Search results for: artificial roughness

Authors: Kiran Nair, Jincy George, Suhaib Anagreh

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Artificial intelligence (AI) in a marketing context is a form of a deterministic tool designed to optimize and enhance marketing tasks, research tools, and techniques. It is on the verge of transforming marketing roles and revolutionize the entire industry. This paper aims to explore the current dissemination of the application of artificial intelligence (AI) in the marketing mix, reviewing the scope and application of AI in various aspects of sales and distribution management. The paper also aims at identifying the areas of the strong impact of AI in factors of sales and distribution management such as distribution channel, purchase automation, customer service, merchandising automation, and shopping experiences. This is a qualitative research paper that aims to examine the impact of AI on sales and distribution management of 30 multinational brands in six different industries, namely: airline; automobile; banking and insurance; education; information technology; retail and telecom. Primary data is collected by means of interviews and questionnaires from a sample of 100 marketing managers that have been selected using convenient sampling method. The data is then analyzed using descriptive statistics, correlation analysis and multiple regression analysis. The study reveals that AI applications are extensively used in sales and distribution management, with a strong impact on various factors such as identifying new distribution channels, automation in merchandising, customer service, and purchase automation as well as sales processes. International brands have already integrated AI extensively in their day-to-day operations for better efficiency and improved market share while others are investing heavily in new AI applications for gaining competitive advantage.

Keywords: artificial intelligence, sales and distribution, marketing mix, distribution channel, customer service

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Authors: Sungpyo Kim, Changseok Oh, Ga-Young Lee, Hyun-Man Kim

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Intercellular junction of keratinocytes is crucial for epithelia to build an epithelial barrier. Junctional epithelium (JE) seals the interfaces between tooth and gingival tissue. Keratinocytes of JE attach to surfaces roughened by abrasion or erosion with aging. Thus behavior of oral keratinocytes on the rough substrates may help understand the epithelial seal of JE of which major intercellular junction is E-cadherin junction (ECJ). The present study investigated the influence of various substrate roughnesses on the development of ECJ between normal human gingival epithelial keratinocytes, HOK-16B cells. HOK-16B cells were slow in the development of ECJ on the rough substrates compared to on the smooth substrates. Furthermore, oral keratinocytes on the substrates of higher roughnesses were delayed in the development of E-cadherin junction than on the substrates of lower roughnesses. Delayed development of E-cadherin junction on the rough substrates was ascribed to the impaired spreading of cells and its higher JNK activity. Cells on the smooth substrates rapidly spread wide cytoplasmic extensions around cells. However, cells on the rough substrates slowly extended narrow cytoplasmic extensions of which number was limited due to the substrate irregularity. As these cytoplasmic extensions formed ECJ when met with the extensions of neighboring cells, thus, the present study demonstrated that a limited chance of contacts between cytoplasmic extensions due to the limited number of cytoplasmic extensions and slow development of cytoplasmic extensions brought about a delayed development of ECJ in oral keratinocytes on the rougher substrates. Sealing between cells was not complete because only part of cell membrane contributes to the formation of intercellular junction between cells on the substrates of higher roughnesses. Interestingly, inhibition of JNK activity promoted the development of ECJ on the rough substrates, of which mechanism remains to be studied further.

Keywords: substrate roughness, E-cadherin junction, oral keratinocyte, cell spreading, JNK

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Authors: Saif M. M. Obeidat

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This research investigates the impact of artificial intelligence (AI) on interior spaces within a college of Architecture and Design. Employing a qualitative approach, the study conducts in-depth interviews and reviews AI-integrated design projects within the academic setting. The key objectives include assessing AI integration in design processes, examining the influence of AI on user experience, exploring its role in architectural innovation, identifying challenges, and assessing educational implications. The study aims to provide a comprehensive understanding of AI's role in shaping interior spaces within academia. It anticipates improved efficiency in design processes, positive user feedback on functionality and experiences, the emergence of innovative design solutions, and the identification of challenges like ethical considerations and technical limitations. Additionally, the research expects insights into how educational programs may need to adapt to incorporate AI knowledge and skills, ensuring students are well-prepared for the evolving landscape of architecture and design practice. By addressing these objectives, the research contributes valuable insights into the evolving relationship between technology and the field of architecture, particularly within educational contexts.

Keywords: interior design, artificial intelligence, academic settings, technology, education

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Authors: Mehvish Bilal, Navneet Singh, Jasir Mushtaq

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Water pollution puts people's health at risk, and it can also impact the ecology. For practitioners of integrated water resources management (IWRM), water quality modelling may be useful for informing decisions about pollution control (such as discharge permitting) or demand management (such as abstraction permitting). To comprehend the current pollutant load, movement of effective load movement of contaminants generates effective relation between pollutants, mathematical simulation, source, and water quality is regarded as one of the best estimating tools. The current study involves the Qual2k model, which includes manual simulation of the various physiochemical characteristics of water. To this end, various sensors could be installed for the automatic simulation of various physiochemical characteristics of water. An artificial intelligence model has been proposed for the automatic simulation of water quality parameters. Models of water quality have become an effective tool for identifying worldwide water contamination, as well as the ultimate fate and behavior of contaminants in the water environment. Water quality model research is primarily conducted in Europe and other industrialized countries in the first world, where theoretical underpinnings and practical research are prioritized.

Keywords: artificial intelligence, QUAL2K, simulation, physiochemical parameters

Procedia PDF Downloads 106

Authors: Faizan Hassan, Seema Kumari, V. P. Singh, Pradeep Das, Diwakar Singh Dinesh

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Phlebotomus argentipes is an established vector for Visceral Leishmaniasis in Indian subcontinent. Laboratory colonization of Sand flies is imperative in research on vectors, which requires a proper diet for their larvae and adult growth that ultimately affects their survival and fecundity. In most of the laboratories, adult Sand flies are reared on rabbit blood feeding/artificial blood feeding and their larvae on fine grinded rabbit faeces as a sole source of food. Rabbit faeces are unhygienic, difficult to handle, high mites infestation as well as owing to bad odour which creates menacing to human users ranging from respiratory problems to eye infection and most importantly it does not full fill all the nutrients required for proper growth and development. It is generally observed that the adult emergence is very low in comparison to egg hatched, which may be due to insufficient food nutrients provided to growing larvae. To check the role of food nutrients on larvae survival and adult emergence, a high protein rich artificial diet for sand fly larvae were used in this study. The composition of artificial diet to be tested includes fine grinded (9 gm each) Rice, Pea nuts & Soyabean balls. These three food ingredients are rich source of all essential amino acids along with carbohydrate and minerals which is essential for proper metabolism and growth. In this study artificial food was found significantly more effective for larval development and adult emergence than rabbit faeces alone (P value >0.05). The weight of individual larvae was also found higher in test pots than the control. This study suggest that protein plays an important role in insect larvae development and adding carbohydrate will also enhances the fecundity of insects larvae.

Keywords: artificial food, nutrients, Phlebotomus argentipes, sand fly

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Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

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With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: artificial neural network (ANN), chromatic dispersion (CD), delay-tap sampling (DTS), optical signal-to-noise ratio (OSNR)

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Authors: Joshua Osondu

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This research explores how Artificial Intelligence (AI) can be utilized as a policy response to address teaching and learning (TL) issues in education in Ghana. The dual (AI and human) instructor model is used as a theoretical framework to examine how AI can be employed to improve teaching and learning processes and to equip learners with the necessary skills in the emerging AI society. A qualitative research design was employed to assess the impact of AI on various TL issues, such as teacher workloads, a lack of qualified educators, low academic performance, unequal access to education and educational resources, a lack of participation in learning, and poor access and participation based on gender, place of origin, and disability. The study concludes that AI can be an effective policy response to TL issues in Ghana, as it has the potential to increase students’ participation in learning, increase access to quality education, reduce teacher workloads, and provide more personalized instruction. The findings of this study are significant for filling in the gaps in AI research in Ghana and other developing countries and for motivating the government and educational institutions to implement AI in TL, as this would ensure quality, access, and participation in education and help Ghana industrialize.

Keywords: artificial intelligence, teacher, learner, students, policy response

Procedia PDF Downloads 92

Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Angelis P. Barlampas

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Target: The (SARS-CoV-2) is still a threat. AI software could be useful, categorizing the disease into different severities and indicate the extent of the lesions. Materials and methods: AI is a new revolutionary technique, which uses powered computerized systems, to do what a human being does more rapidly, more easily, as accurate and diagnostically safe as the original medical report and, in certain circumstances, even better, saving time and helping the health system to overcome problems, such as work overload and human fatigue. Results: It will be given an effort to describe to the inexperienced reader (see figures), as simple as possible, how an artificial intelligence system diagnoses computed tomography pictures. First, the computerized machine learns the physiologic motives of lung parenchyma by being feeded with normal structured images of the lung tissue. Having being used to recognizing normal structures, it can then easily indentify the pathologic ones, as their images do not fit to known normal picture motives. It is the same way as when someone spends his free time in reading magazines with quizzes, such as <> and <>. General conclusion: The AI mimics the physiological processes of the human mind, but it does that more efficiently and rapidly and provides results in a few seconds, whereas an experienced radiologist needs many days to do that, or even worse, he is unable to accomplish such a huge task.

Keywords: covid-19, artificial intelligence, automated imaging, CT, chest imaging

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 157

Authors: Jui-Ting Hsu, Heng-Li Huang, Ming-Tzu Tsai, Kuo-Chih Su, Lih-Jyh Fuh

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The objectives of this study were to measure the initial stability of the dental implant (ISQ and PTV) in the artificial foam bone block with three different quality levels. In addition, the 3D bone to implant contact percentage (BIC%) was measured based on the micro-computed tomography images. Furthermore, the relation between the initial stability of dental implant (ISQ and PTV) and BIC% were calculated. The experimental results indicated that enhanced the material property of the artificial foam bone increased the initial stability of the dental implant. The Pearson’s correlation coefficient between the BIC% and the two approaches (ISQ and PTV) were 0.652 and 0.745.

Keywords: dental implant, implant stability quotient, peak insertion torque, bone-implant contact, micro-computed tomography

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Authors: Sina Fatolahzadeh, Sergio A. Sepúlveda

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This paper presents advanced developments in close-range, terrestrial remote sensing techniques to enhance the characterization of rock masses. The study integrates two state-of-the-art laser-scanning technologies, the HandySCAN and GeoSLAM laser scanners, to extract high-resolution geospatial data for rock mass analysis. These instruments offer high accuracy, precision, low acquisition time, and high efficiency in capturing intricate geological features in small to medium size outcrops and slope cuts. Using the HandySCAN and GeoSLAM laser scanners facilitates real-time, three-dimensional mapping of rock surfaces, enabling comprehensive assessments of rock mass characteristics. The collected data provide valuable insights into structural complexities, surface roughness, and discontinuity patterns, which are essential for geological and geotechnical analyses. The synergy of these advanced remote sensing technologies contributes to a more precise and straightforward understanding of rock mass behavior. In this case, the main parameters of RQD, joint spacing, persistence, aperture, roughness, infill, weathering, water condition, and joint orientation in a slope cut along the Sea-to-Sky Highway, BC, were remotely analyzed to calculate and evaluate the Rock Mass Rating (RMR) and Geological Strength Index (GSI) classification systems. Automatic and manual analyses of the acquired data are then compared with field measurements. The results show the usefulness of the proposed remote sensing methods and their appropriate conformity with the actual field data.

Keywords: remote sensing, rock mechanics, rock engineering, slope stability, discontinuity properties

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Authors: Kasun Gayashan Samarawickrama

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Bone diseases, defects, and fractions are commonly seen in modern life. Since bone is regenerating dynamic living tissue, it will undergo healing process naturally, it cannot recover from major bone injuries, diseases and defects. In order to overcome them, bone grafting technique was introduced. Gold standard was the best method for bone grafting for the past decades. Due to limitations of gold standard, alternative methods have been implemented. Apart from them artificial bone substitutes and bone tissue engineering have become the emerging methods with technology for bone grafting. Many bone diseases and defects will be healed permanently with these promising techniques in future.

Keywords: bone grafting, gold standard, bone substitutes, bone tissue engineering

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Zouhour Neji Ben Salem

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Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance.

Keywords: artificial neural networks, self-organization map, hopfield network, microscopic evacuation, fire building evacuation

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

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

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

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

Procedia PDF Downloads 127

Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Ali Ahmadi, Ambrus Kecskes, Roni Michaely, Phuong-Anh Nguyen

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We quantify the proliferation of artificial intelligence innovation since 1990. Then, studying publicly traded firms, we find that they direct their production of innovation toward AI, motivated by their own and their customers, labor's exposure to AI technology. We instrument actual AI production by interacting with exogenously measured innovation capacity and AI exposure. We find that consistently during the past three decades, producing AI transitorily increases profitability, durably decreases risk (both systematic and idiosyncratic), and increases a firm's future stock returns. We can empirically distinguish the production of AI innovation from AI adoption, automation, and other potential confounds. The results suggest that AI innovation is a firm value increase that is underestimated by investors.

Keywords: artificial intelligence, innovation, technology, labor, firm value, corporate investment, asset pricing

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Authors: Juan Sebastian Huertas Piedrahita, Jaime Arturo Lopez Duque, Eduardo Luis Perez Londoño, Julián S. Rodríguez

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The process of generating computer vision is called artificial vision. The artificial vision is a branch of artificial intelligence that allows the obtaining, processing, and analysis of any type of information especially the ones obtained through digital images. Actually the artificial vision is used in manufacturing areas for quality control and production, as these processes can be realized through counting algorithms, positioning, and recognition of objects that can be measured by a single camera (or more). On the other hand, the companies use assembly lines formed by conveyor systems with actuators on them for moving pieces from one location to another in their production. These devices must be previously programmed for their good performance and must have a programmed logic routine. Nowadays the production is the main target of every industry, quality, and the fast elaboration of the different stages and processes in the chain of production of any product or service being offered. The principal base of this project is to program a computer that recognizes geometric figures (circle, square, and triangle) through a camera, each one with a different color and link it with a group of conveyor systems to organize the mentioned figures in cubicles, which differ from one another also by having different colors. This project bases on artificial vision, therefore the methodology needed to develop this project must be strict, this one is detailed below: 1. Methodology: 1.1 The software used in this project is QT Creator which is linked with Open CV libraries. Together, these tools perform to realize the respective program to identify colors and forms directly from the camera to the computer. 1.2 Imagery acquisition: To start using the libraries of Open CV is necessary to acquire images, which can be captured by a computer’s web camera or a different specialized camera. 1.3 The recognition of RGB colors is realized by code, crossing the matrices of the captured images and comparing pixels, identifying the primary colors which are red, green, and blue. 1.4 To detect forms it is necessary to realize the segmentation of the images, so the first step is converting the image from RGB to grayscale, to work with the dark tones of the image, then the image is binarized which means having the figure of the image in a white tone with a black background. Finally, we find the contours of the figure in the image to detect the quantity of edges to identify which figure it is. 1.5 After the color and figure have been identified, the program links with the conveyor systems, which through the actuators will classify the figures in their respective cubicles. Conclusions: The Open CV library is a useful tool for projects in which an interface between a computer and the environment is required since the camera obtains external characteristics and realizes any process. With the program for this project any type of assembly line can be optimized because images from the environment can be obtained and the process would be more accurate.

Keywords: artificial intelligence, artificial vision, binarized, grayscale, images, RGB

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Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

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Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

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Authors: Luis Rodrigo Valencia Perez, Francisco Flores Aguero, Gibran Aguilar Rangel

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Artificial Intelligence (AI) is rapidly transforming diverse sectors, and higher education in Latin America is no exception. This article explores the impact of AI on higher education institutions in the region, highlighting the imperative need for well-trained teachers in emerging technologies and a cultural shift towards the adoption and efficient use of these tools. AI offers significant opportunities to improve learning personalization, optimize administrative processes, and promote more inclusive and accessible education. However, the effectiveness of its implementation depends largely on the preparation and willingness of teachers to integrate these technologies into their pedagogical practices. Furthermore, it is essential that Latin American countries develop and implement public policies that encourage the adoption of AI in the education sector, thus ensuring that institutions can compete globally. Policies should focus on the continuous training of educators, investment in technological infrastructure, and the creation of regulatory frameworks that promote innovation and the ethical use of AI. Only through a comprehensive and collaborative approach will it be possible to fully harness the potential of AI to transform higher education in Latin America, thereby boosting the region's development and competitiveness on the global stage.

Keywords: artificial intelligence (AI), higher education, teacher training, public policies, latin america, global competitiveness

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Authors: Somoshree Datta, Chithra A. V., Sandeep Nithyanandan, Smitha K. K.

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Space exploration has always been at the forefront of technological innovation, pushing the boundaries of human knowledge and capabilities. In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field, offering unprecedented opportunities to enhance the efficiency, autonomy and intelligence of space missions. This survey paper aims to provide a comprehensive overview of the multifaceted applications of AI in space exploration, exploring the evolution of this synergy and its impact on mission success, scientific discovery, and the future of space endeavors. Indian Space Research Organization (ISRO) has achieved great feats in the recent moon mission (Chandrayaan-3) and sun mission (Aditya L1) by using artificial intelligence to enhance moon navigation as well as help young scientists to study the Sun even before the launch by creating AI-generated image visualizations. Throughout this survey, we will review key advancements, challenges and prospects in the intersection of AI and space exploration. As humanity continues its quest to explore the cosmos, the integration of AI promises to unlock new frontiers, reshape mission architectures, and redefine our understanding of the universe. This survey aims to serve as a comprehensive resource for researchers, engineers and enthusiasts interested in the dynamic and evolving landscape of AI applications in space exploration.

Keywords: artificial intelligence, space exploration, space missions, deep learning

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Authors: M. Bazrafshan, M. B. de Rooij, D. J. Schipper

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Frictional hysteresis is the phenomenon in which mechanical contacts are subject to small (compared to contact area) oscillating tangential displacements. In the presence of adhesion at the interface, the contact repulsive force increases leading to a higher static friction force and pre-sliding displacement. This paper proposes a boundary element model (BEM) for the adhesive frictional hysteresis contact at the interface of two contacting bodies of arbitrary geometries. In this model, adhesion is represented by means of a Dugdale approximation of the total work of adhesion at local areas with a very small gap between the two bodies. The frictional contact is divided into sticking and slipping regions in order to take into account the transition from stick to slip (pre-sliding regime). In the pre-sliding regime, the stick and slip regions are defined based on the local values of shear stress and normal pressure. In the studied cases, a fixed normal force is applied to the interface and the friction force varies in such a way to start gross sliding in one direction reciprocally. For the first case, the problem is solved at the smooth interface between a ball and a flat for different values of work of adhesion. It is shown that as the work of adhesion increases, both static friction and pre-sliding distance increase due to the increase in the contact repulsive force. For the second case, the rough interface between a glass ball against a silicon wafer and a DLC (Diamond-Like Carbon) coating is considered. The work of adhesion is assumed to be identical for both interfaces. As adhesion depends on the interface roughness, the corresponding contact repulsive force is different for these interfaces. For the smoother interface, a larger contact repulsive force and consequently, a larger static friction force and pre-sliding distance are observed.

Keywords: boundary element model, frictional hysteresis, adhesion, roughness, pre-sliding

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Authors: Gaurav D. Sonawane, Vikas G. Sargade

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The present investigation is a study of the effect of advanced Physical Vapor Deposition (PVD) coatings on cutting temperature residual stresses and surface roughness during Duplex Stainless Steel (DSS) 2205 turning. Austenite stabilizers like nickel, manganese, and molybdenum reduced the cost of DSS. Surface Integrity (SI) plays an important role in determining corrosion resistance and fatigue life. Resistance to various types of corrosion makes DSS suitable for applications with critical environments like Heat exchangers, Desalination plants, Seawater pipes and Marine components. However, lower thermal conductivity, poor chip control and non-uniform tool wear make DSS very difficult to machine. Cemented carbide tools (M grade) were used to turn DSS in a dry environment. AlTiN and AlTiCrN coatings were deposited using advanced PVD High Pulse Impulse Magnetron Sputtering (HiPIMS) technique. Experiments were conducted with cutting speed of 100 m/min, 140 m/min and 180 m/min. A constant feed and depth of cut of 0.18 mm/rev and 0.8 mm were used, respectively. AlTiCrN coated tools followed by AlTiN coated tools outperformed uncoated tools due to properties like lower thermal conductivity, higher adhesion strength and hardness. Residual stresses were found to be compressive for all the tools used for dry turning, increasing the fatigue life of the machined component. Higher cutting temperatures were observed for coated tools due to its lower thermal conductivity, which results in very less tool wear than uncoated tools. Surface roughness with uncoated tools was found to be three times higher than coated tools due to lower coefficient of friction of coating used.

Keywords: cutting temperature, DSS2205, dry turning, HiPIMS, surface integrity

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Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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