose to use Global-Regulation.com database of word laws in English (machine translated by Google and Microsoft API’s) in order to create a simulator that will include the information in the tax treaties. Once ready, an AI agent will be sent through the simulator to identify the scenario that will result in minimum tax liability. Identifying the best tax scenario across countries may save multinational corporations, like Google, billions of dollars annually. Given the nature of the raw data and the domain of taxes (i.e., numbers), this is a promising ground to employ artificial intelligence towards a practical and beneficial purpose.

Keywords: taxation, law, multinational, corporation

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Authors: Seyyedeh Mahdis Mousavi, Masoud Maghsoudi, Zahra Vahed

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The purpose of this paper is to examine the relationship between emotional intelligence as conceptualized in Goleman’s competency model, and decision making styles in levels of management. To conduct this study, different level managers in Iran Broadcasting Organization completed a questionnaire on emotional intelligence and decision making styles. Researcher used descriptive and inferential statistics to describe data and analyze the two variables relationship in managers of three levels. Results revealed significant relationships for rational, dependent, avoidant, and spontaneous styles. No significant relationship was found for intuitive style. Yet the results indicate that avoidant style has negative relation to EI. Furthermore, EI has direct and strong relation to rational style.

Keywords: emotional intelligence (EI), decision making styles, Islamic Republic of Iran Broadcasting (IRIB), Iranian manager

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Authors: Amritpal Slaich, Mark Elshaw

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This paper explores the development of a decision support framework for the introduction of business intelligence (BI) through operational research techniques for application by SMEs. Aligned with the goals of the new Midlands Enterprise Initiative of improving the skill levels of the Midlands workforce and addressing high levels of regional unemployment, we have developed a framework to increase the level of business intelligence used by SMEs to improve business decision-making. Many SMEs in the Midlands fail due to the lack of high quality decision making. Our framework outlines how universities can: engage with SMEs in the use of BI through operational research techniques; develop appropriate and easy to use Excel spreadsheet models; and make use of a process to allow SMEs to feedback their findings of the models. Future work will determine how well the framework performs in getting SMEs to apply BI to improve their decision-making performance.

Keywords: SMEs, decision support framework, business intelligence, operational research techniques

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Authors: Kaivan Karami, Sahalu Hassan, Sanna Taking, Afesome Ofiare, Aniket Dhongde, Abdullah Al-Khalidi, Edward Wasige

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We have made a comparative study on the influence of Al₂O₃ and HfO₂ grown using atomic layer deposition (ALD) technique as dielectric in the AlGaN/GaN metal oxide semiconductor high electron mobility transistor (MOS-HEMT) structure. Five samples consisting of 20 nm and 10 nm each of Al₂O₃ and HfO₂ respectively and a Schottky gate HEMT, were fabricated and measured. The threshold voltage shifts towards negative by 0.1 V and 1.8 V for 10 nm thick HfO2 and 10 nm thick Al₂O₃ gate dielectric layers respectively. The negative shift for the 20 nm HfO2 and 20 nm Al₂O₃ were 1.2 V and 4.9 V respectively. Higher gm/IDS (transconductance to drain current) ratio was also obtained in HfO₂ than Al₂O₃. With both materials as dielectric, a significant reduction in the gate leakage current in the order of 10^4 was obtained compared to the sample without the dielectric material.

Keywords: AlGaN/GaN HEMTs, Al2O3, HfO2, MOSHEMTs.

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Authors: László Schmidt

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Technological and digital innovation is constantly and dynamically evolving, which poses an enormous challenge to both lawmaking and law enforcement. To legislation because artificial intelligence permeates many areas of people’s daily lives that the legislator must regulate. it can see how challenging it is to regulate e.g. self-driving cars/taxis/camions etc. Not to mention cryptocurrencies and Chat GPT, the use of which also requires legislative intervention. Artificial intelligence also poses an extraordinary challenge to law enforcement. In criminal cases, police and prosecutors can make great use of AI in investigations, e.g. in forensics, DNA samples, reconstruction, identification, etc. But it can also be of great help in the detection of crimes committed in cyberspace. In the case of cybercrime, on the one hand, it can be viewed as a new type of crime that can only be committed with the help of information systems, and that has a specific protected legal object, such as an information system or data. On the other hand, it also includes traditional crimes that are much easier to commit with the help of new tools. According to Hungarian Criminal Code section 375 (1), any person who, for unlawful financial gain, introduces data into an information system, or alters or deletes data processed therein, or renders data inaccessible, or otherwise interferes with the functioning of the information system, and thereby causes damage, is guilty of a felony punishable by imprisonment not exceeding three years. The Covid-19 coronavirus epidemic has had a significant impact on our lives and our daily lives. It was no different in the world of crime. With people staying at home for months, schools, restaurants, theatres, cinemas closed, and no travel, criminals have had to change their ways. Criminals were committing crimes online in even greater numbers than before. These crimes were very diverse, ranging from false fundraising, the collection and misuse of personal data, extortion to fraud on various online marketplaces. The most vulnerable age groups (minors and elderly) could be made more aware and prevented from becoming victims of this type of crime through targeted programmes. The aim of the study is to show the Hungarian judicial practice in relation to cybercrime and possible preventive solutions.

Keywords: cybercrime, COVID-19, Hungary, criminal law

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Authors: Mehrzad Abdi Khalife, Iraj Mahdavi

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This paper presents the agile model for project management process. For project management process, the Project Management Body of Knowledge (PMBOK) guideline has been selected as platform. Combination of computational science and artificial intelligent methodology has been added to the guideline to transfer the standard to agile project management process. The model is the combination of practical standard, computational science and artificial intelligent. In this model, we present communication model and protocols to keep process agile. Here, we illustrate the collaboration man and machine in project management area with artificial intelligent approach.

Keywords: artificial intelligent, conceptual model, man-machine collaboration, project management, standard

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Authors: Qi Liu, Jiqin Yao, Xiaohu Zhou, Bo Zheng

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The first artificial cell was produced by Thomas Chang in the 1950s when he was trying to make a mimic of red blood cells. Since then, many different types of artificial cells have been constructed from one of the two approaches: a so-called bottom-up approach, which aims to create a cell from scratch, and a top-down approach, in which genes are sequentially knocked out from organisms until only the minimal genome required for sustaining life remains. In this project, bottom-up approach was used to build a new cell-free expression system which mimics artificial cell that capable of protein expression and communicate with each other. The artificial cells constructed from the bottom-up approach are usually lipid vesicles, polymersomes, hydrogels or aqueous droplets containing the nucleic acids and transcription-translation machinery. However, lipid vesicles based artificial cells capable of communication present several issues in the cell communication research: (1) The lipid vesicles normally lose the important functions such as protein expression within a few hours. (2) The lipid membrane allows the permeation of only small molecules and limits the types of molecules that can be sensed and released to the surrounding environment for chemical communication; (3) The lipid vesicles are prone to rupture due to the imbalance of the osmotic pressure. To address these issues, the hydrogel-based artificial cells were constructed in this work. To construct the artificial cell, polyacrylamide hydrogel was functionalized with Acrylate PEG Succinimidyl Carboxymethyl Ester (ACLT-PEG2000-SCM) moiety on the polymer backbone. The proteinaceous factors can then be immobilized on the polymer backbone by the reaction between primary amines of proteins and N-hydroxysuccinimide esters (NHS esters) of ACLT-PEG2000-SCM, the plasmid template and ribosome were encapsulated inside the hydrogel particles. Because the artificial cell could continuously express protein with the supply of nutrients and energy, the artificial cell-artificial cell communication and artificial cell-natural cell communication could be achieved by combining the artificial cell vector with designed plasmids. The plasmids were designed referring to the quorum sensing (QS) system of bacteria, which largely relied on cognate acyl-homoserine lactone (AHL) / transcription pairs. In one communication pair, “sender” is the artificial cell or natural cell that can produce AHL signal molecule by synthesizing the corresponding signal synthase that catalyzed the conversion of S-adenosyl-L-methionine (SAM) into AHL, while the “receiver” is the artificial cell or natural cell that can sense the quorum sensing signaling molecule form “sender” and in turn express the gene of interest. In the experiment, GFP was first immobilized inside the hydrogel particle to prove that the functionalized hydrogel particles could be used for protein binding. After that, the successful communication between artificial cell-artificial cell and artificial cell-natural cell was demonstrated, the successful signal between artificial cell-artificial cell or artificial cell-natural cell could be observed by recording the fluorescence signal increase. The hydrogel-based artificial cell designed in this work can help to study the complex communication system in bacteria, it can also be further developed for therapeutic applications.

Keywords: artificial cell, cell-free system, gene circuit, synthetic biology

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Samiksha Raviraja, Junaid Arif

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One of the most important abilities in species is the ability to communicate. This paper proposes steps to take when and if aliens came in contact with humans, and how humans would communicate with them. The situation would be a time-sensitive scenario, meaning that communication is at the utmost importance if such an event were to happen. First, humans would need to establish mutual peace by conveying that there is no threat to the alien race. Second, the aliens would need to acknowledge this understanding and reciprocate. This would be extremely difficult to do regardless of their intelligence level unless they are very human-like and have similarities to our way of communicating. The first step towards understanding their mind is to analyze their level of intelligence - Level 1-Low intelligence, Level 2-Human-like intelligence or Level 3-Advanced or High Intelligence. These three levels go hand in hand with the Kardashev scale. Further, the Barrow scale will also be used to categorize alien species in hopes of developing a common universal language to communicate in. This paper will delve into how the level of intelligence can be used toward achieving communication with aliens by predicting various possible scenarios and outcomes by proposing an intensive categorization system. This can be achieved by studying their Emotional and Intelligence Quotient (along with technological and scientific knowledge/intelligence). The limitations and capabilities of their intelligence must also be studied. By observing how they respond and react (expressions and senses) to different kinds of scenarios, items and people, the data will help enable good categorisation. It can be hypothesised that the more human-like aliens are or can relate to humans, the more likely it is that communication is possible. Depending on the situation, either human can teach aliens a human language, or humans can learn an alien language, or both races work together to develop a mutual understanding or mode of communication. There are three possible ways of contact. Aliens visit Earth, or humans discover aliens while on space exploration or through technology in the form of signals. A much rarer case would be humans and aliens running into each other during a space expedition of their own. The first two possibilities allow a more in-depth analysis of the alien life and enhanced results compared. The importance of finding a method of talking with aliens is important in order to not only protect Earth and humans but rather for the advancement of Science through the shared knowledge between the two species.

Keywords: intelligence, Kardashev scale, Barrow scale, alien civilizations, emotional and intelligence quotient

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Authors: Kayode Ilesanmi Ebenezer Bowale, Dominic Azuh, Busayo Aderounmu, Alfred Ilesanmi

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There has been a debate among scholars and policymakers about the effects of oil exploration and production on industrial development. In Nigeria, there were many reforms resulting in an increase in crude oil production in the recent past. There is a controversy on the importance of oil production in the development of the manufacturing sector in Nigeria. Some scholars claim that oil has been a blessing to the development of the manufacturing sector, while others regard it as a curse. The objective of the study is to determine if empirical analysis supports the presence of Dutch Disease and de-industrialisation in the Nigerian manufacturing sector between 2019- 2022. The study employed data that were sourced from World Development Indicators, Nigeria Bureau of Statistics, and the Central Bank of Nigeria Statistical Bulletin on manufactured exports, manufacturing employment, agricultural employment, and service employment in line with the theory of Dutch Disease using the unit root test to establish their level of stationarity, Engel and Granger cointegration test to check their long-run relationship. Autoregressive. Distributed Lagged bound test was also used. The Vector Error Correction Model will be carried out to determine the speed of adjustment of the manufacturing export and resource movement effect. The results showed that the Nigerian manufacturing industry suffered from both direct and indirect de-industrialisation over the period. The findings also revealed that there was resource movement as labour moved away from the manufacturing sector to both the oil sector and the services sector. The study concluded that there was the presence of Dutch Disease in the manufacturing industry, and the problem of de-industrialisation led to the crowding out of manufacturing output. The study recommends that efforts should be made to diversify the Nigerian economy. Furthermore, a conducive business environment should be provided to encourage more involvement of the private sector in the agriculture and manufacturing sectors of the economy.

Keywords: Dutch disease, resource movement, manufacturing sector performance, Nigeria

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Shreya Poddar

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Legal drafting and revising are recognized as highly demanding tasks for legal professionals. This paper introduces an approach to automate and refine these processes through the use of advanced Artificial Intelligence (AI). The method employs Large Language Models (LLMs), with a specific focus on 'Chain of Thoughts' (CoT) and knowledge injection via prompt engineering. This approach differs from conventional methods that depend on comprehensive training or fine-tuning of models with extensive legal knowledge bases, which are often expensive and time-consuming. The proposed method incorporates knowledge injection directly into prompts, thereby enabling the AI to generate more accurate and contextually appropriate legal texts. This approach substantially decreases the necessity for thorough model training while preserving high accuracy and relevance in drafting. Additionally, the concept of guardrails is introduced. These are predefined parameters or rules established within the AI system to ensure that the generated content adheres to legal standards and ethical guidelines. The practical implications of this method for legal work are considerable. It has the potential to markedly lessen the time lawyers allocate to document drafting and revision, freeing them to concentrate on more intricate and strategic facets of legal work. Furthermore, this method makes high-quality legal drafting more accessible, possibly reducing costs and expanding the availability of legal services. This paper will elucidate the methodology, providing specific examples and case studies to demonstrate the effectiveness of 'Chain of Thoughts' and knowledge injection in legal drafting. The potential challenges and limitations of this approach will also be discussed, along with future prospects and enhancements that could further advance legal work. The impact of this research on the legal industry is substantial. The adoption of AI-driven methods by legal professionals can lead to enhanced efficiency, precision, and consistency in legal drafting, thereby altering the landscape of legal work. This research adds to the expanding field of AI in law, introducing a method that could significantly alter the nature of legal drafting and practice.

Keywords: AI-driven legal drafting, legal automation, futureoflegalwork, largelanguagemodels

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Authors: Tori Reddy Dodla, Laura Ann Jones

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The objective of this study was to propose strategies to improve the efficiency of Knowledge Management Systems (KMS). This study highlights best practices from various industries to create an overall summary of Knowledge Management (KM) and efficiency in organizational performance. Results indicated eleven best practices for maximizing the efficiency of organizational KMS that can be divided into four categories: Designing the KMS, Identifying Case Studies, Implementing the KMS, and Promoting adoption and usage. Our findings can be used as a foundation for scholars to conduct further research on KMS efficiency.

Keywords: artificial intelligence, knowledge management efficiency, knowledge management systems, organizational performance

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Authors: Marwa Ahmed Abd El Hamied

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

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Authors: Keshav Dubey, Swajeeth Panchangam, Arun Rajendran, Swarnim Gupta

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In the semiconductor capital equipment industry, effective modeling of repairable system reliability is crucial for optimizing maintenance strategies and ensuring operational efficiency. However, repairable system reliability modeling using a renewal process is not as popular in the semiconductor equipment industry as it is in the locomotive and automotive industries. Utilization of this approach will help optimize maintenance practices. This paper presents a structured framework that leverages both parametric and non-parametric approaches to model the reliability of repairable subsystems based on operational data, maintenance schedules, and system-specific conditions. Data is organized at the equipment ID level, facilitating trend testing to uncover failure patterns and system degradation over time. For non-parametric modeling, the Mean Cumulative Function (Mean Cumulative Function) approach is applied, offering a flexible method to estimate the cumulative number of failures over time without assuming an underlying statistical distribution. This allows for empirical insights into subsystem failure behavior based on historical data. On the parametric side, virtual age modeling, along with Homogeneous and Non-Homogeneous Poisson Process (Homogeneous Poisson Process and Non-Homogeneous Poisson Process) models, is employed to quantify the effect of repairs and the aging process on subsystem reliability. These models allow for a more structured analysis by characterizing repair effectiveness and system wear-out trends over time. A comparison of various Generalized Renewal Process (GRP) approaches highlights their utility in modeling different repair effectiveness scenarios. These approaches provide a robust framework for assessing the impact of maintenance actions on system performance and reliability. By integrating both parametric and non-parametric methods, this framework offers a comprehensive toolset for reliability engineers to better understand equipment behavior, assess the effectiveness of maintenance activities, and make data-driven decisions that enhance system availability and operational performance in semiconductor fabrication facilities.

Keywords: reliability, maintainability, homegenous poission process, repairable system

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Authors: Faten Adel Ismael Chaqmaqchee

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Optical, electrical and optical-electrical characterisations of surface light emitting VCSOAs devices are reported. The hot electron light emitting and lasing in semiconductor hetero-structure vertical cavity semiconductor optical amplifier (HELLISH VCSOA) device is a surface emitter based on longitudinal injection of electron and hole pairs in their respective channels. Ga0.35In0.65N0.02As0.08/GaAs was used as an active material for operation in the 1.3 μm window of the optical communications. The device has undoped Distributed Bragg Reflectors (DBRs) and the current is injected longitudinally, directly into the active layers and does not involve DBRs. Therefore, problems associated with refractive index contrast and current injection through the DBR layers, which are common with the doped DBRs in conventional VCSOAs, are avoided. The highest gain of around 4 dB is obtained for the 1300 nm wavelength operation.

Keywords: HELLISH, VCSOA, GaInNAs, luminescence, gain

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Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials.

Keywords: model predictive control, optimal control, process control, crystal growth

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Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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Authors: Alena Kulikova, Tatjana Kanonire

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Intelligence research is one of the oldest fields of psychology. Many factors have made a research on intelligence, defined as reasoning and problem solving [1, 2], a very acute and urgent problem. Thus, it has been repeatedly shown that intelligence is a predictor of academic, professional, and social achievement in adulthood (for example, [3]); Moreover, intelligence predicts these achievements better than any other trait or ability [4]. The individual level, a comprehensive assessment of intelligence is a necessary criterion for the diagnosis of various mental conditions. For example, it is a necessary condition for psychological, medical and pedagogical commissions when deciding on educational needs and the most appropriate educational programs for school children. Assessment of intelligence is crucial in clinical psychodiagnostic and needs high-quality intelligence measurement tools. Therefore, it is not surprising that the development of intelligence tests is an essential part of psychological science and practice. Many modern intelligence tests have a long history and have been used for decades, for example, the Stanford-Binet test or the Wechsler test. However, the vast majority of these tests are based on the classic linear test structure, in which all respondents receive all tasks (see, for example, a critical review by [5]). This understanding of the testing procedure is a legacy of the pre-computer era, in which blank testing was the only diagnostic procedure available [6] and has some significant limitations that affect the reliability of the data obtained [7] and increased time costs. Another problem with measuring IQ is that classical line-structured tests do not fully allow to measure respondent's intellectual progress [8], which is undoubtedly a critical limitation. Advances in modern psychometrics allow for avoiding the limitations of existing tools. However, as in any rapidly developing industry, at the moment, psychometrics does not offer ready-made and straightforward solutions and requires additional research. In our presentation we would like to discuss the strengths and weaknesses of the current approaches to intelligence measurement and highlight “points of growth” for creating a test in accordance with modern psychometrics. Whether it is possible to create the instrument that will use all achievements of modern psychometric and remain valid and practically oriented. What would be the possible limitations for such an instrument? The theoretical framework and study design to create and validate the original Russian comprehensive computer test for measuring the intellectual development in school-age children will be presented.

Keywords: Intelligence, psychometrics, psychological measurement, computerized adaptive testing, multistage testing

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: E. Benga, T. Tengen, A. Alugongo

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Periodic and continuous inventory models are the two classical management tools used to handle inventories. These models have advantages and disadvantages. The implementation of both continuous (r,Q) inventory and periodic (R, S) inventory models in most manufacturing plants comes with higher cost. Such high inventory costs are due to the fact that most manufacturing plants are not flexible enough. Since demand and lead-time are two important variables of every inventory models, their effect on the flexibility of the manufacturing plant matter most. Unfortunately, these effects are not clearly understood by managers. The reason is that the decision parameters of the continuous (r, Q) inventory and periodic (R, S) inventory models are not designed to effectively deal with the issues of uncertainties such as poor manufacturing performances, delivery performance supplies performances. There is, therefore, a need to come up with a predictive and hybrid inventory model that can combine in some sense the feature of the aforementioned inventory models. A linear combination technique is used to hybridize both continuous (r, Q) inventory and periodic (R, S) inventory models. The behavior of such hybrid inventory model is described by a differential equation and then optimized. From the results obtained after simulation, the continuous (r, Q) inventory model is more effective than the periodic (R, S) inventory models in the short run, but this difference changes as time goes by. Because the hybrid inventory model is more cost effective than the continuous (r,Q) inventory and periodic (R, S) inventory models in long run, it should be implemented for strategic decisions.

Keywords: periodic inventory, continuous inventory, hybrid inventory, optimization, manufacturing plant

Procedia PDF Downloads 382

Authors: Ali Khaleel Kareem, Shian Gao, Ahmed Qasim Ahmed

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A numerical investigation of unsteady mixed convection heat transfer in a 3D moving top wall enclosure, which has a central rotating cylinder and uses either artificial roughness on the bottom hot plate or smooth bottom hot plate to study the heat transfer enhancement, is completed for fixed circular cylinder, and anticlockwise and clockwise rotational speeds, -1 ≤ Ω ≤ 1, at Reynolds number of 5000. The top lid-driven wall was cooled, while the other remaining walls that completed obstructed cubic were kept insulated and motionless. A standard k-ε model of Unsteady Reynolds-Averaged Navier-Stokes (URANS) method is involved to deal with turbulent flow. It has been clearly noted that artificial roughness can strongly control the thermal fields and fluid flow patterns. Ultimately, the heat transfer rate has been dramatically increased by involving artificial roughness on the heated bottom wall in the presence of rotating cylinder.

Keywords: artificial roughness, lid-driven cavity, mixed convection heat transfer, rotating cylinder, URANS method

Procedia PDF Downloads 198

Authors: Venkatesan Sundaramoorthy

Abstract:

The field of ceramics has undergone a remarkable transformation with the advent of additive manufacturing technologies. This comprehensive review explores the mechanical properties of additively manufactured ceramic components, focusing on key materials such as Alumina, Zirconia, and Silicon Carbide. The study delves into various authors' review technology into the various additive manufacturing techniques, including Stereolithography, Powder Bed Fusion, and Binder Jetting, highlighting their advantages and challenges. It provides a detailed analysis of the mechanical properties of these ceramics, offering insights into their hardness, strength, fracture toughness, and thermal conductivity. Factors affecting mechanical properties, such as microstructure and post-processing, are thoroughly examined. Recent advancements and future directions in 3D-printed ceramics are discussed, showcasing the potential for further optimization and innovation. This review underscores the profound implications of additive manufacturing for ceramics in industries such as aerospace, healthcare, and electronics, ushering in a new era of engineering and design possibilities for ceramic components.

Keywords: mechanical properties, additive manufacturing, ceramic materials, PBF

Procedia PDF Downloads 66

Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin

Abstract:

Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.

Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system

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Authors: R. K. Vinnakota, D. A. Genov, Z. Dong, A. F. Briggs, L. Nordin, S. R. Bank, D. Wasserman

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We present a semiconductor-based plasmonic electro-optic modulator based on excitation and active control of surface plasmon polaritons (SPPs) at the interface of degenerately doped In₀.₅₃Ga₀.₄₇As pn++ junctions. Set of devices, which we refer to as a surface plasmon polariton diode (SPPD), are fabricated and characterized electrically and optically. Optical characterization predicts far-field voltage-aided reflectivity modulation for mid-IR wavelengths. Numerical device characterizations using a self-consistent electro-optic multiphysics model have been performed to confirm the experimental findings were predicting data rates up to 1Gbits/s and 3dB bandwidth as high as 2GHz. Our findings also show that decreasing the device dimensions can potentially lead to data rates of more than 50Gbits/s, thus potentially providing a pathway toward fast all-semiconductor-based plasmotronic devices.

Keywords: plasmonics, optoelectronics, PN junctions, surface plasmon polaritons

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Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

Procedia PDF Downloads 407