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Статті в журналах з теми "Demand generalization"

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Wang, Chin-lih. "A generalization of an aggregate almost ideal demand system." Economics Letters 41, no. 4 (January 1993): 369–71. http://dx.doi.org/10.1016/0165-1765(93)90207-s.

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Kim, Jae-Dong, Tae-Hyeong Kim, and Sung Won Han. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks." Mathematics 11, no. 3 (January 17, 2023): 501. http://dx.doi.org/10.3390/math11030501.

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The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the Republic of Korea Army’s third generation main battle tanks. Data about the spare part consumption of 1,053,422 transactional data points stored in a military logistics management system were obtained. Demand forecasting classification models were developed to exploit machine learning, stacked generalization, and time series as baseline methods. Additionally, various stacked generalizations were established in spare part demand forecasting. The results demonstrated that a suitable selection of methods could help enhance the performance of the forecasting models in this domain.
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YAMAMOTO, Daisuke, Masaki MURASE, and Naohisa TAKAHASHI. "On-Demand Generalization of Road Networks Based on Facility Search Results." IEICE Transactions on Information and Systems E102.D, no. 1 (January 1, 2019): 93–103. http://dx.doi.org/10.1587/transinf.2017edp7405.

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Corbett, Charles J., and Kumar Rajaram. "A Generalization of the Inventory Pooling Effect to Nonnormal Dependent Demand." Manufacturing & Service Operations Management 8, no. 4 (October 2006): 351–58. http://dx.doi.org/10.1287/msom.1060.0117.

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Gould, Nicholas, and William Mackaness. "From taxonomies to ontologies: formalizing generalization knowledge for on-demand mapping." Cartography and Geographic Information Science 43, no. 3 (August 18, 2015): 208–22. http://dx.doi.org/10.1080/15230406.2015.1072737.

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Zhao, Wenting, Juanjuan Zhao, Xilong Yao, Zhixin Jin, and Pan Wang. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand." Energies 12, no. 7 (April 8, 2019): 1347. http://dx.doi.org/10.3390/en12071347.

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Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.
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Chen, Shih-Shen, Ken Hung, and Chien-Shu Tsai. "On Joan Robinson’s Output Symmetry Theorems with Various Taxations under Third-Degree Price Discrimination: A Generalization." Symmetry 14, no. 5 (May 7, 2022): 959. http://dx.doi.org/10.3390/sym14050959.

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This paper incorporates more general cases with a new class of constantly adjusted concavity demand curves and includes three types of taxes. To verify the output symmetry under various forms of taxation, we simulate both linear and constant elasticity demand functions under the unit, demand ad valorem, and cost ad valorem taxes. If all the demand functions in the submarkets are linear, the total outputs are identical under both uniform pricing and third-degree price discrimination. Furthermore, if all the weak market demand curves are strictly “Robinson-concave” and all the strong market demand curves are strictly “Robinson-convex” or linear, then the total output under price discrimination exceeds that under uniform pricing, and vice versa. While different taxes lead to higher costs, the cost pass-through changes the prices of the products, and the change of total output still depends on the curvature of the demand curve. Therefore, the curvature of the demand curve remains the main determinant of changes in output. Our study provides a theoretical basis for market intervention in price discrimination.
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ABE, Yoshihisa. "Prerequisites for Shaping the Demand Behavior of Writing Messages and Facilitating Generalization." Japanese Journal of Special Education 27, no. 2 (1989): 49–55. http://dx.doi.org/10.6033/tokkyou.27.49.

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Storchevoy, M. "The Economic Theory of the Firm: A Generalization." Voprosy Ekonomiki, no. 9 (September 20, 2012): 41–66. http://dx.doi.org/10.32609/0042-8736-2012-9-41-66.

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The paper deals with development of a general theory of the firm. It discusses the demand for such a theory, reviews existing approaches to its generalization, and offers a new variant of general theory of the firm based on the contract theory. The theory is based on minimization of opportunistic behaviour determined by the material structure of production (a classification of ten structural factors is offered). This framework is applied to the analysis of three boundaries problems (boundaries of the job, boundaries of the unit, boundaries of the firm) and five integration dilemmas (vertical, horizontal, functional, related, and conglomerate).
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Gadzalo, Iaroslav, Mykola Sychevskiy, Olha Kovalenko, Liudmyla Deineko, and Lyudmila Yashchenko. "Assessment of global food demand in unexpected situations." Innovative Marketing 16, no. 4 (December 18, 2020): 91–103. http://dx.doi.org/10.21511/im.16(4).2020.08.

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The methodological approach for assessing the formation of food demand in unforeseen situations using digital Internet-technologies and the assessment itself, is substantiated in the paper (in the context of the COVID-19 pandemic of 2020). Comparison and theoretical generalization, as well as statistical test-assessment of hypotheses and structural regularities based on the data of Google Trends Internet platform, is used to analyze consumer preferences and intensity of demand changes for meat, milk, sugar, bread, and flour during the pandemic and quarantine, both in developed and developing countries. It is discovered that the biggest changes can be observed in the developed countries: consumer preferences shifted from rather expensive food products (milk and meat) to much cheaper ones (flour and bread). It is asserted that a decrease in consumer demand for basic food products will have a negative impact on the global economy. In 2020, a considerable decrease in GDP is expected for the developed countries; in the developing countries, GDP decline will not be as large, but prices are expected to rise much more noticeably. The following anti-crisis measures are proposed: support of the most vulnerable population and increase of food accessibility; temporary reduction of the VAT and other taxes influencing the price of food; reduction of central banks’ lending rates, etc. With the correct measures applied, the stabilization of consumer demand for food and gradual growth of the global economy is expected by the end of 2021.
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Дисертації з теми "Demand generalization"

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Datta, Krupa R. "Generalization of Hitting, Covering and Packing Problems on Intervals." Thesis, 2017. http://etd.iisc.ernet.in/2005/3628.

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Interval graphs are well studied structures. Intervals can represent resources like jobs to be sched-uled. Finding maximum independent set in interval graphs would correspond to scheduling maximum number of non-conflicting jobs on the computer. Most optimization problems on interval graphs like independent set, vertex cover, dominating set, maximum clique, etc can be solved efficiently using combinatorial algorithms in polynomial time. Hitting, Covering and Packing problems have been ex-tensively studied in the last few decades and have applications in diverse areas. While they are NP-hard for most settings, they are polynomial solvable for intervals. In this thesis, we consider the generaliza-tions of hitting, covering and packing problems for intervals. We model these problems as min-cost flow problems using non-trivial reduction and solve it using standard flow algorithms. Demand-hitting problem which is a generalization of hitting problem is defined as follows: Given N intervals, a positive integer demand for every interval, M points, a real weight for every point, select a subset of points H, such that every interval contains at least as many points in H as its demand and sum of weight of the points in H is minimized. Note that if the demand is one for all intervals, we get the standard hitting set problem. In this case, we give a dynamic programming based O(M + N) time algorithm assuming that intervals and points are sorted. A special case of the demand-hitting set is the K-hitting set problem where the demand of all the intervals is K. For the K-hitting set problem, we give a O(M2N) time flow based algorithm. For the demand-hitting problem, we make an assumption that no interval is contained in another interval. Under this assumption, we give a O(M2N) time flow based algorithm. Demand-covering problem which is a generalization of covering problem is defined as follows: Given N intervals, a real weight for every interval, M points, a positive integer demand for every point, select a subset of intervals C, such that every point is contained in at least as many intervals in C as its demand and sum of weight of the intervals in C is minimized. Note that if the demand of points are one, we get the standard covering set problem. In this case, we give a dynamic programming based O(M + N log N) time algorithm assuming that points are sorted. A special case of the demand-covering set is the K-covering set problem where the demand of all the points is K. For the K-covering set problem, we give a O(MN2) time flow based algorithm. For the demand-covering problem, we give a O(MN2) time flow based algorithm. K-pack points problem which is a generalization of packing problem is defined as follows: Given N intervals, an integer K, M points, a real weight for every point, select a subset of points Y , such that every interval contains at most K points from Y and sum of weight of the points in Y is maximized. Note that if K is one, we get the standard pack points problem. In this case, we give a dynamic pro-gramming based O(M + N) time algorithm assuming that points and intervals are sorted. For K-pack points problem, we give O(M2 log M) time flow based algorithm assuming that intervals and points are sorted.
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Книги з теми "Demand generalization"

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Web-based architecture for on-demand maps-integrating meaningful generalization processing. Enschede: ITC, 2009.

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Biba, Anna, and Galina Razumova. Teaching Russian and developing meta-subject skills of students in inclusive primary education classes. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1871394.

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The monograph attempts to identify and substantiate the content and complex of spelling teaching tools for younger schoolchildren with a pronounced general underdevelopment of speech with the simultaneous development of their academic independence. To solve the problem, both theoretical research methods (analysis of psychological and pedagogical literature, generalization of scientific data) and empirical (observation, conversation, testing, analysis of student activity products) were used. The theoretical basis of teaching and development of students with mild speech underdevelopment in inclusive education classes is information about the cognitive, regulatory and speech features of this category of children; ideas of the development of younger schoolchildren in the learning process; the provisions of linguistics and psycholinguistics on the structure of spelling skills and the patterns of its formation, including in children with speech pathologies. It can be in demand in the professional training of future primary school teachers, in the practice of primary school teachers of inclusive education and speech therapists who assist primary school students with speech underdevelopment in mastering the school curriculum.
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Carrillo, M. J. Generalizations of Palm's theorem and Dyna-METRIC's demand and pipeline variability. Rand Corporation, 1989.

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Hughes, Aaron W. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190684464.003.0001.

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This chapter introduces Shared Identities: Medieval and Modern Imaginings of Judeo-Islam. It makes the case that the study of Muslim and Jewish relations is ultimately an issue of comparison and, as a result, ought to be illumined by the field of religious studies. Despite this, and perhaps paradoxically, there are numerous tensions inherent to the particularism of specific subfields and the generalization demanded by the larger field of religious studies. The chapter then examines the regnant paradigm used to describe Jewish–Muslim relations in the premodern period, that of symbiosis, and signals how the study that follows attempts to undermine said paradigm with an eye to rewriting the history of the early interactions between Jews and Arabs.
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Частини книг з теми "Demand generalization"

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Acosta-Elias, Jesús, and Leandro Navarro-Moldes. "Generalization of the Fast Consistency Algorithm to a Grid with Multiple High Demand Zones." In Lecture Notes in Computer Science, 275–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44862-4_30.

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Murase, Masaki, Daisuke Yamamoto, and Naohisa Takahashi. "On-Demand Generalization of Guide Maps with Road Networks and Category-Based Web Search Results." In Web and Wireless Geographical Information Systems, 53–70. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18251-3_4.

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Holzinger, Andreas, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek. "xxAI - Beyond Explainable Artificial Intelligence." In xxAI - Beyond Explainable AI, 3–10. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_1.

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AbstractThe success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”
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Galichon, Alfred. "Transportation on Networks." In Optimal Transport Methods in Economics. Princeton University Press, 2016. http://dx.doi.org/10.23943/princeton/9780691172767.003.0008.

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This chapter considers the optimal network flow problem, which is a generalization of the optimal assignment problem considered in Chapter 3. In optimal flow problems, one considers a network of cities, or edges, to move a distribution of mass on supply nodes to a distribution of mass on demand nodes. The difference from a standard optimal assignment problem is that the matching surplus associated with moving from a supply location to a demand location is not necessarily directly defined; instead, there are several paths from the supply location to the demand location, among these some yield maximal surplus. Therefore, both the optimal assignment problem and the shortest path problem are instances of the optimal flow problem; these instances are representative in the sense that any optimal flow problem may be decomposed into an assignment problem and a number of shortest path problems. The chapter shows how to easily compute these problems using linear programming.
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Pratihar, Jayanta, Ranjan Kumar, Arindam Dey, and Said Broumi. "Transportation Problem in Neutrosophic Environment." In Advances in Data Mining and Database Management, 180–212. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1313-2.ch007.

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The transportation problem (TP) is popular in operation research due to its versatile applications in real life. Uncertainty exists in most of the real-life problems, which cause it laborious to find the cost (supply/demand) exactly. The fuzzy set is the well-known field for handling the uncertainty but has some limitations. For that reason, in this chapter introduces another set of values called neutrosophic set. It is a generalization of crisp sets, fuzzy set, and intuitionistic fuzzy set, which is handle the uncertain, unpredictable, and insufficient information in real-life problem. Here consider some neutrosophic sets of values for supply, demand, and cell cost. In this chapter, extension of linear programming principle, extension of north west principle, extension of Vogel's approximation method (VAM) principle, and extended principle of MODI method are used for solving the TP with neutrosophic environment called neutrosophic transportation problem (NTP), and these methods are compared using neutrosophic sets of value as well as a combination of neutrosophic and crisp value for analyzing the every real-life uncertain situation.
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Li, Lei, Min Feng, Lianwen Jin, Shenjin Chen, Lihong Ma, and Jiakai Gao. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing." In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing, 1158–76. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5339-8.ch055.

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Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.
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Taneja, Abhishek. "Enhancing Web Data Mining." In Advances in Data Mining and Database Management, 116–36. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch005.

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An enormous production of databases in almost every area of human endeavor particularly through web has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. The aim of this study is to study the predictive ability of Factor Analysis a web mining technique to prevent voting, averaging, stack generalization, meta- learning and thus saving much of our time in choosing the right technique for right kind of underlying dataset. This chapter compares the three factor based techniques viz. principal component regression (PCR), Generalized Least Square (GLS) Regression, and Maximum Likelihood Regression (MLR) method and explores their predictive ability on theoretical as well as on experimental basis. All the three factor based techniques have been compared using the necessary conditions for forecasting like R-Square, Adjusted R-Square, F-Test, JB (Jarque-Bera) test of normality. This study can be further explored and enhanced using sufficient conditions for forecasting like Theil's Inequality coefficient (TIC), and Janur Quotient (JQ).
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Baig, Aysha Karamat, and Uzma Karamat Baig. "Halal Cosmetics." In Advances in Electronic Government, Digital Divide, and Regional Development, 286–97. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4639-1.ch022.

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The chapter shows the intention of Pakistani consumers in choosing Halal cosmetic products. Their perceived ideologies about Halal regarding cosmetics are shown as well. The data was congregated through a self-administrated survey based upon judgmental sampling. A total of 500 respondents from two large cities of Pakistan (Lahore and Faisalabad) were involved in this study to determine the awareness about Halal Cosmetics and the extent to which cosmetic brands’ promotional activities had influenced the respondents’ preferences. The results of this study expressed the beguiling demand for Halal cosmetic products was predominantly influenced by Halal logo. Therefore, Pakistani government should give attention to devising and implementing the Halal logo policy, while ensuring that the cosmetic industry provides only those cosmetic products that meet Halal requirements. A limitation to be considered is the generalization of the results. Despite the fact that Lahore and Faisalabad are two of three most populated cities in Pakistan and include almost five percent of the country’s citizens, the results cannot be expected to explain the overall behavior of Pakistani consumers toward Halal cosmetic products. This study is the first one to look into the level of understanding about Halal Cosmetics among Pakistani consumers.
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Daniel, David K., and Vikramaditya Bhandari. "Neural Network Model to Estimate and Predict Cell Mass Concentration in Lipase Fermentation." In Advances in Secure Computing, Internet Services, and Applications, 303–16. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4940-8.ch015.

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Lipase is an industrially important enzyme with major use in food industries. The demand of lipase is increasing every year. An online prediction of cell mass concentration is of great value in real time process involving the production of lipase. In the current work, the use of a back-propagation multilayer neural network to predict cell mass during lipase production by Rhizopus delemar NRRL 1472 is targeted. Network training data with respect to time is generated by carrying out experiments in laboratory. The fungus is grown in erlenmeyer flasks at initial pH of 5.6, temperature of 30ºC, and at 150 rpm. During the experiments, readings for cell mass growth are collected in specific period of time. By the training data, an artificial neural network model programmed in MATLAB for Windows is trained and used for prediction of cell mass. The Levenberg-Marquardt algorithm with back-propagation is used in the network to get the optimized weights. The optimum network configuration with different activation function and the number of nodes in the hidden layer are identified by trial and error method. Sigmoid unipolar activation function is 2-5-1, whereas logarithmoid and sigmoid bipolar is 2-3-1. These are chosen according to the values of Sum of Square of Errors (SSE), Root Mean Square (RMS) training and testing. The sigmoid unipolar activation function gives a good fit for estimated value with network configuration 2-5-1, which could be used for generalization.
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Rampton, Martha. "Demons of the Lower Air." In Trafficking with Demons, 63–84. Cornell University Press, 2019. http://dx.doi.org/10.7591/cornell/9781501702686.003.0003.

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This chapter considers demons and their place in late classical and early medieval cosmologies. This discussion is not sequential or linear, nor does it focus on texts in situ. Rather, it accommodates a high level of generalization and acts as a large canopy covering material from across a broad spectrum of time and space in order to clarify terms and assumptions that informed an understanding of magic throughout the first millennium. In the Christian mental universe, demons were at the very center of all magic, so much so that magic itself equated to traffic with demons. Moreover, any active involvement with demons—from blatantly honoring pagan idols, to eating meat cooked at pagan shrines, to casting spells—was magic, even if the persons involved were not intentionally trafficking.
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Тези доповідей конференцій з теми "Demand generalization"

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Weihua, Dong. "Generating On-Demand Web Mapping through Progressive Generalization." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.234.

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Tugay, Resul, and Şule Gündüz Öğüdücü. "Demand Prediction using Machine Learning Methods and Stacked Generalization." In 6th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006431602160222.

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Subroto, Cynthia Athena Mahadewi, and Saiful Akbar. "The Effect of Preprocessing Techniques on Stacked Generalization and Stand-Alone Method for E-commerce Demand Prediction." In 2022 9th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA). IEEE, 2022. http://dx.doi.org/10.1109/icaicta56449.2022.9932926.

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Simpson, Z., N. Janse van Rensburg, and M. van Ryneveld. "Developing Students as Higher-Order Thinkers: Analyzing Student Performance Against Levels of Cognitive Demand in a Material Science Course." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37652.

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Today’s increasingly complex engineering workplace demands skill in evaluation, reasoning and critical thinking; however, engineering curricula often test lower-order learning at the expense of higher-order reasoning. This paper analyzes the level of cognitive demand in a course on Material Science in the Department of Mechanical Engineering Science at the University of Johannesburg, South Africa. This is done by applying Biggs’ SOLO taxonomy to classify test and exam questions in the course and then analyzing student performance against this taxonomy of higher- and lower-order learning. The results demonstrate that many students battle with questions that require extended abstract reasoning (argument, evaluation, hypothesizing and generalization). Similarly, relational thinking (through comparison, contrast, application and so on) proves to be a significant problem for weaker students. The paper recommends that engineering lecturers build higher-order thinking into course outcomes, teaching and assessment and that engineering qualifications work systematically towards developing students as higher-order thinkers.
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Chen, Yuxuan, and Patrick Phelan. "Predicting Peak Energy Demand for an Office Building Using Artificial Intelligence (AI) Approaches." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-64492.

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Abstract Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.
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Salih, Bilal S., and Tuhin K. Das. "Placing Minimum Phase Zeros to Shape Transient Response: Generalization From Control of Hybrid Power Systems." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5234.

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Conservation of energy can be applied in designing control of hybrid power systems to manage power demand and supply. In practice, it can be used for designing decentralized controllers. In this paper, this idea is analyzed in a generalized theoretical framework. The problem is transformed to that of using minimum phase zeros to generate a specific type of transient response admitted by dynamical systems. Here, the transient step response is shaped using an underlying conservation principle. In this paper, emphasis is placed on second order systems. However, the analysis can be extended to higher order transfer functions. Analytical results relating zero location to the matched/ mismatched areas of the transient response are established for a class of second order systems. A combination of feedback and feedforward actions are shown to achieve the desired zero placement/addition and the desired transient response. The proposed analysis promises extension to nonlinear systems. Optimization studies also seem appropriate, especially for higher order transfer functions.
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Vinichenko, Victoria А., and Yulija А. Masalova. "The demand for the quality of human resources in the context of changing generational groups." In Sustainable and Innovative Development in the Global Digital Age. Dela Press Publishing House, 2022. http://dx.doi.org/10.56199/dpcsebm.juft9241.

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The purpose of the study: to substantiate the factors and constraints that determine the request for human resource characteristics by employers. Data analyses, generalization, synthesis, method of age shifting were used as methods of research. A number of prerequisites that change the duration of the period of labor activity are analyzed: reform of the education system and the pension system of the Russian Federation; socio-economic transformations; sanctions restrictions; specificity of generations, etc. The considered prerequisites determine the presence of factors influencing the peculiarities of the processes in human resource management of the organization. It is hypothesized that internal factors are controllable and have a greater influence on the choice of human resource policy in the context of digital transformation. The professional and qualification structure of generational groups is presented in the nexus of generational shifts in the labor market. The results of the study suggest that sanctions will have a negative impact on the employment conditions of workers. There are gaps between the professional training of specialists and the demands of employers, who create alternative forms of training to meet their needs. The novelty of the study lies in the analysis of the current situation in the Russian labor market and the conclusions about the need to provide new formats of interaction between universities and business.
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Vorum, Martin. "An Energy Appetite of U.S. Water Systems: How Much Energy Does It Take to Supply Our Water?" In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65991.

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This paper reports a new method to estimate energy used in water supply and return phases of the water cycle. An example is a composite case for energy used in water systems of the United States (U.S.) using data from the U.S. Geological Survey (USGS). The new method is innovatively simple: it avoids complexities of data-intensive, bottom-up models, often used to map water networks. This method helps to prioritize how to improve energy efficiency in water systems. The method uses a top-down approach requiring limited system data, and relying on a transparent computation of energy. The method applies to standalone or composite systems. It applies to small-scale, single-user systems, to large water supply/return networks, or to summations of data for classes of systems whether simple or complex, and whether interconnected or not. The reported example shows transport could account for about 95 percent of U.S. water systems’ energy use. Energy use for transport among different water-sectors ranged from roughly 80 percent to nearly 100 percent of total sectoral energy demand. Contrasting that generalization, specific data for standalone systems would show when energy demand depends, for example, more on treatment needed to meet quality standards.
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Zhao, Chengshuai, Shuai Liu, Feng Huang, Shichao Liu, and Wen Zhang. "CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/517.

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Molecular interactions are significant resources for analyzing sophisticated biological systems. Identification of multifarious molecular interactions attracts increasing attention in biomedicine, bioinformatics, and human healthcare communities. Recently, a plethora of methods have been proposed to reveal molecular interactions in one specific domain. However, existing methods heavily rely on features or structures involving molecules, which limits the capacity of transferring the models to other tasks. Therefore, generalized models for the multifarious molecular interaction prediction (MIP) are in demand. In this paper, we propose a contrastive self-supervised graph neural network (CSGNN) to predict molecular interactions. CSGNN injects a mix-hop neighborhood aggregator into a graph neural network (GNN) to capture high-order dependency in the molecular interaction networks and leverages a contrastive self-supervised learning task as a regularizer within a multi-task learning paradigm to enhance the generalization ability. Experiments on seven molecular interaction networks show that CSGNN outperforms classic and state-of-the-art models. Comprehensive experiments indicate that the mix-hop aggregator and the self-supervised regularizer can effectively facilitate the link inference in multifarious molecular networks.
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ATKOČIŪNIENĖ, Vilma, and Ilona KIAUŠIENĖ. "THE MODEL OF INTEGRATIVE MANAGEMENT OF RURAL SOCIAL INFRASTRUCTURE DEVELOPMENT." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.228.

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One of the most difficult problems encountered by social infrastructure development management in various countries of economic development is the search for RSI management interactions at the national, regional and local (municipal, neighborhood) levels. Traditional solutions to RSI development do not create opportunities for the coherence, viability and resilience of rural development. This article describes integrative management of rural social infrastructure (RSI), provides the analysis of RSI management processes and explanation of “triple bottom line”, determination of main principles required in order to achieve sustained development of the region. The main research objective, namely, creation of an integrative rural social infrastructure management model reached. The integrative RSI management conception based on four- tier governance cycle “plan-do-check-act” and internal governance functions. The functions RSI management are determination of consumer demand for RSI services and strategic development goals; planning of RSI services, means and results; organization of RSI services supply; horizontal and vertical coordination of RSI activities; assessment of RSI services consumers’ opinion and community sustainability; supervision and evaluation of RSI activities. The main research methods were used: analysis and generalization of scientific literature, logical and systematical reasoning, graphic presentation of comparison, abstracts and other methods.
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Звіти організацій з теми "Demand generalization"

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Нечипуренко, Павло Павлович, Тетяна Валеріївна Старова, Тетяна Валеріївна Селіванова, Анна Олександрівна Томіліна, and Олександр Давидович Учитель. Use of Augmented Reality in Chemistry Education. CEUR-WS.org, November 2018. http://dx.doi.org/10.31812/123456789/2658.

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The purpose of this article is to analyze the current trends in the use of the augmented reality in the chemistry education and to identify the promising areas for the introduction of AR-technologies to support the chemistry education in Ukrainian educational institutions. The article is aimed at solving such problems as: the generalization and analysis of the scientific researches results on the use of the augmented reality in the chemistry education, the characteristics of the modern AR-tools in the chemistry education and the forecasting of some possible areas of the development and improvement of the Ukrainian tools of the augmented reality in the chemistry education. The object of research is the augmented reality, and the subject is the use of the augmented reality in the chemistry learning. As a result of the study, it has been found that AR-technologies are actively used in the chemistry education and their effectiveness has been proven, but there are still no Ukrainian software products in this field. Frequently AR-technologies of the chemistry education are used for 3D visualization of the structure of atoms, molecules, crystalline lattices. The study has made it possible to conclude that there is a significant demand for the chemistry education with the augmented reality that is available via the mobile devices, and accordingly the need to develop the appropriate tools to support the chemistry education at schools and universities. The most promising thing is the development of methodological recommendations for the implementation of laboratory works, textbooks, popular scientific literature on chemistry with the use of the augmented reality technologies and the creation of the simulators for working with the chemical equipment and utensils using the augmented reality.
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Morkun, Volodymyr S., Сергій Олексійович Семеріков, Svitlana M. Hryshchenko, and Kateryna I. Slovak. System of competencies for mining engineers. Видавництво “CSITA”, 2016. http://dx.doi.org/10.31812/0564/719.

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Topicality of the material, highlighted in this article is stipulated by the need to ensure effectiveness of educational process while preparing mining engineers. System of competencies for future mining engineers, taken as basis for high school sectoral standard for Mining 6.050301 update is theoretically substantiated and developed. Sources of state-of-the-art foreign educational system and technologies as well as scientific research results of local teachers have been analyzed, enabling development of new sectoral standard. Switching to new high school competencies-based sectoral standards is the necessary step in high education reforming in Ukraine, while the application of competencies-based approach to high school sectoral standards development facilitates tuning of education towards labour market’s requirements and demands, further development of educational techniques and educational system as a whole. Objective of the article: to project system of competencies and to define components of environmental competencies for mining engineers. Methods: – theoretical: analysis, generalization, systematization of legislative framework, educational standards, Internet - sources in order to distinguish theoretical basis of research, develop system of competencies for future mining engineers. – Empirical – improvement of system of competencies for future mining engineers. Scientific novelty is represented with structured system, consisting of 49 competencies, comprising the core of new sectoral standard for mining engineers preparation; Practical importance of the outcomes is related to developments: separate constituents of high school draft sectoral standard for Mining engineers bachelors’ preparation 6.050301 Mining (system of social & personal, general scientific, tool-based, general professional and special professional competencies. Research outcomes can be used while developing educational qualification profile and training program for Mining bachelors 6.050301 education field, in course of geoinformational technologies review by ecology, land survey and geography bachelors.
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