Journal articles: 'Demand generalization'

1

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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Gao, Wei, and Yun Gang Zhang. "Generalization Bounds for Certain Class of Ranking Algorithm." Advanced Materials Research 267 (June 2011): 456–61. http://dx.doi.org/10.4028/www.scientific.net/amr.267.456.

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The quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on a ranking setting which uses truth function to label each pair of instances and the ranking preferences are given randomly from some distributions on the set of possible undirected edge sets of a graph. The contribution of this paper is the given generalization bounds for such ranking algorithm via strong and weak stability. Such stabilities have lower demand than uniform stability and fit for more real applications.

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Madden, Paul. "A Generalization of Hicksian q Substitutes and Complements with Application to Demand Rationing." Econometrica 59, no. 5 (September 1991): 1497. http://dx.doi.org/10.2307/2938377.

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Haritchabalet, Carole. "The Production of Goods in Excess of Demand: A Generalization of Self-Protection." Geneva Papers on Risk and Insurance Theory 25, no. 1 (June 2000): 51–63. http://dx.doi.org/10.1023/a:1008797407679.

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14

Xue, Xiang Hong, Xiao Feng Xue, and Lei Xu. "Study on Improved PCA-SVM Model for Water Demand Prediction." Advanced Materials Research 591-593 (November 2012): 1320–24. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1320.

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construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this model is higher than SVM, BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.

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Kravchuk, Oksana, Iryna Varis, and Tetyana Bidna. "Demand of HR-competency in Ukraine: changes and challenges at the labor market under pandemic COVID-19." Social and labour relations: theory and practice 11, no. 1 (August 31, 2021): 14–30. http://dx.doi.org/10.21511/slrtp.11(1).2021.02.

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The experience of the last year of business operation has highlighted the need to find new ways and approaches to managing people, through the crisis caused by the COVID-19 coronavirus pandemic. New socio-economic reality, digitalization and socialization are transforming the people management processes of, which, in turn, changes the modern requirements for HR competencies. The article’s purpose is to form a model of personnel manager competencies by clusters of general and professional competencies based on the implementation of best international experience in forming and developing HR competencies models and considering demand trends in the Ukrainian labor market in the segment «HR Management» in modern realities. To produce results, the following methods were used: abstract-logical to generalize the scientific literature; monographic and analogies to summarize the international experience of forming HR-competencies models; empirical for the analysis of trends in the formation of demand for HR-competencies in the Ukrainian labor market; modeling to develop a modern HR competencies model for Ukraine; generalizations to draw conclusions. The generalization of the current state of scientific research and international experience in modeling HR-competencies allowed to structure the current HR-competencies of SHRM, HRCI, CIPD and to develop proposals for their implementation in Ukrainian HR-practice. The analysis of the demand for HR-competence in Ukraine allowed to select the most popular of them by general, managerial, and special clusters; to identify the frequency of demand for HR-competences and calculate their weight level. On this basis, the structure of demand in the labor market of Ukraine in the segment of «HR-Management» was generalized. The influence of changes and challenges of the organization’s activity during the pandemic COVID-19 on the transformation of the roles of people management and the current HR competencies model in Ukraine is substantiated. Prospective directions of using the model for professional standard development, educational services improvement, and development of the national system of HR-certification for making better the quality and performance of the human resource management function in Ukraine are proposed.

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Han, Long Xi, Fen Fen Yan, Hui Peng, Jun Jie Gao, and Man Man Pan. "Methods for Calculation of Water Environment Capacity of Small and Medium River Channels." Advanced Materials Research 610-613 (December 2012): 2745–50. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.2745.

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Methods for calculation of water environment capacity can be divided into two types in general, one type is ideal water environment capacity method and the other is pollution source generalization method. This paper proposes another way of generalization, uniform generalization method, assuming that distribution of the amount of pollutants discharged is uniform in the lengthways direction. In consideration of the practical demand for administration of water resources protection, a formula for calculation of water environment capacity for different combinations of environment function regions is presented，based on the water quality targets of function regions, with the numerical or analytical methods of 1-D water quality model. The proposed method provides a simple and effective method for water resources management and planning.

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Yan, Kun, and Min-Zhi Yang. "Water demand forecast model of Least Squares Support Vector Machine based on Particle Swarm Optimization." MATEC Web of Conferences 246 (2018): 01029. http://dx.doi.org/10.1051/matecconf/201824601029.

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In order to solve the problem of precision of water demand forecast model, a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper. A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai, Jiangsu Province, and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%. The results showed that the model had good simulation effect and strong generalization performance, and can be widely used to solve the problem of small- sample, nonlinear and high dimensional water demand forecast.

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Lu, Zhanhui, Mengfan Ji, Weijuan Wang, and Gengyin Li. "Stability of the Stochastic Model for Power Markets with Interval Parameters." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6287458.

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Pertaining to the random nature of demand sides and the range of demand elasticity with suppliers and consumers, a stochastic model for power markets with interval parameters is described to illustrate uncertain external disturbances, which is a generalization of the Alvarado dynamic model, stochastic model, and interval model. The interval stochastic stability criteria of the provided model are investigated by the theory of economics, interval dynamical system, and the theory stability of stochastic differential equations. The conclusions indicate that the demand elasticity stable interval can be calculated and the random excitation intensity does not impact the system stability. Some numerical examples are given to show the applicability and validity of the obtained results from a statistical perspective.

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Aksoy, Asli, Nursel Öztürk, and Eric Sucky. "Demand forecasting for apparel manufacturers by using neuro-fuzzy techniques." Journal of Modelling in Management 9, no. 1 (March 11, 2014): 18–35. http://dx.doi.org/10.1108/jm2-10-2011-0045.

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Purpose – According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers. Design/methodology/approach – The ANFIS is used forecasting the demand for apparel manufacturers. Findings – The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply. Originality/value – ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

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Liu, Xin, Xuefeng Sang, Jiaxuan Chang, Yang Zheng, and Yuping Han. "Water demand prediction optimization method in Shenzhen based on the zero-sum game model and rolling revisions." Water Policy 23, no. 6 (October 1, 2021): 1506–29. http://dx.doi.org/10.2166/wp.2021.046.

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Abstract In this study, a deep learning model based on zero-sum game (ZSG) was proposed for accurate water demand prediction. The ensemble learning was introduced to enhance the generalization ability of models, and the sliding average was designed to solve the non-stationarity problem of time series. To solve the problem that the deep learning model could not predict water supply fluctuations caused by emergencies, a hypothesis testing method combining Student's t-test and discrete wavelet transform was proposed to generate the envelope interval of the predicted values to carry out rolling revisions. The research methods were applied to Shenzhen, a megacity with extremely short water resources. The research results showed that the regular bidirectional models were superior to the unidirectional model, and the ZSG-based bidirectional models were superior to the regular bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and ZSG could better guide the model to find the optimal solution. The fluctuations in water supply were mainly caused by the floating population, but the fluctuation was still within the envelope interval of the predicted values. The predicted values after rolling revisions were very close to the measured values.

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STAF'EVA, Ol'ga V. "Demand for leaders of a new type under present-day conditions." National Interests: Priorities and Security 18, no. 7 (July 14, 2022): 1401–16. http://dx.doi.org/10.24891/ni.18.7.1401.

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Subject. This article discusses the importance of leadership attributes at a new stage of economic development. Objectives. The article aims to investigate the factors influencing gender inequality in governance, and the attributes of transformational leadership of a new type necessary for the development of an innovation-driven economy. Methods. For the study, I used a comparative analysis and generalization. Results. The article describes the factors that prevent women from progressing up the career ladder, and finds that Russian society is largely exposed to gender stereotypes with the exception of the younger generation. Conclusions. The new challenges emphasize the importance of equal access to leadership positions for men and women, when the criteria of professionalism and the availability of competencies necessary to solve urgent problems play a crucial role.

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Xu, Yan, Ge Li, Lili Mou, and Yangyang Lu. "Learning Non-Taxonomic Relations on Demand for Ontology Extension." International Journal of Software Engineering and Knowledge Engineering 24, no. 08 (October 2014): 1159–75. http://dx.doi.org/10.1142/s0218194014400099.

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Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

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Meng, Ming, Dong Xiao Niu, Wei Sun, and Wei Shang. "Research on Monthly Electric Energy Demand Forecasting under the Influence of Two Calendars." Applied Mechanics and Materials 20-23 (January 2010): 963–68. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.963.

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Monthly electric energy demand forecasting plays an important role for the running of power system. China has two tow calendars and they works at the same time. Holidays designed by the lunar calendar affect the regularity of monthly electric load recorded only by the Gregorian one. The normal fuzzy transform is advanced here to quantitatively describe the impact of the Spring Festival and further divided the influence into Jan. and Feb. After excluding the influence, the amended historical data are adopted to training RBF neural network. Experiment results show that because the regularity of raw data is improved, the generalization ability and forecasting precise of RBF neural network are improved.

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Zhou, Yajun, Lilei Wang, Rong Zhong, and Yulong Tan. "A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/8028714.

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Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

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Azimjon, S., P. K. Gupta, and R. S. G. S. Sukhmani. "Comparative Study of Algorithms for Automated Generalization of Linear Objects." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 159–63. http://dx.doi.org/10.5194/isprsannals-ii-8-159-2014.

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Automated generalization, rooted from conventional cartography, has become an increasing concern in both geographic information system (GIS) and mapping fields. All geographic phenomenon and the processes are bound to the scale, as it is impossible for human being to observe the Earth and the processes in it without decreasing its scale. To get optimal results, cartographers and map-making agencies develop set of rules and constraints, however these rules are under consideration and topic for many researches up until recent days. Reducing map generating time and giving objectivity is possible by developing automated map generalization algorithms (McMaster and Shea, 1988). Modification of the scale traditionally is a manual process, which requires knowledge of the expert cartographer, and it depends on the experience of the user, which makes the process very subjective as every user may generate different map with same requirements. However, automating generalization based on the cartographic rules and constrains can give consistent result. Also, developing automated system for map generation is the demand of this rapid changing world. The research that we have conveyed considers only generalization of the roads, as it is one of the indispensable parts of a map. Dehradun city, Uttarakhand state of India was selected as a study area. The study carried out comparative study of the generalization software sets, operations and algorithms available currently, also considers advantages and drawbacks of the existing software used worldwide. Research concludes with the development of road network generalization tool and with the final generalized road map of the study area, which explores the use of open source python programming language and attempts to compare different road network generalization algorithms. Thus, the paper discusses the alternative solutions for automated generalization of linear objects using GIS-technologies. Research made on automated of road network generalization, which is summarized in this paper, was conducted in IIRS, Dehradun, India.

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Cheng, Y., Y. Yin, C. M. Li, W. Wu, P. P. Guo, X. L. Ma, and F. M. Hu. "A MODEL STUDY OF SMALL-SCALE WORLD MAP GENERALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 223–28. http://dx.doi.org/10.5194/isprs-archives-xlii-3-223-2018.

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With the globalization and rapid development every filed is taking an increasing interest in physical geography and human economics. There is a surging demand for small scale world map in large formats all over the world. Further study of automated mapping technology, especially the realization of small scale production on a large scale global map, is the key of the cartographic field need to solve. In light of this, this paper adopts the improved model (with the map and data separated) in the field of the mapmaking generalization, which can separate geographic data from mapping data from maps, mainly including cross-platform symbols and automatic map-making knowledge engine. With respect to the cross-platform symbol library, the symbol and the physical symbol in the geographic information are configured at all scale levels. With respect to automatic map-making knowledge engine consists 97 types, 1086 subtypes, 21845 basic algorithm and over 2500 relevant functional modules.In order to evaluate the accuracy and visual effect of our model towards topographic maps and thematic maps, we take the world map generalization in small scale as an example. After mapping generalization process, combining and simplifying the scattered islands make the map more explicit at 1&thinsp;:&thinsp;2.1 billion scale, and the map features more complete and accurate. Not only it enhance the map generalization of various scales significantly, but achieve the integration among map-makings of various scales, suggesting that this model provide a reference in cartographic generalization for various scales.

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Tsyganov, Alexander A., Sergey V. Brovchak, Yulia А. Gorelova, Lyubov S. Krutova, and Marina A. Selivanova. "Improving the Effectiveness and Demand for Educational Programs in Financial Technologies." SHS Web of Conferences 79 (2020): 02027. http://dx.doi.org/10.1051/shsconf/20207902027.

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The research purpose is to develop proposals for improving the quality of education in the field of innovative financial technologies.Methods. The following methods were used in the research: theoretical methods, including review and analytical study, comparison and generalization methods, as well as empirical methods, such as expert assessments (questionnaire survey), and statistical methods (statistical observation). Conclusion. Analyzing the respondents’ assessment, the data were obtained that allowed revealing that in practice, the development of the labor market associated with digitalization faced certain problems. In particular, it was revealed that, despite the potential value of improving the quality of education in the field of financial technologies, increasing the level of knowledge and competencies, there was a problem associated with reducing jobs, which in turn increased an opportunity for developing the labor market. Based on the results obtained, conclusions were formulated demonstrating the relationship between the quality of training of labor forces and innovative changes in the economy within the educational process and educational programs.

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Zhang, Xiao, and Chao Yang Li. "Speculation of the Barrier-Free Design about Humanization and Generalization in Urban Transportation Hubs." Applied Mechanics and Materials 253-255 (December 2012): 1787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1787.

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The barrier-free design of transportation hubs is the embodiment of modern civilization and the requirement for the city development. The barrier-free design of urban transportation hubs in Singapore has implemented the people-oriented concept and built a systematic, delicate, harmonious public barrier-free environment. By analyzing the barrier-free design of transportation hubs in Singapore and taking into consideration the speeding population aging and the growing demand of disabled people to integrate into society in our country, the enlightenment is summarized, which will provide reference to the establishment of barrier-free system of our urban transportation and the development of harmonious society.

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Mukhametov, A. Ye. "Assortment, quality and demand for mayonnaise products in Kazakhstan." Problems of AgriMarket, no. 1 (March 15, 2022): 144–52. http://dx.doi.org/10.46666/2022-1.2708-9991.17.

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Mayonnaise is a product that exists for over 100 years and is one of the most beloved sauces in the world. The relevant trend in production of mayonnaise for human health is the decrease of the amount of fat, a balance in their composition, vegetarianism. The goal is to investigate and analyze production and consumption of mayonnaise products in Kazakhstan. Methods – comparative and system analysis, statistical and economic, logical generalization. Results – the main feature of mayonnaise market is the independent supply of the need for this product in most countries. The share of the world imports of mayonnaise is 8% and tends to decrease. There has been no pronounced trend in Kazakhstani mayonnaise market in recent years. In 2019 the volume of domestic market for these products was estimated at 70.6 thousand tons. The recorded rate of decline in mayonnaise market volume in the republic per year was 9%. During the period under review, mayonnaise exports grew by 19% in physical terms and by 23% in terms of value. Conclusions – despite the trends in a healthy lifestyle and the economic crisis which is associated with the spread of coronavirus infection in 2020, the level of consumption of mayonnaise not only remains high, but also increases. In case if there will be further increase of domestic production in the coming years, export sales will grow, while imports will presumably decline.

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Пименова, Д. А., А. Е. Суглобов, and Т. Ю. Демина. "Analysis and generalization of approaches to defining the subject of economic expertise." Voprosy regionalnoj ekonomiki, no. 2(47) (June 18, 2021): 120–27. http://dx.doi.org/10.21499/2078-4023-2021-47-2-120-127.

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Учитывая опыт авторов, накопленный в результате производства и рецензирования экономических экспертиз, в статье предложены следующие вопросы для рассмотрения: · Что понимается под «предметом экспертизы»; · Аккумулируем ведомственные подходы в определении предмета экономической экспертизы; · Аккумулируем сущностные подходы в определении предмета экономической экспертизы применимо к сфере интересов административного права, гражданского права и уголовного права при раскрытии экономических правонарушений; · Предлагаем определение предмета экономической экспертизы по востребованным направлениям в расследовании экономических правонарушений. Taking into account the experience of the authors, accumulated as a result of the production and reviewing of economic examinations, the article suggests the following questions for consideration: • What is meant by the «subject of expertise»; • We accumulate departmental approaches in determining the subject of economic expertise; • We accumulate essential approaches in determining the subject of economic expertise applicable to the sphere of interests of administrative law, civil law and criminal law in the disclosure of economic offenses; • We offer the definition of the subject of economic expertise in the areas in demand in the investigation of economic offenses.

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Imam, Huzaimu Lawal, M. S. Gaya, and G. S. M. Galadanci. "Short term load forecast of Kano zone using artificial intelligent techniques." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 562. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp562-567.

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Load forecast provides useful information for effective electricity dispatch, planning for future expansion and significantly enhances operational efficiency. Conventional techniques yield unsatisfactory forecast which results in high energy losses and in turn leads to high operational cost and suppressed electricity demand. This paper presents hybrid neuro fuzzy (HNF) and Nonlinear Auto-Regressive with eXogeneous input (NARX) neural network for the short term load prediction of Kano region Nigeria. Simulation results obtained demonstrated the generalization capabilities of the models in predicting the load accurately well by achieving MAPE of 0.025% and 0.6551% for the HNF model and NARX network model respectively. The models could serve as promising tool for predicting Kano Zone load demand.

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Peng, Rui, Qing Qing Zhai, Lei Shi, and Jun Yang. "Multi-Valued Decision Diagram Based Reliability Analysis of Demand-Based Warm Standby Systems with Imperfect Fault Coverage." Applied Mechanics and Materials 513-517 (February 2014): 4161–66. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4161.

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In many real-world applications, warm standby redundancy is a commonly applied technique that can compromise recovery time and energy consumption in the fault-tolerant system design. It is considered as a generalization of cold standby and hot standby techniques and has attracted lots of research attentions. In this paper, a demand-based warm standby system subject to imperfect fault coverage is studied. The demand-based system consists of components with different capacities and fails if the cumulative capacity of working components is lower than the desired system demand. To adapt to different fault covering mechanisms, this paper considers two different kinds of fault coverage models, i.e. element level coverage and fault level coverage. A multi-valued decision diagram based approach is proposed to analyze the system reliability. The suggested method is combinatorial and has no limitation on the type of time-to-failure distributions for system components. An example is presented to illustrate the application and advantage of the proposed method.

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Komleva, N. S., and E. G. Shcherbakova E. "RESEARCH OF DEMAND AND MARKET CONDITIONS OF HOTEL SERVICES." Scientific Review Theory and Practice 11, no. 5 (2021): 1360–70. http://dx.doi.org/10.35679/2226-0226-2021-11-5-1360-1370.

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The relevance of the research is connected with the dynamic development of the hotel industry as the most important part of the market economy. To promote a hotel product, it is necessary to have a clear understanding of the features of the market in which the hotel operates, about the competitive advantages of its products, take into account the peculiarities of customer behavior and their needs, that is, conduct research activities. The article studies the trends of demand in the market of hotel services of the Republic of Mordovia, the main groups of consumers of hotel services, analyzes the offers of hotel services based on modern methods and assessment tools. The conducted research has shown that the interest of the population living in the territory of the Republic of Mordovia in the services provided by hotels has increased in the market of hotel services, and the analysis of offers has demonstrated the lack of pronounced uniqueness in the offer of a hotel product for hotel enterprises. In the course of the research, modern general scientific and special methods were used: comparison, generalization, expert assessment, analysis and synthesis. The theoretical and practical significance of the study is that the presented study of the market situation in the field of hotel business on the basis of modern methods and assessment tools can be used in the activities of hotel enterprises in the Republic of Mordovia.

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Meneses-Cime, Karina, Bilin Aksun-Guvenc, and Levent Guvenc. "Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning." Sensors 22, no. 21 (October 29, 2022): 8317. http://dx.doi.org/10.3390/s22218317.

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Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers.

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Kim Hek, Tan, Mohammad Fadzli Ramli, Iryanto, Siti Rohana Goh, and Mohd Faiz M. Zaki. "Generalization of Water Pricing Model in Agriculture and Domestic Groundwater for Water Sustainability and Conservation." E3S Web of Conferences 34 (2018): 02008. http://dx.doi.org/10.1051/e3sconf/20183402008.

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The water requirement greatly increased due to population growth, increased agricultural areas and industrial development, thus causing high water demand. The complex problems facing by country is water pricing is not designed optimally as a staple of human needs and on the other hand also cannot guarantee the maintenance and distribution of water effectively. The cheap water pricing caused increase of water use and unmanageable water resource. Therefore, the more optimal water pricing as an effective control of water policy is needed for the sake of ensuring water resources conservation and sustainability. This paper presents the review on problems, issues and mathematical modelling of water pricing based on agriculture and domestic groundwater for water sustainability and conservation.

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Pishchal’nikova, V. A. "N.I. Zhinkin and Corporeal Semantics (For 125th Anniversary of the Scientist’s Birth)." Russian language at school 79, no. 8 (September 13, 2018): 78–83. http://dx.doi.org/10.30515/0131-6141-2018-79-8-78-83.

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The author updates N.I. Zhinkin’s idea that the language, intelligence, sensing are the mechanizms for getting and processing of information. Any objective perception is the essentially result of polymodal activity, so «significatums of a natural language demand the corpus and emotions being semantically functional». The program of generalization of all extra and inner impacts play the great role in the formation of language capacity. The idea of the unity of somatic and psychological processes became the basis of the new tendency of psycholinguistics – so called corporeal semantics.

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Birmingham, William, and Georg Klinker. "Knowledge-acquisition tools with explicit problem-solving models." Knowledge Engineering Review 8, no. 1 (March 1993): 5–25. http://dx.doi.org/10.1017/s0269888900000047.

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AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

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Jiang, Hao, Yuhang Wang, Ye Tian, Xingyi Zhang, and Jianhua Xiao. "Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle Routing Problems." ACM Transactions on Evolutionary Learning and Optimization 1, no. 1 (April 27, 2021): 1–28. http://dx.doi.org/10.1145/3447540.

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The algorithm recommendation is attracting increasing attention in solving real-world capacitated vehicle routing problems (CVRPs), due to the fact that existing meta-heuristic algorithms often show different performances on different CVRPs. To effectively perform algorithm recommendation for CVRPs, it becomes vital to extract suitable features to characterize the CVRPs accurately. To this end, in this article three groups of penetrating features are proposed to capture the characteristics of CVRPs. The first group consists of some basic features of CVRPs, where several features are suggested to capture the distribution of customer demand, the relationship between customer demand and vehicle capacity, besides some common attributes widely used in CVRPs. The second group is composed of the features extracted from some CVRP solutions generated by local search, where in addition to the feasible and better solutions, the worse solutions and the distribution of travel cost are also used to measure the sensitivity of CVRPs to local search operations. The third group is made up of image features obtained by depicting CVRP instances through images, which is first introduced by us to enhance the generalization of algorithm recommendation. Furthermore, based on the three groups of features, an algorithm recommendation method called ARM-I is built on the basis of a KNN classifier to recommend suitable algorithm for CVRPs. Experimental results on several selected benchmarks demonstrate the effectiveness of the designed features. More interestingly, the proposed ARM-I shows high generalization on real-world instances.

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Jamii, Mohammed, and Mohamed Maaroufi. "The Forecasting of Electrical Energy Consumption in Morocco with an Autoregressive Integrated Moving Average Approach." Mathematical Problems in Engineering 2021 (June 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/6623570.

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The national demand for primary energy has experienced an average increase of almost 5% in recent years, driven by the growth in electricity consumption, which grew by an average of 6% per year between 2003 and 2017, by virtue of the almost generalization of rural electrification and the dynamism of our economy and especially the policy of major works in infrastructure, industry, agriculture, tourism, and social housing. In fact, forecasting the demand for electrical energy remains a controversial issue in the development of the electricity grid and energy management. The ARIMA (1, 1, 1) model is applied to model electrical energy consumption for the annual period from 1971 to 2020. The same data are also used to predicting for 2021–2030 in order to verify the adequacy of the model and to provide information on the state of energy demand in Morocco in the future. The main results indicate an upward trend in electrical energy consumption by the end of 2030, with electricity consumption expected to be in the range of 2039639.09–53589.00 GWh per year.

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Lu, Zhanhui, Weijuan Wang, Gengyin Li, and Di Xie. "Electricity Market Stochastic Dynamic Model and Its Mean Stability Analysis." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/207474.

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Based on the deterministic dynamic model of electricity market proposed by Alvarado, a stochastic electricity market model, considering the random nature of demand sides, is presented in this paper on the assumption that generator cost function and consumer utility function are quadratic functions. The stochastic electricity market model is a generalization of the deterministic dynamic model. Using the theory of stochastic differential equations, stochastic process theory, and eigenvalue techniques, the determining conditions of the mean stability for this electricity market model under small Gauss type random excitation are provided and testified theoretically. That is, if the demand elasticity of suppliers is nonnegative and the demand elasticity of consumers is negative, then the stochastic electricity market model is mean stable. It implies that the stability can be judged directly by initial data without any computation. Taking deterministic electricity market data combined with small Gauss type random excitation as numerical samples to interpret random phenomena from a statistical perspective, the results indicate the conclusions above are correct, valid, and practical.

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Pujić, Dea, Nikola Tomašević, and Marko Batić. "A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring." Sensors 23, no. 3 (January 28, 2023): 1444. http://dx.doi.org/10.3390/s23031444.

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Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

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Chen, Ying, Chun Feng Deng, and Yao Zhi Huang. "Exploration of Elderly Ecological Residential Area Project under the Aging Background - Take Changzhou Taihu Yaoxianshan Yiyang Villa as Example." Applied Mechanics and Materials 507 (January 2014): 625–30. http://dx.doi.org/10.4028/www.scientific.net/amm.507.625.

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Ecological planning and construction of elderly residential area is the new trend under the present aging development background. Aimed at the demand gap of elderly residential area on the market currently, based on the generalization and summarization of pension settlements development history, the inevitability and feasibility of elderly ecological residential areas development has been demonstrated. Changzhou Taihu Yaoxianshan Yiyang Villa, as the elderly ecological residential area project, provides referential experience in terms of the practice of elderly residential area; it has a certain value of the Construction and popularization of elderly residential area.

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Pan, Erting, Yong Ma, Fan Fan, Xiaoguang Mei, and Jun Huang. "Hyperspectral Image Classification across Different Datasets: A Generalization to Unseen Categories." Remote Sensing 13, no. 9 (April 26, 2021): 1672. http://dx.doi.org/10.3390/rs13091672.

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With the rapid developments of hyperspectral imaging, the cost of collecting hyperspectral data has been lower, while the demand for reliable and detailed hyperspectral annotations has been much more substantial. However, limited by the difficulties of labelling annotations, most existing hyperspectral image (HSI) classification methods are trained and evaluated on a single hyperspectral data cube. It brings two significant challenges. On the one hand, many algorithms have reached a nearly perfect classification accuracy, but their trained models are hard to generalize to other datasets. On the other hand, since different hyperspectral datasets are usually not collected in the same scene, different datasets will contain different classes. To address these issues, in this paper, we propose a new paradigm for HSI classification, which is training and evaluating separately across different hyperspectral datasets. It is of great help to labelling hyperspectral data. However, it has rarely been studied in the hyperspectral community. In this work, we utilize a three-phase scheme, including feature embedding, feature mapping, and label reasoning. More specifically, we select a pair of datasets acquired by the same hyperspectral sensor, and the classifier learns from one dataset and then evaluated it on the other. Inspired by the latest advances in zero-shot learning, we introduce label semantic representation to establish associations between seen categories in the training set and unseen categories in the testing set. Extensive experiments on two pairs of datasets with different comparative methods have shown the effectiveness and potential of zero-shot learning in HSI classification.

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Wang, Chin-lih. "A generalization of an aggregate almost ideal demand system (Economics letters, vol. 41, no. 4, 1993, pp. 369–371)." Economics Letters 46, no. 3 (November 1994): 289. http://dx.doi.org/10.1016/0165-1765(94)90099-x.

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Sankepally, R. R., and K. S. Rajan. "IMPROVING PATH QUERY PERFORMANCE IN PGROUTING USING A MAP GENERALIZATION APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 191–98. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-191-2018.

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Abstract. pgRouting library provides functions to compute shortest path between any two points of a road network which is of great demand and also a topic of interest in the field of GIS, graph theory and transportation. To compute path in a road network, pgRouting functions process the entire road network which is a major bottleneck when it comes to routing in large road networks leading to the requirement of large server resources. A reduction/compression in the input network that is to be processed for path computation would improve the performance of pgRouting. In this study a map generalization based network model is proposed which extracts a significantly smaller subset of the road network aka skeleton which further used to divide the network into zones, that shall be selectively used in path computation. This results in processing a much smaller part of the network to compute path between any two points leading to an overall improvement in query performance of pgRouting when computing path, especially on large road networks. As part of assessment of this approach and its applicability to large road networks, the paper presents an in-depth analysis of the trade-offs between deviation in computed path and the performance gain in terms of space and time on road networks of varying sizes and topology to get a better understanding for both providing a sound proof of the utility of the proposed method and also to show its implementability within the current model of pgRouting or any other routing platforms.

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Lv, Zhixing, Sijin Cheng, Yi Wang, Shenzheng Wang, Xinyi Li, and Ran Ran. "Combining Daily Electricity Forecasts Based on DBSCAN and Stacking Fusion." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, no. 7 (December 10, 2021): 767–77. http://dx.doi.org/10.2174/2352096514666211018124044.

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Background: Modern upgrades of power grids and a rapidly expanding economy complexify the uncertainties of electricity demand. Objective: The objective of the study is to have a more precise prediction on the demand side, which is beneficial in affirming the stable operation of the power system. Methods: This paper presents a combined electricity forecasting method based on the users clustering and stacking ensemble learning to mine underlying properties of different individual consumers. The preprocessed electricity consumption profiles are inputted into the DBSCAN clustering algorithm to obtain the clusters. The alternative models are tailored for different clusters in the stacking fusion framework for training and testing. Result: Experimental results on the operating data of Shandong Power Grid show that the proposed method has higher prediction accuracy and better generalization ability. Conclusion: The framework is of great significance for improving the level of power supply service.

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Chen, Shang-Yuan, and Tzu-Tien Chen. "Dockless Shared Bicycle Flow Control by Using Kernel Density Estimation Based Clustering." Advances in Technology Innovation 6, no. 3 (April 12, 2021): 146–56. http://dx.doi.org/10.46604/aiti.2021.6666.

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Since dockless sharing bicycles have become an indispensable means of everyday life for urban residents, how to effectively control the supply and demand balance of bikes has become an important issue. This study aims to apply Kernel Density Estimation based (KDE-based) clustering analysis and a threshold-based reverse flow incentive mechanism to encourage the users of bicycles to adjust the supply and demand actively. And it takes Shanghai Jing’an Temple and its surroundings as the research area. Its practical steps include: (1) compilation and processing of the needed data, (2) application of KDE-based clustering, partitioning, and grading, and (3) incentives calculation based on dockless shared bicycle flow control system. The study finds that the generalization function of KDE-based clustering can be used to estimate the density value at any point in the study area to support the calculation of the incentive mechanism for bicycle reverse flow.

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Conner, David, Emily Irwin, and Maelle Simmen. "Exploring demand for eco-labeled dairy product attributes in Vermont." British Food Journal 120, no. 12 (December 3, 2018): 2857–67. http://dx.doi.org/10.1108/bfj-05-2018-0305.

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PurposeThe purpose of this paper is to investigate the potential of eco-labels to help dairy farm viability as one partial solution to a complex problem. Specifically, it aims to understand which attributes are most likely to increase consumption and garner price premiums.Design/methodology/approachThis paper uses key informant interviews and a convenience sample (n=203) of supermarket shoppers in Vermont. It uses Likert-type scales to measure the likelihood of increased purchase and paying price premiums based on a series attributes including animal welfare, fair labor standards, family farms and environmental stewardship. It calculates and compares mean ratings of each attribute and use an ordinal regression to measure the effect of demographic attributes on each attribute’s rating.FindingsInterviewed stakeholders named low milk prices and evolving industry structure as harming dairy farm viability. They list supply control and improved promotion as potential solutions. Survey respondents say attributes supporting animal welfare, farm workers, family farms and healthy soil are most likely to garner increased consumption and price premiums.Research limitations/implicationsThe authors use a convenience sample, so generalization to larger populations is not advisable.Practical implicationsPromotion around animal welfare, farm workers, family farms and healthy soil is most likely to be effective, based on the results of this study. The survey responses are very highly correlated, suggesting that a multi-attribute eco-label may garner the most support.Social implicationsThis work can inform efforts to promote dairy farm viability, an important sector of the agricultural economy in Vermont and elsewhere in the USA.Originality/valueThis research provides the ranking of attributes which may appear on eco-labels by current consumers of dairy products in a state with an important dairy heritage and industry.

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Tang, Zhongjun, and Lang Ni. "An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/2025/1/012022.

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Abstract The lack of historical sales data and word-of-mouth information in the film preparation period, the few available variables and the uncertainty in the prediction process lead to the difficulty in predicting the total box office demand of films. To solve this problem, this paper constructed and verified the prediction method of interval reliability demand in the film preparation period, which combined XGBoost algorithm and D-S evidence theory. Firstly, the total box office interval was effectively divided according to the sample data of the training set, and XGBoost was used to complete the calculation of the reliability function value of the evidence variables. Then, the D-S evidence theory was used for information fusion to obtain the results of box office interval reliability fusion. Finally, the box office attribution was judged by the interval reliability, so as to realize the interval reliability demand prediction in the preparatory period. The validity of the proposed method was verified by selecting the data of Chinese films from 2017 to 2019, and it was compared with the classical predictive classification algorithm. The results showed that the method has higher prediction accuracy and better generalization ability.

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Belyaev, Yuri, and Sara Sjöstedt-de Luna. "Weakly approaching sequences of random distributions." Journal of Applied Probability 37, no. 3 (September 2000): 807–22. http://dx.doi.org/10.1239/jap/1014842838.

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We introduce the notion of weakly approaching sequences of distributions, which is a generalization of the well-known concept of weak convergence of distributions. The main difference is that the suggested notion does not demand the existence of a limit distribution. A similar definition for conditional (random) distributions is presented. Several properties of weakly approaching sequences are given. The tightness of some of them is essential. The Cramér-Lévy continuity theorem for weak convergence is generalized to weakly approaching sequences of (random) distributions. It has several applications in statistics and probability. A few examples of applications to resampling are given.

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Journal articles on the topic 'Demand generalization'

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