Search results for: artificial intelligence in semiconductor manufacturing

Authors: Orit Wolf

Abstract:

Classical music confronts a crucial challenge: updating cherished concert traditions for the digital age. This paper is a journey, and a quest to make classical concerts resonate with a new generation. It's not just about asking questions; it's about exploring the future of classical concerts and their potential to captivate and connect with today's audience in an era defined by change. The younger generation, known for their love of diversity, interactive experiences, and multi-sensory immersion, cannot be overlooked. This paper explores innovative strategies that forge deep connections with audiences whose relationship with classical music differs from the past. The urgency of this challenge drives the transformation of classical concerts. Examining classical concerts is necessary to understand how they can harmonize with contemporary sensibilities. New dimensions in audiovisual experiences that enchant the emerging generation are sought. Classical music must embrace the technological era while staying open to fusion and cross-cultural collaboration possibilities. The role of technology and Artificial Intelligence (AI) in reshaping classical concerts is under research. The fusion of classical music with digital experiences and dynamic interdisciplinary collaborations breathes new life into the concert experience. It aligns classical music with the expectations of modern audiences, making it more relevant and engaging. Exploration extends to the structure of classical concerts. Conventions are challenged, and ways to make classical concerts more accessible and captivating are sought. Inspired by innovative artistic collaborations, musical genres and styles are redefined, transforming the relationship between performers and the audience. This paper, therefore, aims to be a catalyst for dialogue and a beacon of innovation. A set of critical inquiries integral to reshaping classical concerts for the digital age is presented. As the world embraces digital transformation, classical music seeks resonance with contemporary audiences, redefining the concert experience while remaining true to its roots and embracing revolutions in the digital age.

Keywords: new concert formats, reception of classical music, interdiscplinary concerts, innovation in the new musical era, mash-up, cross culture, innovative concerts, engaging musical performances

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

Abstract:

Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Dike H. Ogbuagu, Etsede J. Oritsematosan

Abstract:

This work investigated possible inductions of CaC₂, often misused by fruit vendors to stimulate artificial ripening, on mammalian sperm morphology and viability. Thirty isogenic strains of male albino mice, Mus musculus (age≈ 8weeks; weight= 32.5±2.0g) were acclimatized (ambient temperature 28.0±1.0°C) for 2 weeks and fed standard growers mash and water ad libutum. They were later exposed to graded toxicant concentrations (w/w) of 2.5000, 1.2500, 0.6250, and 0.3125% in 4 cages. A control cage was also established. After 5 weeks, 3 animals from each cage were sacrificed by cervical dislocation and the cauda epididymis excised. Sperm morphology and viability were determined by microscopic procedures. The ANOVA, means plots, Student’s t-test and variation plots were used to analyze data. The common abnormalities observed included Double Head, Pin Head, Knobbed Head, No Tail and With Hook. The higher toxicant concentrations induced significantly lower body weights [F(829.899) ˃ Fcrit(4.19)] and more abnormalities [F(26.52) ˃ Fcrit(4.00)] at P˂0.05. Sperm cells in the control setup were significantly more viable than those in the 0.625% (t=0.005) and 2.500% toxicant doses (t=0.018) at the 95% confidence limit. CaC₂ appeared to induced morphological abnormalities and reduced viability in sperm cells of M. musculus.

Keywords: artificial ripening, calcium carbide, fruit vendors, sperm morphology, sperm viability

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Cheh-Shyh Ting, Jiin-Liang Lin

Abstract:

This study was conducted at the Wanglung Farm in Pingtung County to test the groundwater seepage influences on the implemented project for artificial groundwater recharge. The study was divided into three phases. The first phase, conducted on natural groundwater that was recharged through the local climate and growing conditions, observed the natural form of vegetation species. The original plants were flooded, and after 60 days it was observed that of the original plants only Goosegrass (Eleusine indica) and Black heart (Polygonum lapathifolium Linn.) remained. Direct infiltration tests were carried out, and calculations for the effect of vegetation on infiltration velocity of the recharge pool were noted. The second phase was an indoor test. Bahia grass and wild amaranth were selected as vegetation roots. After growth, the distribution of different grassroots was observed in order to facilitate a comparison permeability coefficient calculated by the amount of penetration and to explore the relationship between density and the efficiency to groundwater recharge. The third phase was the root tomography analysis, further observation of the development of plant roots using computed tomography technology. Computed Tomography, also known as (CT), is a diagnostic imaging examination, normally used in the medical field. In the first phase of the feasibility study, most non-aquatic plants wilted and died within seven days. In seven days, the remaining plants were used for experimental infiltration analysis. Results showed that in eight hours of infiltration test, Eleusine indica stems averaged 0.466 m/day and wild amaranth averaged 0.014 m/day. The second phase of the experiment was conducted on the remains of the plant a week in it had died and rotted, and the infiltration experiment was performed under these conditions. The results showed eight hours in end of the infiltration test, Eleusine indica stems averaged 0.033 m/day, and wild amaranth averaged 0.098 m/day. Non-aquatic plants died within two weeks, and their rotted remains clogged the pores of bottom soil particles, causing obstruction of recharge pool infiltration. Experiment results showed that eight hours in the test the average infiltration velocity for Eleusine indica stems was 0.0229 m/day and wild amaranth averaged 0.0117 m/day. Since the rotted roots of the plants blocked the pores of the soil in the recharge pool, which resulted in the obstruction of the artificial infiltration pond and showed an immediate impact on recharge efficiency. In order to observe the development of plant roots, the third phase used computed tomography imaging. Iodine developer was injected into the Black heart, allowing its cross-sectional images to be shown on CT and to be used to observe root development.

Keywords: artificial recharge of groundwater, computed tomography, infiltration velocity, vegetation root system

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Authors: Adelina Vrancianu

Abstract:

The problem of artificial real accession will be analyzed in this study both in terms of old and current Civil Code provisions and in terms of comparative law, European legal and Canadian systems. The current Civil Code from 2009 has brought new changes about the application and solutions regarding artificial real accession. The hypothesis in which a person is making works with his own materials on the real estate belonging to another person is developed and analyzed in detail from national and international point of view in relation with the good faith. The scope of this analysis is to point out what are the changes issued from case-law and which ones are new, inspired from other law systems in regard to the good/bad faith. The new civil code has promoted a definition for this notion. Is this definition a new one inspired from the comparative law or is it inspired from the case-law? Is it explained for every case scenario of accession or is a general notion? The study tries to respond to these questions and to present the new aspects in the area. has reserved a special place for the situation of execution of works with own materials exceeding the border with violation of another’s right of property, where the variety of solutions brings into discussion the case of expropriation for private interest. The new Civil Code is greatly influenced by the Civil Code from Quebec in comparison with the old code of French influence. The civil reform was needed and has brought into attention new solutions inspired from the Canadian system which has mitigated the permanent conflict between the constructor and the immovable owner.

Keywords: accession, good faith, new civil code, comparative law

Procedia PDF Downloads 462

Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

Abstract:

Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

Procedia PDF Downloads 446

Authors: L. Giraudet, O. Simonetti, G. de Tournadre, N. Dumelié, B. Clarenc, F. Reisdorffer

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In this paper we intend to give a comprehensive view of the saturation misbehavior of thin film transistors (TFTs) based on disordered semiconductors, such as most organic TFTs, and its link to the field activation of the mobility. Experimental evidence of the field activation of the mobility is given for disordered semiconductor based TFTs, when reducing the gate length. Saturation misbehavior is observed simultaneously. Advanced transport models have been implemented in a quasi-2D numerical TFT simulation software. From the numerical simulations it is clearly established that field activation of the mobility alone cannot explain the saturation misbehavior. Evidence is given that high longitudinal field gradient at the drain end of the channel is responsible for an excess charge accumulation, preventing saturation. The two combined effects allow reproducing the experimental output characteristics of short channel TFTs, with S-shaped characteristics and saturation failure.

Keywords: mobility field activation, numerical simulation, OTFT, saturation failure

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Authors: Abdulmajeed Dabwan, Saqib Anwar, Ali Al-Samhan

Abstract:

Electron Beam Melting (EBM) is a metal powder bed-based Additive Manufacturing (AM) technology, which uses computer-controlled electron beams to create fully dense three-dimensional near-net-shaped parts from metal powder. It gives the ability to produce any complex parts directly from a computer-aided design (CAD) model without tools and dies, and with a variety of materials. However, the quality of the surface finish in EBM process has limitations to meeting the performance requirements of additively manufactured components. The aim of this study is to investigate the cutting forces induced during milling Ti6Al4V produced by EBM as well as the surface quality of the milled surfaces. The effects of cutting speed and radial depth of cut on the cutting forces, surface roughness, and surface morphology were investigated. The results indicated that the cutting speed was found to be proportional to the resultant cutting force at any cutting conditions while the surface roughness improved significantly with the increase in cutting speed and radial depth of cut.

Keywords: electron beam melting, additive manufacturing, Ti6Al4V, surface morphology

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Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

Abstract:

Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 187

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 80

Authors: Ishita Singh

Abstract:

Social-dimension of Climate Change is no longer peripheral to Science, Technology and Innovation (STI). Indeed, STI is being mobilized to address small farmers’ vulnerability and adaptation to Climate Change. The experiences from the cold desert of Leh (Ladakh) in North-West Himalaya illustrate the potential of STI to address the challenges of Climate Change and the needs of small farmers through the use of Artificial Glacier Techniques. Small farmers have a unique technique of water harvesting to augment irrigation, called “Artificial Glaciers” - an intricate network of water channels and dams along the upper slope of a valley that are located closer to villages and at lower altitudes than natural glaciers. It starts to melt much earlier and supplements additional irrigation to small farmers’ improving their livelihoods. Therefore, the issue of vulnerability, adaptive capacity and adaptation strategy needs to be analyzed in a local context and the communities as well as regions where people live. Leh (Ladakh) in North-West Himalaya provides a Case Study for exploring the ways in which adaptation to Climate Change is taking place at a community scale using Artificial Glacier Technology. With the above backdrop, an attempt has been made to analyze the rural poor households' vulnerability and adaptation practices to Climate Change using this technology, thereby drawing lessons on vulnerability-livelihood interactions in the cold desert of Leh (Ladakh) in North-West Himalaya, India. The study is based on primary data and information collected from 675 households confined to 27 villages of Leh (Ladakh) in North-West Himalaya, India. It reveals that 61.18% of the population is driving livelihoods from agriculture and allied activities. With increased irrigation potential due to the use of Artificial Glaciers, food security has been assured to 77.56% of households and health vulnerability has been reduced in 31% of households. Seasonal migration as a livelihood diversification mechanism has declined in nearly two-thirds of households, thereby improving livelihood strategies. Use of tactical adaptations by small farmers in response to persistent droughts, such as selling livestock, expanding agriculture lands, and use of relief cash and foods, have declined to 20.44%, 24.74% and 63% of households. However, these measures are unsustainable on a long-term basis. The role of policymakers and societal stakeholders becomes important in this context. To address livelihood challenges, the role of technology is critical in a multidisciplinary approach involving multilateral collaboration among different stakeholders. The presence of social entrepreneurs and new actors on the adaptation scene is necessary to bring forth adaptation measures. Better linkage between Science and Technology policies, together with other policies, should be encouraged. Better health care, access to safe drinking water, better sanitary conditions, and improved standards of education and infrastructure are effective measures to enhance a community’s adaptive capacity. However, social transfers for supporting climate adaptive capacity require significant amounts of additional investment. Developing institutional mechanisms for specific adaptation interventions can be one of the most effective ways of implementing a plan to enhance adaptation and build resilience.

Keywords: climate change, adaptation, livelihood, stakeholders

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

Abstract:

Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Prashant Dhiman, Viranshu Kumar, Pradeep Joshi

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Lot of research is going on to study the effect of cryogenic treatment on materials. Cryogenic treatment is a heat treatment process which is used widely to enhance the mechanical and metallurgical properties of various materials whether the material is ferrous or non ferrous. In almost all ferrous metals, it is found that retained austenite is converted into martensite. Generally deep cryogenic treatment is done using liquid nitrogen having temperature of -195 ℃. The austenite is unstable at this stage and converts into martensite. In non ferrous materials there presents a microcavity and under the action of stress it becomes crack. When this crack propagates, fracture takes place. As the metal contract under low temperature, by doing cryogenic treatment these microcavities will be filled hence increases the soundness of the material. Properties which are enhanced by cryogenic treatment of both ferrous and non ferrous materials are hardness, tensile strength, wear rate, electrical and thermal conductivity, and others. Also there is decrease in residual stress. A large number of manufacturing process (EDM, CNC etc.) are using cryogenic treatment on different tools or workpiece to reduce their wear. In this Review paper the use of cryogenic heat treatment in different manufacturing has been shown along with their advantages.

Keywords: cyrogenic treatment, EDM (Electrical Discharge Machining), CNC (Computer Numeric Control), Mechanical and Metallurgical Properties

Procedia PDF Downloads 436

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 70

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, José J. Hernández-Flores, Edith Olaco Garcia

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The Project Scheduling Problem (PSP) is a generic name given to a whole class of problems in which the best form, time, resources and costs for project scheduling are necessary. The PSP is an application area related to the project management. This paper aims at being a guide to understand PSP by presenting a survey of the general parameters of PSP: the Resources (those elements that realize the activities of a project), and the Activities (set of operations or own tasks of a person or organization); the mathematical models of the main variants of PSP and the algorithms used to solve the variants of the PSP. The project scheduling is an important task in project management. This paper contains mathematical models, resources, activities, and algorithms of project scheduling problems. The project scheduling problem has attracted researchers of the automotive industry, steel manufacturer, medical research, pharmaceutical research, telecommunication, industry, aviation industry, development of the software, manufacturing management, innovation and technology management, construction industry, government project management, financial services, machine scheduling, transportation management, and others. The project managers need to finish a project with the minimum cost and the maximum quality.

Keywords: PSP, Combinatorial Optimization Problems, Project Management; Manufacturing Management, Technology Management.

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Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: Michael N. O'Sullivan, Con Sheahan

Abstract:

Mass customization is the idea of offering custom products or services to satisfy the needs of each individual customer while maintaining the efficiency of mass production. Technologies like 3D printing and artificial intelligence have many start-ups hoping to capitalize on this dream of creating personalized products at an affordable price, and well established companies scrambling to innovate and maintain their market share. However, the majority of them are failing as they struggle to understand one key question – where does customization make sense? Customization and personalization only make sense where the value of the perceived benefit outweighs the cost to implement it. In other words, will people pay for it? Looking at the Kano Model makes it clear that it depends on the product. In products where customization is an inherent need, like prosthetics, mass customization technologies can be highly beneficial. However, for products that already sell as a standard, like headphones, offering customization is likely only an added bonus, and so the product development team must figure out if the customers’ perception of the added value of this feature will outweigh its premium price tag. This can be done through the use of a ‘serious game,’ whereby potential customers are given a limited budget to collaboratively buy and bid on potential features of the product before it is developed. If the group choose to buy customization over other features, then the product development team should implement it into their design. If not, the team should prioritize the features on which the customers have spent their budget. The level of customization purchased can also be translated to an appropriate production method, for example, the most expensive type of customization would likely be free-form design and could be achieved through digital fabrication, while a lower level could be achieved through short batch production. Twenty-five teams of final year students from design, engineering, construction and technology tested this methodology when bringing a product from concept through to production specification, and found that it allowed them to confidently decide what level of customization, if any, would be worth offering for their product, and what would be the best method of producing it. They also found that the discussion and negotiations between players during the game led to invaluable insights, and often decided to play a second game where they offered customers the option to buy the various customization ideas that had been discussed during the first game.

Keywords: Kano model, mass customization, new product development, serious game

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Authors: Pram Abhayawardhana, Ali Reza Nazmi, Hossein Najaf Zadeh

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This study examines the use of additive manufacturing (AM) to develop sustainable and intelligent agricultural formulations that can gradually release fertilisers. AM offers the ability to design customised formulations with precise geometries and controlled release properties while taking into account their mechanical, chemical, and environmental properties. The study specifically investigates the use of an aerogel matrix mixed with a potential fertiliser in agriculture. Highly porous 3D printed aerogel structures were designed to enable the slow release of fertilisers. The performance of the formulated mixture is evaluated against other commonly used materials for slow-release applications. The findings suggest that the 3D printed gel made has great potential for slow-release fertilisers, providing an environmentally friendly solution for agricultural practices. The combination of AM technology and sustainable materials can play a vital role in mitigating the negative environmental impact of traditional fertilisers, as well as improving the efficiency and sustainability of agricultural production.

Keywords: 3D printing, hydrogel, aerogel, fertiliser, agriculture

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Authors: Mina Shokry Hanna Saleh Tadros

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The centrality of the Petroleum Industry in the world energy is undoubted. The world economy almost runs and depends on petroleum. Petroleum industry is a multi-trillion industry; it turns otherwise poor and underdeveloped countries into wealthy nations and thrusts them at the center of international diplomacy. Although these developing nations lack the necessary technology to explore and exploit petroleum resources they are not without help as developed nations, represented by their multinational corporations are ready and willing to provide both the technical and managerial expertise necessary for the development of this natural resource. However, the exploration of these petroleum resources comes with, sometimes, grave, concomitant consequences. These consequences are especially pronounced with respect to the environment. From the British Petroleum Oil rig explosion and the resultant oil spillage and pollution in New Mexico, United States to the Mobil Oil spillage along Egyptian coast, the story and consequence is virtually the same. Egypt’s delta Region produces Nigeria’s petroleum which accounts for more than ninety-five percent of Nigeria’s foreign exchange earnings. Between 1999 and 2007, Egypt earned more than $400 billion from petroleum exports. Nevertheless, petroleum exploration and exploitation has devastated the Delta environment. From oil spillage which pollutes the rivers, farms and wetlands to gas flaring by the multi-national corporations; the consequences is similar-a region that has been devastated by petroleum exploitation. This paper thus seeks to examine the consequences and impact of petroleum pollution in the Egypt Delta with particular reference on the right of the people of Niger Delta to a healthy environment. The paper further seeks to examine the relevant international, regional instrument and Nigeria’s municipal laws that are meant to protect the result of the people of the Egypt Delta and their enforcement by the Nigerian State. It is quite worrisome that the Egypt Delta Region and its people have suffered and are still suffering grave violations of their right to a healthy environment as a result of petroleum exploitation in their region. The Egypt effort at best is half-hearted in its protection of the people’s right.

Keywords: crude oil, fire, floating roof tank, lightning protection systemenvironment, exploration, petroleum, pollutionDuvernay petroleum system, oil generation, oil-source correlation, Re-Os

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Authors: Dominik Halstrup, Marlene Schriever

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The industry, as one of the main energy consumer, is of critical importance along the way of transforming the energy system to Renewable Energies. The distributed character of the Energy Transition demands for further flexibility being introduced to the grid. In order to shed further light on the acceptance of Electric Energy Storage (ESS) from an industrial point of view, this study therefore examines the German manufacturing industry. The analysis in this paper uses data composed of a survey amongst 101 manufacturing companies in Germany. Being part of a two-stage research design, both qualitative and quantitative data was collected. Based on a literature review an acceptance concept was developed in the paper and four user-types identified: (Dedicated) User, Impeded User, Forced User and (Dedicated) Non-User and incorporated in the questionnaire. Both descriptive and bivariate analysis is deployed to identify the level of acceptance in the different organizations. After a factor analysis has been conducted, variables were grouped to form independent acceptance factors. Out of the 22 organizations that do show a positive attitude towards ESS, 5 have already implemented ESS and show a positive attitude towards ESS. They can be therefore considered ‘Dedicated Users’. The remaining 17 organizations have a positive attitude but have not implemented ESS yet. The results suggest that profitability plays an important role as well as load-management systems that are already in place. Surprisingly, 2 organizations have implemented ESS even though they have a negative attitude towards it. This is an example for a ‘Forced User’ where reasons of overriding importance or supporters with overriding authority might have forced the company to implement ESS. By far the biggest subset of the sample shows (critical) distance and can therefore be considered ‘(Dedicated) Non-Users’. The results indicate that the majority of the respondents have not thought ESS in their own organization through yet. For the majority of the sample one can therefore not speak of critical distance but rather a distance due to insufficient information and the perceived unprofitability. This paper identifies the relative state of acceptance of ESS in the manufacturing industry as well as current reasons for hindrance and perspectives for future growth of ESS in an industrial setting from a policy level. The interest that is currently generated by the media could be channeled and taken into a more substantial and individual discussion about ESS in an industrial setting. If the current perception of profitability could be addressed and communicated accordingly, ESS and their use in for instance cooperative business models could become a topic for more organizations in Germany and other parts of the world. As price mechanisms tend to favor existing technologies, policy makers need to further access the use of ESS and acknowledge the positive effects when integrated in an energy system. The subfields of generation, transmission and distribution become increasingly intertwined. New technologies and business models, such as ESS or cooperative arrangements entering the market, increase the number of stakeholders. Organizations need to find their place within this array of stakeholders.

Keywords: acceptance, energy storage solutions, German energy transition, manufacturing industry

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Authors: Alexandre D. Ferraz, Dario H. Alliprandini, Mauro Sampaio

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Companies and other institutions are constantly seeking better organizational performance and greater competitiveness. In order to fulfill this purpose, there are many tools, methodologies and models for increasing performance. However, the Lean Management approach seems to be the most effective in terms of achieving a significant improvement in productivity relatively quickly. Although Lean tools are relatively easy to understand and implement in different contexts, many organizations are not able to transform themselves into 'Lean companies'. Most of the efforts in its implementation have shown single benefits, failing to achieve the desired impact on the performance of the overall enterprise system. There is also a growing perception of the importance of management in Lean transformation, but few studies have empirically investigated and described the 'Lean Management'. In order to understand more clearly the ideas that guide Lean Management and its influence on the maturity level of the production system, the objective of this research is analyze the relationship between the Lean Management principles and the Lean maturity level in the organizations. The research also analyzes the principles of Lean Management and its relationship with the 'Lean culture' and the results obtained. The research was developed using the case study methodology. Three manufacturing units of a German multinational company from industrial automation segment, located in different countries were studied, in order to have a better comparison between the practices and the level of maturity in the implementation. The primary source of information was the application of a research questionnaire based on the theoretical review. The research showed that higher the level of Lean Management principles, higher are the Lean maturity level, the Lean culture level, and the level of Lean results obtained in the organization. The research also showed that factors such as time for application of Lean concepts and company size were not determinant for the level of Lean Management principles and, consequently, for the level of Lean maturity in the organization. The characteristics of the production system showed much more influence in different evaluated aspects. The present research also left recommendations for the managers of the plants analyzed and suggestions for future research.

Keywords: lean management, lean principles, lean maturity level, lean manufacturing

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Authors: Temesgen Geremew

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ZnO is currently receiving a lot of attention in the semiconductor industry due to its unique characteristics. ZnO is widely used in solar cells, heat-reflecting glasses, optoelectronic bias, and detectors. In this composition, we provide an overview of the ZnO thin flicks' packages, methods of characterization, and implicit operations. They consist of Transmission spectroscopy, Raman spectroscopy, Field emigration surveying electron microscopy, and X-ray diffraction. This review content also demonstrates how ZnO thin flicks function in electrical components for piezoelectric bias, optoelectronics, detectors, and renewable energy sources. Zinc oxide (ZnO) thin films offer a captivating tapestry of possibilities due to their unique blend of electrical, optical, and mechanical properties. This review delves into the realm of their potential applications and future prospects, highlighting the pivotal contributions of research endeavors aimed at tailoring their functionalities.

Keywords: Zinc oxide, raman spectroscopy, thin films, piezoelectric devices

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Authors: A. Majeed, P. Ali

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Before designing an electrical system, the estimation of load is necessary for unit sizing and demand-generation balancing. The system could be a stand-alone system for a village or grid connected or integrated renewable energy to grid connection, especially as there are non–electrified villages in developing countries. In the classical model, the energy demand was found by estimating the household appliances multiplied with the amount of their rating and the duration of their operation, but in this paper, information exists for electrified villages could be used to predict the demand, as villages almost have the same life style. This paper describes a method used to predict the average energy consumed in each two months for every consumer living in a village by Artificial Neural Network (ANN). The input data are collected using a regional survey for samples of consumers representing typical types of different living, household appliances and energy consumption by a list of information, and the output data are collected from administration office of Piramagrun for each corresponding consumer. The result of this study shows that the average demand for different consumers from four villages in different months throughout the year is approximately 12 kWh/day, this model estimates the average demand/day for every consumer with a mean absolute percent error of 11.8%, and MathWorks software package MATLAB version 7.6.0 that contains and facilitate Neural Network Toolbox was used.

Keywords: artificial neural network, load estimation, regional survey, rural electrification

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Authors: Yannick Willemin

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Carbon fibre-reinforced plastic (CFRP) offers outstanding value. However, like all materials, CFRP also has its challenges. Many forming processes are largely manual and hard to automate, making it challenging to control repeatability and reproducibility (R&R); they generate significant scrap and are too slow for high-series production; fibre costs are relatively high and subject to supply and cost fluctuations; the supply chain is fragmented; many forms of CFRP are not recyclable, and many materials have yet to be fully characterized for accurate simulation; shelf life and outlife limitations add cost; continuous-fibre forms have design limitations; many materials are brittle; and small and/or thick parts are costly to produce and difficult to automate. A majority of small structural parts are metal due to high CFRP fabrication costs for the small-size class. The fact that CFRP manufacturing processes that produce the highest performance parts also tend to be the slowest and least automated is another reason CFRP parts are generally higher in cost than comparably performing metal parts, which are easier to produce. Fortunately, business is in the midst of a major manufacturing evolution—Industry 4.0— one technology seeing rapid growth is additive manufacturing/3D printing, thanks to new processes and materials, plus an ability to harness Industry 4.0 tools. No longer limited to just prototype parts, metal-additive technologies are used to produce tooling and mold components for high-volume manufacturing, and polymer-additive technologies can incorporate fibres to produce true composites and be used to produce end-use parts with high aesthetics, unmatched complexity, mass customization opportunities, and high mechanical performance. A new hybrid manufacturing process combines the best capabilities of additive—high complexity, low energy usage and waste, 100% traceability, faster to market—and post-consolidation—tight tolerances, high R&R, established materials, and supply chains—technologies. The platform was developed by Zürich-based 9T Labs AG and is called Additive Fusion Technology (AFT). It consists of a design software offering the possibility to determine optimal fibre layup, then exports files back to check predicted performance—plus two pieces of equipment: a 3d-printer—which lays up (near)-net-shape preforms using neat thermoplastic filaments and slit, roll-formed unidirectional carbon fibre-reinforced thermoplastic tapes—and a post-consolidation module—which consolidates then shapes preforms into final parts using a compact compression press fitted with a heating unit and matched metal molds. Matrices—currently including PEKK, PEEK, PA12, and PPS, although nearly any high-quality commercial thermoplastic tapes and filaments can be used—are matched between filaments and tapes to assure excellent bonding. Since thermoplastics are used exclusively, larger assemblies can be produced by bonding or welding together smaller components, and end-of-life parts can be recycled. By combining compression molding with 3D printing, higher part quality with very-low voids and excellent surface finish on A and B sides can be produced. Tight tolerances (min. section thickness=1.5mm, min. section height=0.6mm, min. fibre radius=1.5mm) with high R&R can be cost-competitively held in production volumes of 100 to 10,000 parts/year on a single set of machines.

Keywords: additive manufacturing, composites, thermoplastic, hybrid manufacturing

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Authors: Keroles Akram Saed Ghatas

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The near future will bring significant changes in modern organizations and management due to the growing role of intangible assets and knowledge workers. The area of copyright, intellectual property, digital (intangible) assets and media redistribution appears to be one of the greatest challenges facing business and society in general and management sciences and organizations in particular. The proposed article examines the views and perceptions of fairness in digital media sharing among Harvard Law School's LL.M.s. Students, based on 50 qualitative interviews and 100 surveys. The researcher took an ethnographic approach to her research and entered the Harvard LL.M. in 2016. at, a Face book group that allows people to connect naturally and attend in-person and private events more easily. After listening to numerous students, the researcher conducted a quantitative survey among 100 respondents to assess respondents' perceptions of fairness in digital file sharing in various contexts (based on media price, its availability, regional licenses, copyright holder status, etc.). to understand better . .). Based on the survey results, the researcher conducted long-term, open-ended and loosely structured ethnographic interviews (50 interviews) to further deepen the understanding of the results. The most important finding of the study is that Harvard lawyers generally support digital piracy in certain contexts, despite having the best possible legal and professional knowledge. Interestingly, they are also more accepting of working for the government than the private sector. The results of this study provide a better understanding of how “fairness” is perceived by the younger generation of lawyers and pave the way for a more rational application of licensing laws.

Keywords: cognitive impairments, communication disorders, death penalty, executive function communication disorders, cognitive disorders, capital murder, executive function death penalty, egyptian law absence, justice, political cases piracy, digital sharing, perception of fairness, legal profession

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Authors: Prateek Guleria, Shubham Sharma, Rakesh Kumar Shukla, Harshit Sharma

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Performance measurement of small and medium-sized businesses is the primary need for all companies to survive and thrive in a dynamic global company. A structured and systematic, integrated organization increases employee reliability, sustainability, and loyalty. This paper is a case study of a gear manufacturing industry that was facing the problem of rejection due to dents and damages in gear. The DMAIC cycle, along with different tools used in the research work includes SIPOC (Supply, Input, Process, Output, Control) Pareto analysis, Root & Cause analysis, and FMEA (Failure Mode and Effect Analysis). The six-sigma level was improved from 4.06 to 3.46, and the rejection rate was reduced from 7.44% to 1.56%. These findings highlighted the influence of a Lean Six Sigma module in the gear manufacturing unit, which has already increased operational quality and continuity to increase market success and meet customer expectations. According to the findings, applying lean six sigma tools will result in increased productivity. The results could assist businesses in deciding the quality tools that were likely to improve efficiency, competitiveness, and expense.

Keywords: six sigma, DMAIC, SIPOC, failure mode, effect analysis

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Authors: Jessica BrodeFrank, Beka Bryer, Lacey Wilson, Sierra Van Ryck deGroot

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Museums are enjoying quite the moment in pop culture. From discussions of labor in Bob’s Burger to introducing cultural repatriation in The Black Panther, discussions of various museum issues are making their way to popular media. “Talking Back to Hollywood” analyzes the impact museums have on movies and television. The paper will highlight a series of cultural cameos and discuss what each reveals about critical themes in museums: repatriation, labor, obfuscated histories, institutional legacies, artificial intelligence, and holograms. Using a mixed methods approach to include surveys, descriptive research, thematic analysis, and context analysis, the authors of this paper will explore how we, as the museum staff, might begin to cite museums and movies together as texts. Drawing from their experience working in museums and public history, this contingent of mid-career professionals will highlight the impact museums have had on movies and television and the didactic lessons these portrayals can provide back to cultural heritage professionals. From tackling critical themes in museums such as repatriation, labor conditions/inequities, obfuscated histories, curatorial choice and control, institutional legacies, and more, this paper is grounded in the cultural zeitgeist of the 2000s and the message these media portrayals send to the public and the cultural heritage sector. In particular, the paper will examine how portrayals of AI, holograms, and more technology can be used as entry points for necessary discussions with the public on mistrust, misinformation, and emerging technologies. This paper will not only expose the legacy and cultural understanding of the museum field within popular culture but also will discuss actionable ways that public historians can use these portrayals as an entry point for discussions with the public, citing literature reviews and quantitative and qualitative analysis of survey results. As Hollywood is talking about museums, museums can use that to better connect to the audiences who feel comfortable at the cinema but are excluded from the museum.

Keywords: museums, public memory, representation, popular culture

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Authors: Till Gramberg, Max Kellner, Erwin Gross

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This paper presents a comprehensive investigation into the topic of smart services and IIoT-Platforms, focusing on their selection and prioritization in manufacturing organizations. First, a literature review is conducted to provide a basic understanding of the current state of research in the area of smart services. Based on discussed and established definitions, a definition approach for this paper is developed. In addition, value propositions for smart services are identified based on the literature and expert interviews. Furthermore, the general requirements for the provision of smart services are presented. Subsequently, existing approaches for the selection and development of smart services are identified and described. In order to determine the requirements for the selection of smart services, expert opinions from successful companies that have already implemented smart services are collected through semi-structured interviews. Based on the results, criteria for the evaluation of existing methods are derived. The existing methods are then evaluated according to the identified criteria. Furthermore, a novel method for the selection of smart services in manufacturing companies is developed, taking into account the identified criteria and the existing approaches. The developed concept for the method is verified in expert interviews. The method includes a collection of relevant smart services identified in the literature. The actual relevance of the use cases in the industrial environment was validated in an online survey. The required data and sensors are assigned to the smart service use cases. The value proposition of the use cases is evaluated in an expert workshop using different indicators. Based on this, a comparison is made between the identified value proposition and the required data, leading to a prioritization process. The prioritization process follows an established procedure for evaluating technical decision-making processes. In addition to the technical requirements, the prioritization process includes other evaluation criteria such as the economic benefit, the conformity of the new service offering with the company strategy, or the customer retention enabled by the smart service. Finally, the method is applied and validated in an industrial environment. The results of these experiments are critically reflected upon and an outlook on future developments in the area of smart services is given. This research contributes to a deeper understanding of the selection and prioritization process as well as the technical considerations associated with smart service implementation in manufacturing organizations. The proposed method serves as a valuable guide for decision makers, helping them to effectively select the most appropriate smart services for their specific organizational needs.

Keywords: smart services, IIoT, industrie 4.0, IIoT-platform, big data

Procedia PDF Downloads 89