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Journal articles on the topic 'Artificial production'

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Published: 22 June 2024

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1

Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (October 31, 2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12 hidden layers, 12 process elements (12-12-1) and Levenberg-Marquardt back propagation algorithm. The amount of avocado production was estimated between 2021 and 2030 with the ANN. As a result of the prediction, it is expected that the amount of avocado production for the period 2021-2030 will be between 2,410,741-2,502,302 tons.

2

Fagundes Netto, Eduardo Barros, and Ilana Rosental Zalmon. "Artificial reefs: "Attraction versus Production"." Revista Vértices 13, no. 1 (2011): 179–90. http://dx.doi.org/10.5935/1809-2667.20110010.

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Kagawa, S., F. Ito, and K. Kagawa. "Production of artificial snow crystals." Physics Education 34, no. 2 (January 1, 1999): 92–95. http://dx.doi.org/10.1088/0031-9120/34/2/020.

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4

Bannat, Alexander, Thibault Bautze, Michael Beetz, Juergen Blume, Klaus Diepold, Christoph Ertelt, Florian Geiger, et al. "Artificial Cognition in Production Systems." IEEE Transactions on Automation Science and Engineering 8, no. 1 (January 2011): 148–74. http://dx.doi.org/10.1109/tase.2010.2053534.

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Gupta, Shanker. "Artificial Intelligence and Pharmaceutical Production." PDA Journal of Pharmaceutical Science and Technology 77, no. 3 (2023): 145. http://dx.doi.org/10.5731/pdajpst.2023.001423.

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Farzana Bashir, Farzana Bashir, Yumna Sadef Yumna Sadef, Iqra Nadeem Iqra Nadeem, Romana Shahzadi Romana Shahzadi, and Rubina Nelofer and Muhammad Tariq Rubina Nelofer and Muhammad Tariq. "Application of Artificial Neural Network Technique for the Production of Biotoxin." Journal of the chemical society of pakistan 44, no. 6 (2022): 568. http://dx.doi.org/10.52568/001181/jcsp/44.06.2022.

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In this study. a locally isolated strain of Bacillus thuringiensis that has insecticidal activity against dengue vector (larvae of Aedes aegypti), was cultivated. Different carbon and nitrogen sources were screened for enhanced bacterial growth. The factors affecting Bacillus thuringiensis’s biomass production like concentration of carbon, nitrogen, pH and temperature were optimized by one parameter at a time technique. The optimal levels of the selected parameters were also obtained by using an Artificial Neural Network (ANN). Peptone and molasses were selected as the best nitrogen and carbon sources respectively. The optimal levels obtained for nitrogen, carbon, pH and temperature by using the one parameter at a time technique were 1%, 0.25%, 8, and 37 ℃ respectively with 0.53 mg/mL biomass production. The ANN predicted levels were 1% for nitrogen, 0.25% for carbon, 9 pH and 31 ℃ for temperature with the predicted value of biomass being 0.85 mg/ml. The biomass produced at predicted optimum levels of variables was 0.82 mg/ml, very close to the predicted value of 0.85 mg/ml.

7

Sase, S., H. Ikeda, and T. Takezono. "PLANT PRODUCTION IN THE ARTIFICIAL ENVIRONMENT." Acta Horticulturae, no. 230 (September 1988): 323–28. http://dx.doi.org/10.17660/actahortic.1988.230.42.

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Mould, Richard F. "Artificial Production of Radioactive Elements 1934." Zeitschrift für Medizinische Physik 19, no. 3 (August 2009): 155. http://dx.doi.org/10.1016/j.zemedi.2009.05.001.

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9

De Toni, Alberto, Guido Nassimbeni, and Stefano Tonchia. "An artificial, intelligence‐based production scheduler." Integrated Manufacturing Systems 7, no. 3 (June 1996): 17–25. http://dx.doi.org/10.1108/09576069610116896.

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Goswami, Saurabh, and Dr Tej Singh Chouhan. "Artificial Lift to Boost Oil Production." International Journal of Engineering Trends and Technology 26, no. 1 (August 25, 2015): 1–5. http://dx.doi.org/10.14445/22315381/ijett-v26p201.

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11

Owusu, Winnie A., and Solomon A. Marfo. "Artificial Intelligence Application in Bioethanol Production." International Journal of Energy Research 2023 (October 10, 2023): 1–8. http://dx.doi.org/10.1155/2023/7844835.

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Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade.

12

VIEBIG, Ricardo Guilherme. "NAVIGATING THE ADVANTAGES AND CHALLENGES OF INTEGRATING ARTIFICIAL INTELLIGENCE IN SCIENTIFIC PRODUCTION." Arquivos de Gastroenterologia 60, no. 1 (January 2023): 1–3. http://dx.doi.org/10.1590/s0004-2803.202301000-01.

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13

Thivhilaheli, Richard Netshirovha, Mammikele Tsatsimpe, Thabo Muller, Fhulufhelo Vincent Ramukhithi, Masindi Lotus Mphaphathi, Gogamatsamang Makgothi, and Ronald Sylvester Thomas. "Artificial Insemination Training Program for Smallholder Pig Farms in Gauteng Province, South Africa." Journal of Agricultural Science 12, no. 12 (November 15, 2020): 89. http://dx.doi.org/10.5539/jas.v12n12p89.

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The aim of this study was to facilitate artificial insemination training to enhance sustainable pig production within the developing smallholder pig production sector in Gauteng Province, South Africa. Eighteen smallholder pig farmers with requisite structures (pig house, pens), pigs (large white, landrace duroc or South African indigenous) and management (feeding, cleaning and record keeping) capacity were trained on routine pig management and artificial insemination procedures in a “learning by doing” on-farm supervised programme administered by Agricultural Research Council, Animal Production pig training team. Following estrus detection, 96 sows were artificially inseminated and 31 naturally served (NS). Farrowing rates (FR), total born (TB) and born alive (BA) piglets were recorded. The occurrence ccurrence of mummified fetuses (0.019 vs. 0.022%) and weak piglets (0.038 vs. 0.049%) did not differ between artificially inseminated sows and naturally mated sows. Born alive, birth weight and weaning weight were higher for artificial inseminated sows. The average litter size was 15± and 13±, birth weight 1.98±0.79 kg and 1.48±0.58 kg and weaning weight 9.89±0.87 kg and 7.23±0.71 kg for the AI and NS litters, respectively. Farmer demographic factors (age, gender and educational level) had no effect on farrowing rate, total born and piglets born alive. Therefore, implementation of artificial insemination techniques and pig production training was feasible under a smallholder pig production system.

14

Denney, Dennis. "Production Optimization With Combined Artificial-Lift Systems." Journal of Petroleum Technology 51, no. 05 (May 1, 1999): 42–43. http://dx.doi.org/10.2118/0599-0042-jpt.

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15

IBARAKI, Yasuomi. "Photosynthesis and Artificial Lighting for Plant Production." Shokubutsu Kankyo Kogaku 30, no. 2 (June 1, 2018): 79–86. http://dx.doi.org/10.2525/shita.30.79.

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16

Winter, Felix, Christoph Mrkvicka, Nysret Musliu, and Jakob Preininger. "Automated Production Scheduling for Artificial Teeth Manufacturing." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 500–508. http://dx.doi.org/10.1609/icaps.v31i1.15997.

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In industrial artificial teeth manufacturing, nowadays a high level of automation is utilized to produce a large quantity of teeth in short production cycles. As a large variety of different product shapes and colors have to be processed on a single machine, the creation of efficient production schedules becomes a very challenging task. Due to the complexity of the problem and several cost minimization objectives that need to be considered, there usually is a large potential to improve the currently manually created schedules with automated solution methods. In this paper, we formally specify and solve a novel challenging real-life machine batch scheduling problem from the area of artificial teeth manufacturing. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. To efficiently solve the problem, we propose an innovative construction heuristic and metaheuristic approach as well as an exact method using constraint programming. An extensive experimental evaluation shows that exact techniques can efficiently solve small scheduling scenarios and can provide optimal solutions for four instances. Furthermore, we show that the proposed metaheuristic approach is able to reach optimal results for small instances and can find high quality solutions also for large real-life benchmark instances.

17

Goshovskyі, S. V., and A. V. Zurіan. "REVIEW OF ARTIFICIAL GAS HYDRATES PRODUCTION TECHNOLOGIES." Geology and mineral resources of world ocean 15, no. 1 (March 2019): 97–114. http://dx.doi.org/10.15407/gpimo2019.01.097.

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18

Lind et al., Yuliya. "Artificial Neural Networks in Oil Production Problems." Journal of Applied Nonlinear Dynamics 3, no. 4 (December 2014): 229–306. http://dx.doi.org/10.5890/jand.2014.12.001.

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19

Fukuzumi, Shunichi. "Artificial photosynthetic systems for production of hydrogen." Current Opinion in Chemical Biology 25 (April 2015): 18–26. http://dx.doi.org/10.1016/j.cbpa.2014.12.008.

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20

Hynynen, Juha. "Using artificial intelligence technologies in production management." Computers in Industry 19, no. 1 (April 1992): 21–35. http://dx.doi.org/10.1016/0166-3615(92)90004-7.

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21

Lim, Seongje, Kwang Hee Kim, Sungsoon Kim, and Jong Hyeok Park. "Artificial Photosynthesis for Value-Added Chemicals Production." Ceramist 23, no. 4 (December 31, 2020): 324–38. http://dx.doi.org/10.31613/ceramist.2020.23.4.01.

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22

Amao, Yutaka, Naho Shuto, Kana Furuno, Asami Obata, Yoshiko Fuchino, Keiko Uemura, Tsutomu Kajino, et al. "Artificial leaf device for solar fuel production." Faraday Discuss. 155 (2012): 289–96. http://dx.doi.org/10.1039/c1fd00097g.

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23

Komarysta, Bohdana, Iryna Dzhygyrey, Vladyslav Bendiuh, Olha Yavorovska, Antonina Andreeva, Kateryna Berezenko, Iryna Meshcheriakova, Oksana Vovk, Sofiia Dokshyna, and Ivan Maidanskyi. "Optimizing biogas production using artificial neural network." Eastern-European Journal of Enterprise Technologies 2, no. 8 (122) (April 29, 2023): 53–64. http://dx.doi.org/10.15587/1729-4061.2023.276431.

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The object of this study is the operating parameters of the anaerobic digestion unit. The study aims to increase the potential of biogas production. The task to select the optimal parameters of the working process of anaerobic digestion has been solved. A model of cumulative biogas and methane output in the process of anaerobic waste digestion has been constructed, which is conceptualized using the method of artificial neural network. The model is built on the basis of 11 process-related variables, such as hydraulic retention time, pH, operating temperature, etc. The plant parameters, leading to the highest volume of biogas production, were selected. It was determined that the optimal amount of biogas can be brought to 90 %, which exceeds the maximum value obtained from factory records by 12.6 % to 700 m3/t. Working conditions that led to optimal methane production were defined as the temperature of 39 °C, the total solids of 4.5 %, the organic percentage of 97.8 %, and pH 8.0. It was found that biogas production is the highest at temperature within the thermophilic range while the local maximum is achieved in the mesophilic temperature range. The model built serves to determine the optimal operating parameters for maximum biogas production and could be used to optimize biogas production productivity using limited experimental data. The model also makes it possible to predict the performance of anaerobic digestion under untested conditions. It is possible to practically use the developed biogas production model when monitoring the operation of the anaerobic digestion unit, to increase the efficiency of the process, and when adjusting the working conditions of the methane tank

24

Park, Hyun, Weichen Wang, Seo Hyeon Min, Yongshuo Ren, Kwanwoo Shin, and Xiaojun Han. "Artificial organelles for sustainable chemical energy conversion and production in artificial cells: Artificial mitochondrion and chloroplasts." Biophysics Reviews 4, no. 1 (March 2023): 011311. http://dx.doi.org/10.1063/5.0131071.

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Sustainable energy conversion modules are the main challenges for building complex reaction cascades in artificial cells. Recent advances in biotechnology have enabled this sustainable energy supply, especially the adenosine triphosphate (ATP), by mimicking the organelles, which are the core structures for energy conversion in living cells. Three components are mainly shared by the artificial organelles: the membrane compartment separating the inner and outer parts, membrane proteins for proton translocation, and the molecular rotary machine for ATP synthesis. Depending on the initiation factors, they are further categorized into artificial mitochondrion and artificial chloroplasts, which use chemical nutrients for oxidative phosphorylation and light for photosynthesis, respectively. In this review, we summarize the essential components needed for artificial organelles and then review the recent progress on two different artificial organelles. Recent strategies, purified and identified proteins, and working principles are discussed. With more study on the artificial mitochondrion and artificial chloroplasts, they are expected to be very powerful tools, allowing us to achieve complex cascading reactions in artificial cells, like the ones that happen in real cells.

25

Faghri, Ardeshir, and Sandeep Aneja. "Artificial Neural Network–Based Approach to Modeling Trip Production." Transportation Research Record: Journal of the Transportation Research Board 1556, no. 1 (January 1996): 131–36. http://dx.doi.org/10.1177/0361198196155600115.

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Accurate and reliable estimates of trip production of a study area are important for an accurate forecast from the four-step travel demand forecasting procedure. In the trip generation step, trip production estimates are considered more accurate, and trip attractions are adjusted while keeping the productions constant. This means that more accurate trip production rates will result in more reliable forecasts. Improving the accuracy of forecasts requires an extensive and reliable data base or improvement in the modeling techniques. Since data base enhancement is costly and time-consuming, an alternative methodology is proposed and examined for trip production prediction using artificial neural network (ANN) concepts and techniques. The data base used was made available by the Delaware Department of Transportation. The data were collected for 60 sites throughout Delaware between 1970 and 1974 and are based on field counts and home interviews. Twenty-six regression models were calibrated on these data. In addition, 18 ANN architectures were developed, and their predictions were compared with those from regression models. Comparisons indicate that the ANNs have the capability to represent the relationship between the trip production rate and the independent variables more accurately than regression analysis at no additional cost of increasing the data base.

26

Bajohr, Hannes. "Artificial and Post-Artificial Texts." Basel Media Culture and Cultural Techniques Working Papers, no. 007 (March 1, 2023): 1–31. http://dx.doi.org/10.12685/bmcct.2023.007.

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In contrast to the times of Höllerer and Zemanek, we are now truly on the threshold of being surrounded by texts that are entirely artificial—while at the same time we continue to merge with our language technologies in our own writing, so that our text production is increasingly supported, extended, and partially taken over by assistance systems. Therefore, I want to discuss—quite speculatively, but always with an eye on the state of the art—two questions: first, what happens when we are confronted with artificial texts in addition to natural ones? How do we read a text that we can no longer be sure was not written by an AI? And second, what direction might this development take if, at some point, the distinction between natural and artificial itself becomes obsolete, so that we no longer even ask about it and instead read post-artificial texts?

27

Stover, Kyle. "Insuring Artificial Stone." Enquiry The ARCC Journal for Architectural Research 20, no. 2 (November 10, 2023): 127–33. http://dx.doi.org/10.17831/enqarcc.v20i2.1158.

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This paper examines experiments to increase the durability of architecture as a means to manage the risk of catastrophic loss through tangible systems of artificial material and intangible systems of insurance. At the intersection of these dyads is Coade’s Artificial Stone Manufactory conducting experiments in architecture to yield an ornament useful in securing capital from 1769 - 1821. Selling goods from the south bank of the River Thames in London, Coade’s made use of a catalog to mediate the global exchange between the site of production and construction. For architects and builders, these commodities construct a modern architecture relying upon cheapness, mass production, and abstraction. The utility of this artificial stone explicates a relationship between durability and catastrophic failure at work in the manufacture of modern architecture.

28

Shang, Shilong, Lijuan Gu, and Hailong Lu. "The Effects of the Length and Conductivity of Artificial Fracture on Gas Production from a Class 3 Hydrate Reservoir." Energies 14, no. 22 (November 10, 2021): 7513. http://dx.doi.org/10.3390/en14227513.

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Natural gas hydrate is considered as a potential energy resource. To develop technologies for the exploitation of natural gas hydrate, several field gas production tests have been carried out in permafrost and continental slope sediments. However, the gas production rates in these tests were still limited, and the low permeability of the hydrate-bearing sediments is identified as one of the crucial factors. Artificial fracturing is proposed to promote gas production rate by improving reservoir permeability. In this research, numerical studies about the effect of fracture length and fluid conductivity on production performance were carried out on an artificially fractured Class 3 hydrate reservoir (where the single hydrate zone is surrounded by an overlaying and underlying hydrate-free zone), in which the equivalent conductivity method was applied to depict the artificial fracture. The results show that artificial fracture can enhance gas production by offering an extra fluid flow channel for the migration of gas released from hydrate dissociation. The effect of fracture length on production is closely related to the time frame of production, and gas production improvement by enlarging the fracture length is observed after a certain production duration. Through the production process, secondary hydrate formation is absent in the fracture, and the high conductivity in the fracture is maintained. The results indicate that the increase in fracture conductivity has a limited effect on enhancing gas production.

29

Leizerovich Krichevsky, Mikhail, Svetlana Vladimirovna Dmitrieva, and Julia Anatolevna Martynova. "Instruments of Artificial Intelligence in Management of High Technology Production." International Journal of Engineering & Technology 7, no. 3.14 (July 25, 2018): 347. http://dx.doi.org/10.14419/ijet.v7i3.14.17024.

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The instruments of artificial intelligence (AI) that can be used in management of high technology production and training students are considered. Specific differences and characteristics of high technology production (HTP) that set certain requirements for such production management are specified. Brief information from the AI methods that include artificial neural networks, fuzzy logic, genetic algorithms and their combinations are given. It is indicated that there is a relation between the level of training masters and the requirements of modern productions. The necessity to use techniques and methods of AI when training students to form their competencies, knowledge and skills that comply with the HTP is explained. The techniques of using AI instruments in the educational process focused on the practical importance of the tasks being solved in such disciplines as HR management, risk management, strategic management, etc. are shown.

30

Mazzoni, Fábio Muniz, and André da Silva Barcelos. "MONITORAMENTO DA PRODUÇÃO ENXUTA COM INTELIGÊNCIA ARTIFICIAL / LEAN PRODUCTION MONITORING WITH ARTIFICIAL INTELLIGENCE." Brazilian Journal of Development 7, no. 3 (2021): 26595–609. http://dx.doi.org/10.34117/bjdv7n3-385.

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31

Mandelstam, I. G. "Some comments on the production of a legitimate artificial abortion by scraping." Journal of obstetrics and women's diseases 11, no. 3 (December 22, 2020): 267–75. http://dx.doi.org/10.17816/jowd113267-275.

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Recently, two works have appeared in the Russian medical literature, treating about scooping or scraping as a method for producing a legitimate artificial miscarriage: part of the pr., I. Lvov), and part of the pr., A. A. Muratov). Both of these authors refer to an article by Dr. P. Puech, entitled: "Du Curettage comme mthode d'avortement artificiel", published in the August book of Annales de Gyncologie et d'obsttr. for 1895. Within the meaning of this reference, one may think that Puech was the first to recommend this method in print as an independent one and pointed out its advantages over all other methods practiced before to induce an artificial abortion. Lvov (1. p.), Says: True, many other authors resorted to removing the fetal egg from the uterus with a sharp or blunt spoon, but they resorted to this method not on their own, but accidentally, as an auxiliary technique when it was impossible to remove fertile eggs in another way. "

32

Ruiz- Serna, Marco Antonio, Guillermo Arturo Alzate- Espinosa, Andrés Felipe Obando- Montoya, and Hernán Dario Álvarez- Zapata. "Combined artificial intelligence modeling for production forecast in a petroleum production field." CT&F - Ciencia, Tecnología y Futuro 9, no. 1 (May 10, 2019): 27–35. http://dx.doi.org/10.29047/01225383.149.

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This paper presents the results about using a methodology that combines two artificial intelligence (AI) models to predict the oil, water and gas production in a Colombian petroleum field. By combining fuzzy logic (FL) and artificial neural networks (ANN) a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include into each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are capable to predict specific behaviors, while ANN models are able to forecast an average behavior. The combined use of both tools under few iterative steps, allows to improve forecasting of well behavior until reach a specified accuracy level. The proposed data imputation procedure is the key element to correct false or to complete void positions into operation data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.

33

Arinichev, I. V., V. A. Sidorov, and I. V. Arinicheva. "Superfrontiers of Crop Production: Artificial Intelligence in Formation the Grain Production Ecosystem." BIO Web of Conferences 108 (2024): 06002. http://dx.doi.org/10.1051/bioconf/202410806002.

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The paper presents a study dedicated to analyzing the role of crop production superfrontiers in forming the agricultural production ecosystem, with a focus on the grain sector. Special attention is given to the superfrontier - data intelligence analytics as an effective tool for optimizing grain production. The authors consider specific examples of successful integration of intelligent analytical methods into practice, identifying their positive impact on improving production efficiency, including cost reduction and business process management enhancement. Among the main barriers to the implementation and use of intelligent technologies and systems, the lack of a unified methodology for collecting and preparing data for training and configuring intelligent grain production systems as a whole is noted. It is shown that artificial intelligence forms the basis of modern monitoring systems, permeating the ecosystem at all levels (supply, production, sales). Considering these features, the article presents a fundamental scheme for organizing the grain production ecosystem.

34

Matveev, V. I. "ARTIFICIAL INTELLIGENCE." Kontrol'. Diagnostika, no. 275 (May 2021): 56–63. http://dx.doi.org/10.14489/td.2021.05.pp.056-063.

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Artificial intelligence is becoming the main direction of the development of science and technology, making progress at a new level. Automation of production, the implementation of operations in hazardous and harmful areas, the implementation of routine actions in the environment are inevitable in the modern world. A person creates an analogue for himself, realizing the possible consequences and limiting them to legislative acts. The article provides positive examples of the implementation of the artificial intelligence project and legislative measures that limit its impact on the social environment.

35

Jian Wang, Jian Wang, Dong-Liang Fan Jian Wang, Jin-Ping Du Dong-Liang Fan, Lei Geng Jin-Ping Du, and Ya-Jin Hou Lei Geng. "Research on Artificial Intelligence Detection Method of Lithium Battery Surface Defects for Production Line." 電腦學刊 34, no. 2 (April 2023): 203–14. http://dx.doi.org/10.53106/199115992023043402015.

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<p>Lithium batteries are widely used in new energy vehicles and electronic equipment. Aiming at the typical defects that are easy to occur in the production process of lithium batteries, this paper improves the performance and recognition accuracy of the algorithm by integrating void convolution and attention mechanism into the YOLOv5 basic framework. At the same time, whale algorithm is used to automatically optimize the algorithm parameters in the process of optimization. Finally, through simulation experiments. This method realizes the rapid and accurate identification of lithium battery defects in the rapid production process of automatic production line.</p> <p> </p>

36

Wardoyo, Galih Kusumo, Heru Berian Pratama, Sutopo, Ali Ashat, and Yodha Yudhistira. "Application of Artificial Intelligence in Forecasting Geothermal Production." IOP Conference Series: Earth and Environmental Science 732, no. 1 (April 1, 2021): 012022. http://dx.doi.org/10.1088/1755-1315/732/1/012022.

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37

Cho, Eun-Il, and Tae-Seok Kim. "Life Cycle Assessment of Artificial Reefs Production Process." Korean Ecological Engineering Society 6, no. 2 (December 30, 2018): 15–24. http://dx.doi.org/10.33214/kees.2018.6.2.15.

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38

Silva, C. B. da, and P. R. P. de Paiva. "Artificial stone production using iron ore tailing (IOT)." Cerâmica 66, no. 378 (June 2020): 164–71. http://dx.doi.org/10.1590/0366-69132020663782854.

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Abstract Artificial stone materials (ASM) were produced with an iron ore tailing (IOT) from the disruption of Fundão’s tailing dam, located in Mariana, Minas Gerais State, Brazil. The IOT was separated in 3 powders with different particle sizes: DAG (<600 μm), DAR (600 to 75 μm), and DSA (<75 μm); then, each powder was characterized and mixed with a polymer resin (polyester or epoxy). ASM samples were prepared using the transfer molding technique; vacuum vibration technology was also applied to half of the samples. After curing, the ASM samples were characterized for mechanical properties and physical parameters. The microstructure of polished ASM samples was also analyzed by scanning electron microscopy. All results indicated that ASM samples produced with epoxy resin were superior to the samples made with polyester resin. The features found for the different compositions and shaping conditions for the produced ASM samples can allow various applications for these alternative materials in the construction industry, such as floor and wall tiles, providing a means of reducing the amount of IOT deposited in the tailing dams and adding economic value to this waste.

39

Yu, Chenggong. "Artificial Intelligence-Based Drug Production Quality Management Data." Mathematical Problems in Engineering 2022 (August 23, 2022): 1–14. http://dx.doi.org/10.1155/2022/4089917.

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As a special commodity, medicine plays a vital role in people’s healthy life. The basic effects of drugs are excitation and inhibition. Under the action of drugs, any function that can enhance or increase the function of the body’s original tissues and organs is called excitement. On the contrary, any function that can weaken or reduce the function of the original tissues and organs of the body is called inhibition. The nature of action of drugs has three aspects: regulatory function; antipathogen and antitumor; and complementary therapy. If there is a problem with the quality and safety of medicines, it is tantamount to making money and killing people. Based on artificial intelligence, this paper analyzes the current situation and improvement strategies of quality management in pharmaceutical production management enterprises and proposes how to reduce the risk of drug safety with the assistance of artificial intelligence technology. The experimental results in this paper show that the sales of heparin sodium APIs were 2.099 billion yuan, accounting for 91.6% of the operating income in 2015, when company A had not conducted a drug risk assessment in 2015. After the outbreak of drug risks, the sales in 2016 were 1.743 billion yuan, accounting for 77.1% of the operating income in 2016. After the final implementation of the measures, the sales in 2019 were 4.743 billion yuan, accounting for 329.1% of the operating income in 2016. The research method in this paper can improve the hidden safety problems of drugs more efficiently, and it can improve the profit while ensuring the safety.

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Seber, Rogério, Daniella Moura, Nilsa Lima, and Juliana Massari. "LED TUBS ARTIFICIAL LIGHTING SYSTEM IN BROILER PRODUCTION." Engenharia Agrícola 38, no. 3 (June 2018): 319–25. http://dx.doi.org/10.1590/1809-4430-eng.agric.v38n3p319-325/2018.

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TANAKA, HIDEKI. "Studies on artificial fry production of Japanese eel." NIPPON SUISAN GAKKAISHI 77, no. 3 (2011): 345–51. http://dx.doi.org/10.2331/suisan.77.345.

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Arya, Satyendra. "A study on artificial intelligence in production management." South Asian Journal of Marketing & Management Research 11, no. 11 (2021): 102–7. http://dx.doi.org/10.5958/2249-877x.2021.00118.1.

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Fertsch, Marek. "Artificial neural pseudo-network for production control purposes." Logforum 16, no. 1 (March 30, 2020): 7–13. http://dx.doi.org/10.17270/j.log.2020.382.

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Kumar, Hemant, and BK Hooda. "Prediction of milk production using artificial neural network." Current Advances in Agricultural Sciences(An International Journal) 6, no. 2 (2014): 173. http://dx.doi.org/10.5958/2394-4471.2014.00013.6.

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Muerdter, F., I. Olovnikov, A. Molaro, N. V. Rozhkov, B. Czech, A. Gordon, G. J. Hannon, and A. A. Aravin. "Production of artificial piRNAs in flies and mice." RNA 18, no. 1 (November 17, 2011): 42–52. http://dx.doi.org/10.1261/rna.029769.111.

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Fu, Chengjie, Zhengzhong Shao, and Vollrath Fritz. "Animal silks: their structures, properties and artificial production." Chemical Communications, no. 43 (2009): 6515. http://dx.doi.org/10.1039/b911049f.

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Zachs, G. G. "On the production of a legal artificial miscarriage." Journal of obstetrics and women's diseases 11, no. 12 (December 23, 2020): 1557–63. http://dx.doi.org/10.17816/jowd11121557-1563.

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Abstract:

The question of the treatment of miscarriage and the question of the production of a legitimate artificial miscarriage, which is closely connected with it, seems to be one of the most vital issues of modern gynecology, which are of great importance for a practitioner. In most cases, this requires a quick decisive action on the part of the doctor, and it is quite understandable why we are so interested in the question: in what way, in what way it is possible to achieve the goal most surely and faster with the least danger for the patient. As you know, the danger to a patient with miscarriage, whether it is artificial or not, lies on the one hand in bleeding, and on the other in infection; both the one and the other danger is depending on how quickly and completely the emptying of the uterus occurred. Cases of prolonged or incomplete miscarriage pose the greatest danger from both infection and blood loss.

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Fakharudin, Abdul Sahli, Md Nasir Sulaiman, and Norwati Mustapha. "Artificial Neural Network Modelling of Biogas Production Processes." Advanced Science Letters 24, no. 10 (October 1, 2018): 7582–87. http://dx.doi.org/10.1166/asl.2018.12982.

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Sodha, Mahendra Singh, Gyan Prakash, and Sujeet Kumar Agarwal. "Production of Artificial Conducting Regions in the Atmosphere." IEEE Transactions on Plasma Science 47, no. 11 (November 2019): 5113–18. http://dx.doi.org/10.1109/tps.2019.2941552.

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Vajravel, Sindhujaa, Sema Sirin, Sergey Kosourov, and Yagut Allahverdiyeva. "Towards sustainable ethylene production with cyanobacterial artificial biofilms." Green Chemistry 22, no. 19 (2020): 6404–14. http://dx.doi.org/10.1039/d0gc01830a.

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