Journal articles: 'Production support'

1

Nakagawa, Yoshiaki. "Advanced IT to Support Production." JAPAN TAPPI JOURNAL 73, no. 3 (2019): 188–93. http://dx.doi.org/10.2524/jtappij.73.188.

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HIBINO, Hironori, and Kentaro YANAGA. "Decision support for energy-saving idle production facility operations (Decision support in production line)." Transactions of the JSME (in Japanese) 84, no. 868 (2018): 18–00133. http://dx.doi.org/10.1299/transjsme.18-00133.

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Avliyakulov, Nodir Nizomovich, and Sadriddin Ubaidilloevich Rukhilloev. "ACTIVITY OF ME Y OF METROLOGICAL SUPPOR OGICAL SUPPORT OF PRODUC T OF PRODUCTION PROVIDING RELEASE OF QU VIDING RELEASE OF QUALITY PRODUC Y PRODUCTS." Scientific Reports of Bukhara State University 4, no. 1 (February 26, 2020): 19–24. http://dx.doi.org/10.52297/2181-1466/2020/4/1/2.

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Improving the efficiency of production and quality of products is possible only with the organization of modern metrological support. The paper presents measures and tasks concerning metrological support of production at the stages of design, development, production and testing, contributing to the production of quality products. To fulfill the tasks assigned to the metrological service, it must have a position, structure, quality assurance system, personnel, necessary premises, conditions for the operation and storage of measuring instruments.

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Unsworth, Richard K. F., Lina Mtwana Nordlund, and Leanne C. Cullen-Unsworth. "Seagrass meadows support global fisheries production." Conservation Letters 12, no. 1 (May 21, 2018): e12566. http://dx.doi.org/10.1111/conl.12566.

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Allan, Craig. "EL production support: procedures and tools." Library Hi Tech 16, no. 3/4 (December 1998): 132–37. http://dx.doi.org/10.1108/07378839810305981.

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WAN, Jiangping. "Research on Software Production Support Structure." Journal of Software Engineering and Applications 02, no. 03 (2009): 173–94. http://dx.doi.org/10.4236/jsea.2009.23025.

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Matthies, Klaus. "Production cuts to support oil prices." Intereconomics 36, no. 5 (September 2001): 272–76. http://dx.doi.org/10.1007/bf02928981.

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Bakhrankova, Krystsina. "Decision support system for continuous production." Industrial Management & Data Systems 110, no. 4 (April 27, 2010): 591–610. http://dx.doi.org/10.1108/02635571011039043.

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Renna, Paolo. "Coordination strategies to support distributed production planning in production networks." European J. of Industrial Engineering 9, no. 3 (2015): 366. http://dx.doi.org/10.1504/ejie.2015.069342.

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Stoffels, Pascal, and Michael Vielhaber. "Decision Support for Energy Efficient Production in Product and Production Development." Procedia CIRP 40 (2016): 530–35. http://dx.doi.org/10.1016/j.procir.2016.01.128.

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Krasnyuk, Ivan, and Natalya Demina. "Information and analytical support for pharmaceutical production." Farmacevticheskoe delo i tehnologija lekarstv (Pharmacy and Pharmaceutical Technology), no. 1 (February 1, 2020): 43–48. http://dx.doi.org/10.33920/med-13-2001-04.

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Operation of modern pharmaceutical enterprise is impossible without information and analytical support providing prompt access to information, its analysis and the use of data for the functioning of enterprise. Structure of pharmaceutical enterprise includes divisions, which are often remote from each other. Centralized administration of their concerted activities, day-to-day communication, information transfer are important objectives of production meeting the requirements of international standards. Today, these issues are solved with the help of information technology providing collection and analysis of information necessary for sound management decisions and implemented on the basis of modern computer technology and modern means of communication.

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Gabriela, Oshiro Reynaldo, Martin de Moraes Paula, Skowronski Leandro, Paes Herrera Gabriel, Vieira de Araújo Rildo, Constantino Michel, and Brito Costa Reginaldo. "Organic production and its market support policies." African Journal of Agricultural Research 14, no. 26 (June 27, 2019): 1081–90. http://dx.doi.org/10.5897/ajar2018.13618.

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Korchevskiy, O. S., and L. V. Kolomiets. "METROLOGICAL SUPPORT OF PRODUCTION OF OPTICAL CABLE." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 2(5) (2014): 62–67. http://dx.doi.org/10.32684/2412-5288-2014-2-5-62-67.

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Mikhnova, Alina, Dmitro Mikhnov, and Kateryna Chyrkova. "Information support model of production trunsfusion processes." Eastern-European Journal of Enterprise Technologies 3, no. 3(81) (June 28, 2016): 36. http://dx.doi.org/10.15587/1729-4061.2016.71673.

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de Rosa, Daniele, Bruno Basso, David W. Rowlings, Clemens Scheer, Johannes Biala, and Peter R. Grace. "Can Organic Amendments Support Sustainable Vegetable Production?" Agronomy Journal 109, no. 5 (September 2017): 1856–69. http://dx.doi.org/10.2134/agronj2016.12.0739.

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Wai, Watt Kwong, Lin Ting, Liang Wei Pang, Hadianto Budihardjo, Gan Chiu Liang, Ding Liya, Zhu Fangming, and Charles Pang T-Howe. "Decision Support System for Production Scheduling (DSSPS)." Procedia Computer Science 96 (2016): 315–23. http://dx.doi.org/10.1016/j.procs.2016.08.144.

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Lennox, B., K. Kipling, J. Glassey, G. Montague, M. Willis, and H. Hiden. "Automated Production Support for the Bioprocess Industry." Biotechnology Progress 18, no. 2 (April 5, 2002): 269–75. http://dx.doi.org/10.1021/bp0101839.

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18

Audsley, E. "Decision Support Systems for Profitable Livestock Production." Journal of Agricultural Science 121, no. 1 (August 1993): 131–34. http://dx.doi.org/10.1017/s0021859600076887.

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The Agricultural and Related Industries Study Group of the Operational Research Society held a one-day meeting on ‘Decision Support Systems for Profitable Livestock Production’ on 18 November 1992 at the Meat and Livestock Commission, Milton Keynes, UK. The Group promotes the use of the scientific method in solving management problems. The aim of the meeting was to look at the contribution of advances in modelling towards improving management and hence profitability in livestock production. The first four papers concerned feeding and the remaining two breeding. A large part of the discussion could perhaps best be summed up by the regression equation which related dry matter intake and waste output from a cow. When the output was zero, the intake was reduced by 50%!

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Ding, Fong-Yuen, and Ravi Tolani. "Production planning to support mixed-model assembly." Computers & Industrial Engineering 45, no. 3 (October 2003): 375–92. http://dx.doi.org/10.1016/s0360-8352(03)00071-8.

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Johnston, David, Mark Pagell, Anthony Veltri, and Robert Klassen. "Values-in-action that support safe production." Journal of Safety Research 72 (February 2020): 75–91. http://dx.doi.org/10.1016/j.jsr.2019.11.004.

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21

Hailu, Getu, and Kenneth Poon. "Do Farm Support Programs Reward Production Inefficiency?" Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 65, no. 4 (November 2, 2017): 567–89. http://dx.doi.org/10.1111/cjag.12150.

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22

Zhichkin, Kirill, Vladimir Nosov, Lyudmila Zhichkina, Vladimir Panchenko, Elena Zueva, and Darya Vorob’eva. "Modelling of state support for biodiesel production." E3S Web of Conferences 203 (2020): 05022. http://dx.doi.org/10.1051/e3sconf/202020305022.

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Government support for the development of biofuel production is a relevant part of the system of budget regulation of agricultural production in the Russian Federation. Currently, there is no sound financing method for mechanisms of state regulation of biofuel production which impedes impartial allocation of funds and makes this procedure non-transparent and not motivated enough. In view of this situation, a mathematical economic model was developed that allows one to calculate the optimum level of government support for every type of biofuel considering main areas of state support. We propose to consider three scenarios for the determination of the optimum level of public funding. The first one allows for optimization of the level of government support considering sizes of agricultural production for the i-th crop to provide farms of the region. The second scenario suggests the determination of the maximum profit from the biofuel production through increased agriculturally used areas. Finally, the third one considers calculation of the minimum expenses of achieving the volume of production that provides the farm with raw materials. According to the first scenario, the optimum level of government support for the field should be 1163.6 million rubles. In the implementation of the second scenario in the Samara region, the agriculturally used area planted with oil crops should be increased by 47.1 thousand ha.

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Sullivan, Kelly J. "$16M to support USDA food production security." Federal Grants & Contracts 39, no. 6 (February 17, 2015): 1. http://dx.doi.org/10.1002/fgc.30012.

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Sokolov, A. "Decision support system for energy wood production." IOP Conference Series: Earth and Environmental Science 574 (October 30, 2020): 012075. http://dx.doi.org/10.1088/1755-1315/574/1/012075.

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25

Schmidt, Günter. "A Decision Support System for Production Scheduling." Journal of Decision Systems 1, no. 2-3 (January 1992): 243–60. http://dx.doi.org/10.1080/12460125.1992.10511527.

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26

Felea, Ioan, Simona Dzitac, Tiberiu Vesselenyi, and Ioan Dzitac. "Decision Support Model for Production Disturbance Estimation." International Journal of Information Technology & Decision Making 13, no. 03 (May 2014): 623–47. http://dx.doi.org/10.1142/s0219622014500576.

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A current modeling framework for disturbance in manufacturing systems (MS) is given by concepts like discrete-event systems, stochastic fluid models and infinitesimal disturbance analysis. The goal of modeling is to achieve control and structural and functional optimization of MS. Objective functions of these optimization models are focused on quantities which reflect the level of reliability, the level of manufactured products, the quality of products or the impact on the environment of MS with disturbances. These models do not allow a dynamic evaluation of consequences of the disturbances which appears in the operation of MS machines and also do not allow an evaluation of the evolution in time of disturbance consequence indicators. Disturbances in technological lines of MS represent local bottlenecks of production with severe economic consequences in what regards production time losses. Good estimation of disturbances dynamics can be very helpful to both technological line designers, who can optimize their projects and production managers who can minimize their losses. Our model allows a dynamic evaluation of consequences of some disturbance of machine operation in MS, using indicators based on time, energy and costs. A MATLAB software package was developed for tests.

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27

Plitsos, Stathis, Panagiotis P. Repoussis, Ioannis Mourtos, and Christos D. Tarantilis. "Energy-aware decision support for production scheduling." Decision Support Systems 93 (January 2017): 88–97. http://dx.doi.org/10.1016/j.dss.2016.09.017.

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28

Gupta, Manoj Kumar, D. V. K. Samuel, and N. P. S. Sirohi. "Decision support system for greenhouse seedling production." Computers and Electronics in Agriculture 73, no. 2 (August 2010): 133–45. http://dx.doi.org/10.1016/j.compag.2010.05.009.

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29

Repele, Mara, Lina Udrene, and Gatis Bazbauers. "Support Mechanisms for Biomethane Production and Supply." Energy Procedia 113 (May 2017): 304–10. http://dx.doi.org/10.1016/j.egypro.2017.04.070.

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30

Larshin, Vasily Petrovich, Natalia Vladimirovna Lishchenko, Olga Borisovna Babiychuk, and Ján Piteľ. "COMPUTER-AIDED DESIGN AND PRODUCTION INFORMATION SUPPORT." Herald of Advanced Information Technology 4, no. 2 (June 30, 2021): 111–22. http://dx.doi.org/10.15276/hait.02.2021.1.

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Information support for modern computer-aided design of products and processes is considered in this review in accordance with the methodology of the integrated CAD/CAM/CAE system. Three levels of the management hierarchy at the design and production stages are considered. At the top (organizational) level, computer-aided design of the product structure and its manufacturing technology is performed. At the middle (coordinating) level, a binding to existing technological equipment and debugging of individual fragments of the control program are performed. At the lower (executive) level, the control program is finally created, debugged and executed. A distinctive feature of the proposed automation methodology at the design and production stages is the use of feedback from the lower level to the middle and upper levels to correct the decisions made there, taking into account the existing management powers at these levels of the hierarchy. Thus, the indicated levels of the hierarchy of the intelligent system correspond to the hierarchy of objects and subjects of management and control, taking into account the powers (and capabilities) of management and control at each level. Information is a basic category not only in information (virtual) technology for its transformation and transmission, but also in physical technology of material production in the manufacture of a corresponding material product. Such technology as a rule, contain preparatory (pre-production) and executive (implementation) stages. At the preparatory stage, a virtual product is created (an information model of a real product in the form of virtual reality), and at the executive stage, a real (physical) product appears that has a use value (possession utility). This research describes the features of information processing at both stages of production in order to increase its efficiency.

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31

Zamula, Iryna, and Dmytro Liudvenko. "INFORMATION SUPPORT AND DEVELOPMENT OF ORGANIC PRODUCTION." Green, Blue and Digital Economy Journal 1, no. 2 (December 3, 2020): 1–7. http://dx.doi.org/10.30525/2661-5169/2020-2-1.

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The purpose of the paper is to justify the organizational and methodological provisions and to develop practical proposals for improving the information support of organic production delineating ways to advance the accounting system. To achieve this goal, we were focused on the next tasks: determination of the essence of the state and directions of development of organic production, research of legislative requirements for accounting of organic production and improvement of its accounting information support. Methodology. During the research, the following general and specific scientific methods were used: theoretical generalization, analogy, statistical observation, analysis, synthesis, scientific abstraction, critical analysis of accounting issues. Results. In the paper, the following issues were investigated: the place of Ukraine in the organic products market, the main advantages and disadvantages of organic products, ways to improve the information support for the development of organic products. It is proved that the establishment of organic agricultural production in Ukraine requires the development of its accounting support. According to the emergence of such support, it is possible to expect the development of this segment of the agricultural sector. Practical implications. In this paper, the accounting of organic production is improved considering the necessity of coordination of economic, social, and ecological activities of agricultural enterprises by making documentation of business operations related to organic production, confirming the need for internal control of organic production costs to improve their management efficiency, establishing a work plan of accounts regarding the formation of separate analytical accounts for the process of receipt of biological assets, producing organic products and selling them, as well as creating separate analytical accounts to determine the financial result (based on the proposed analytical calculations, the process of creating production costs and determining revenues can be simplified; if cross-production is carried out at an enterprise, then this technique will help to determine the benefits of organic production over conventional production easily), developing a system of financial and statistical reporting indicators for organic enterprises. Value / originality. The improvement of accounting and internal control system can provide the information with the required level of detail to meet the needs of interested users and to make such management decisions that will help to preserve natural resources and reduce the negative impact of enterprise activity on the environmental situation in Ukraine.

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Romanov, Anton A., Aleksei A. Filippov, Nadezhda G. Yarushkina, and Vladimir A. Maklaev. "THE DECISION SUPPORT MODULE OF INFORMATION ENVIRONMENT FOR TECHNOLOGICAL SUPPORT OF PRODUCTION." Автоматизация Процессов Управления 60, no. 2 (2020): 62–72. http://dx.doi.org/10.35752/1991-2927-2020-2-60-62-72.

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33

Jabjone, Saisunee, and Sura Wannasang. "Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand." International Journal of Computer and Electrical Engineering 6, no. 2 (2014): 162–66. http://dx.doi.org/10.7763/ijcee.2014.v6.814.

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34

Olhan, Emine, Hasan Arisoy, Yener Ataseven, and Coskun Ceylan. "Changes in Livestock Production Support Policies in Turkey and Effects on Production." Journal of Animal and Veterinary Advances 9, no. 3 (March 1, 2010): 570–75. http://dx.doi.org/10.3923/javaa.2010.570.575.

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35

Meier, M., M. Baecker, and W. Massberg. "User-friendly Production Control Systems to Support the Flexibility of Production Organization." CIRP Annals 50, no. 1 (2001): 335–38. http://dx.doi.org/10.1016/s0007-8506(07)62134-9.

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36

OHARA, Satoshi, Yoshifumi TERAJIMA, Akira SUGIMOTO, Tatsuhiro HAYANO, Kunihiro UJIHARA, Masaki SAGEHASHI, and Akiyoshi SAKODA. "Biomass Ethanol Production from Sugarcane for Energy Generation to Support Sugar Production." Journal of the Japan Institute of Energy 84, no. 11 (2005): 923–28. http://dx.doi.org/10.3775/jie.84.923.

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37

Araujo, Victor A. De, Elen A. M. Morales, Juliana Cortez-Barbosa, Maristela Gava, and José N. Garcia. "PUBLIC SUPPORT FOR TIMBER HOUSING PRODUCTION IN BRAZIL." CERNE 25, no. 4 (December 2019): 365–74. http://dx.doi.org/10.1590/01047760201925042652.

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38

Hiyoshi, Yoshihisa, Yoichi Ishikawa, Shiro Nishikawa, Shuhei Masuda, Hiromichi Igarashi, and Yuji Sasaki. "Production support system for 4D-VAR data assimilation." JAMSTEC Report of Research and Development 18 (2014): 103–14. http://dx.doi.org/10.5918/jamstecr.18.103.

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39

Туровец, Оскар, Oskar Turovyets, Валентина Родионова, and Valyentina Rodionova. "ORGANIZATIONAL FACTORS IN SUPPORT OF PRODUCTION SYSTEM FLEXIBILITY." Bulletin of Bryansk state technical university 2018, no. 3 (June 25, 2018): 88–96. http://dx.doi.org/10.30987/article_5b0532912a0c02.20682093.

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40

Mohylnytska, A. "INFORMATION SUPPORT OF ECOLOGICALLY ORIENTED MANAGEMENT AGRICULTURAL PRODUCTION." Agrosvit, no. 24 (January 6, 2021): 37. http://dx.doi.org/10.32702/2306-6792.2020.24.37.

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41

Minkova, Olha, Antonina Kalinichenko, and Oleg Gorb. "Research of organic agricultural production support in Poland." Technology audit and production reserves 2, no. 5(34) (March 30, 2017): 50–54. http://dx.doi.org/10.15587/2312-8372.2017.98376.

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42

Rizvanov, D. A., and E. S. Chernyshev. "Information and Algorithmic Support of Production Capacity Planning." Intellekt. Sist. Proizv. 18, no. 4 (December 29, 2020): 117. http://dx.doi.org/10.22213/2410-9304-2020-4-117-125.

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В настоящей статье рассматриваются вопросы применения интеллектуальных технологий для решения задачи планирования производственных мощностей при создании/модернизации производства. В силу того что расчет количества необходимого технологического оборудования ведется классическим способом – на основе станкоемкости на максимальную известную годовую программу, в которой различные простои предусмотрены на основе нормативов, задача эффективного планирования производственных мощностей остается актуальной. Расчет необходимого количества технологического оборудования предлагается проводить с помощью системы поддержки принятия решений, реализованной на основе многоагентных технологий. Интеграция с разработанной ранее многоагентной системой календарного планирования производства позволяет осуществить проверку выполнимости производственного плана с использованием рассчитанного состава и количества оборудования. Выделены основные агенты: «расчет», «программа», «объем», «деталь», «станок». Представлены алгоритмы поведения и взаимодействия основных агентов для решения задачи планирования производственных мощностей. Программное обеспечение прототипа СППР для планирования производственных мощностей реализовано на базе Embarcadero CodeGear RAD Studio. Приведены результаты оценки эффективности решения задачи с помощью СППР в сравнении с классическим методом расчета по показателям, отражающим суммарное количество необходимого технологического оборудования, затрат на его приобретение и календарный график мероприятий закупки и поставки оборудования.

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43

Ndiritu, C. G. "Remarks on donor support to animal production research." BSAP Occasional Publication 16 (1993): 5–11. http://dx.doi.org/10.1017/s0263967x00031013.

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AbstractIn spite of being the home of a significant number of the world's flocks and herds, the productivity per head and per ha of the livestock sector in sub-Saharan Africa continues to be so low that the total output does not meet the domestic demand. Indeed, there is evidence that there has been little or no increase in output of animal products in this region during the 1980s. On the other hand, the human population has increased at a rate of more than 3% per annum. This has been associated with an escalating demand for food thus adding pressure on natural resources which are already threatened.There is therefore a need for technical innovations to increase output of livestock products to at least a level of self-sufficiency. This increase must be obtained in a manner which is consistent with protection of the environment and conservation of natural resources for sustainable performance.In this endeavour to develop technologies to support the livestock industries, developing countries of Africa are faced with inadequate resources to carry out research programmes, addressing a multiplicity of produciton problems. Towards this end, the donor countries have contributed resources including capital goods, technical assistance, operational finance for agreed projects and manpower development. However it is important that the national scientists are involved in the conceptualization, formulation and implementation of research programmes. The donor inputs are therefore expended in execution of projects generated by the local personnel. In this connexion, it is hoped that the level of support will be increased and sustained for a reasonable period to bear impact at the farm level. However it should be noted that research alone cannot bring about increase in output of meat and milk, etc. Rather the new technology from research is one of the inputs which must be mixed with other inputs to improve production.

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44

Chong, Yong Yin, and Mohammad Isa bin Ibrahim. "A Dedicated Production Support Vessel For Offshore Operations." Journal of Petroleum Technology 37, no. 10 (October 1, 1985): 1806–12. http://dx.doi.org/10.2118/12413-pa.

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45

Watson, T. G., I. Nelligan, and L. Lessing. "Further support for fed-batch production of cellulases." Biotechnology Letters 8, no. 4 (April 1986): I—II. http://dx.doi.org/10.1007/bf01030500.

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46

Davison, Elizabeth L., and Thomas S. Hansell. "Establishing documentary production support for 21st century campuses." Education and Information Technologies 19, no. 1 (May 29, 2012): 227–37. http://dx.doi.org/10.1007/s10639-012-9202-3.

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47

Modolo, Valéria A., and Cyro Paulino da Costa. "Production of paulista gherkin using trellis net support." Scientia Agricola 61, no. 1 (February 2004): 43–46. http://dx.doi.org/10.1590/s0103-90162004000100008.

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Paulista gherkin is a new gherkin type obtained by crossing Cucumis anguria var. anguria x C. anguria var. longaculeatus. It differs from common gherkin in its fruits and leaves. Elite lines of Paulista gherkin present similar performance for total yield and fruit weight but some have distinctive characteristics, including fruit and leaf attributes. The combination of these characteristics through production of Paulista gherkin hybrids could be an alternative for fruit quality and/or yield improvement. The purpose of the present work was to compare the yield of Paulista gherkin lines and their hybrids grown on a trellis net under field conditions. Four lines and their six single-cross hybrids were evaluated for yield and fruit production using the trellised net production system. Seedlings were produced in polystyrene trays with 128 cells, and transplanted to the middle of 1.20 m wide beds. Plants were trained without pruning on netting having 0.1 x 0.1 m openings. The experiment was conducted in a randomized block design with four replicates and seven plants per plot. Yield was expressed as number of fruits and total weight of fruits per plot. Length, width and fruit flesh thickness were also evaluated, with five samples per plot in two harvesting times. Hybrids and their parental lines were similar in fruit yield and quality. The trellised net was suitable for Paulista gherkin production and provided adequate support to the plants. The trellis technique is suitable to make harvesting easier and to improve fruit quality.

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48

Brandon, Peter, Martin Betts, and Hans Wamelink. "Information technology support to construction design and production." Computers in Industry 35, no. 1 (February 1998): 1–12. http://dx.doi.org/10.1016/s0166-3615(97)00080-8.

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49

Kubat, Cemalettin, Ercan Öztemel, and Harun Taşkιn. "Decision support systems in production planning and control." Production Planning & Control 18, no. 1 (January 2007): 1–2. http://dx.doi.org/10.1080/09537280600940572.

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50

Zudor, Elisabeta Ilie, László Monostori, and Ekaterina Kuzmina. "Constraint Programming Based Support for Production Networks Management." IFAC Proceedings Volumes 36, no. 3 (April 2003): 13–18. http://dx.doi.org/10.1016/s1474-6670(17)37728-5.

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Journal articles on the topic 'Production support'

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