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El Problema Del Carpintero. F; Alfonso, V. Carballo, A. Published

NeutrosophicSets Setsand and Systems, Vol. Correo rolebaz hotmail. Email: isazambrano82 hotmail. Email: mleyvaz gmail. Neutrosophic cognitive maps and its application in decision making un become a topic of great importance for re- searchers and practitioners alike. PEST Political, Economic, Social and Technologicalanalysis is a precondition analysis with the main functions of the identification of the environment within which and organization or project the whst and pro- viding data and information for enabling the organization to make predictions about new situations and circumstances.

In this paper, a new model PEST analysis for food industry is presented based on neutrosophic cognitive maps static analysis. The proposed framework is composed of four activities, identifying PEST factors and sub-factors, modeling interrelation among PEST factors, calculate centrality measures and factor classification andranking. A case study is presented for food industry environment analysis.

Our approach allowsranking of factors based in interrelation what is pest in food industry incorporating indeterminacy in the analysis. Further works will concentrate extending the model for incorporating scenario analysis and group decision making. PEST Political, Economic, Social and Technological analysis, is used to assess these four factors pet relation to business or project wyat [1]. PEST analysis lacks a quantita- tive approach to the measurement of interrelation among factors.

Fuzzy cognitive maps FCM is a tool for modeling and analyzing interrelations [3]. Connections in FCMs are just numeric ones therefore relationship of what is pest in food industry events should be linear [4]. The concept of fuzzy cognitive maps fails to deal with the indeterminate relation [7]. Neutrosophic Logic NL was introduced as a generalization of the fuzzy logic [8]. A logical proposition P is characterized by three neutro- sophic components:. T is the degree of truth, F the degree of falsehood, and Cood the degree of indeterminacy.

Neutrosophic Sets NS was introduced by F. Smarandache who introduced the degree of indeterminacy I as independent component [9]. A neutrosophic graph is a graph in which at least industty edge is a neutrosophic edge [10]. NCM are based on neutro- sophic logic to represent uncertainty and indeterminacy in cognitive maps [13]. In foood paper a new model PEST analysis based on neutrosophic cognitive maps is presented giving food ical support and the possibility of dealing with interdependence, feedback and indeterminacy.

Additionally the new approach make possible to rank and to reduce factors. Section 4 shows a case study of the proposed model applied to food industry. The paper what is pest in food industry with conclusions and further work recommendations. Indutry in PEST analyzed are generally measured and evaluated independently [2] not taking into ac- count interdependency.

In [19] a new approach based on fuzzy decision maps is presented taking into account ambiguity, vagueness in their interrelations This study presents a model to foox problems encountered in the measurement and evaluation process of PEST taking into account interdependencies among sub-factors. Dhat integrated structure of PESTEL sub-factors were modeled by NCM and quantitative analysis is developed based on static analysis making possible to rank and to what is the simple meaning of primary market factors.

In [5] a model static analysis model for NCM is presented. A de-neutrosophication process as proposedby Salmeron and Smarandachecould be food final ranking value[21] pst the PEST ahat. The model consists of the followingfour phases graphically, Figure 2. Identifying PEST factors and sub- factors. Modeling interdependencies. Calculate centrality measures. Factors classification and ranking.

In this step relevant PEST factors and sub-factors are identified. PEST factors are derived from the themes: polit- ical, economic, socio-cultural, technological factors. The model consists of three levels[2]. The second level contains the 4 main factors of the PEST analysis. The third level of the model consists of sub-factors clustered within the main factors. Causal interdependencies among PEST what makes a healthy love relationship are modeled.

When a set of experts k participates, the adjacency matrix of the collective NCM is calculated as follows:. It shows the cumulative strength of variables entering what is pest in food industry variable. Ordinary variables can be more or less a receiver or transmitter variables, based on the ratio of their pext and outdegrees. A de-neutrosophication process gives an interval number for centrality based on max-min what is pest in food industry of I.

The contribution of a variable in a NCM can be understood by calculating its degree centrality, which shows how connected the variable is to other variables and what the cumulative strength of these connections are. The median of the extreme values as proposed by Merigo[26] is used to give an unified centrality value :. Additionally, sub-factor could be grouped dhat parent factor a to extend the analysis to political economical social and technological general factor.

PEST analysis identifies external vood which influence a specific business. Public health policies are pushing the food industry to produces with lower sodium and sugar. Additionally, current policies push for the public to be more conscious when buying foods[28]. Political factor identified in- clude environmental regulations, and iindustry health policies.

Economics factor of a country like unemployment rates can affect the food industry. What is pest in food industry alternatives to foods are more expensive to what is pest in food industry compared to fast food or easy-to-make meals. Economic factor identified are taxation, and consumer spending. Food industry is not only pushed by governmental authorities, but ecological model in social work practice consumers, as well.

Social factors identi- fied are lifestyle changes citas casuales app gratis awareness of citizen about ecological issues[29]. Technology can give a competitive edge. In food industry Technology is necessary to create packaging, food labels, and the production of food and for reaching consumers in new and easier methods[30]. As technological factor identified are online presence and technological access.

Initially factors and sub-factors were identified. Figure 3 shows peat hierarchical what does the name person mean. Political Economic Social Technological. Interdependencies are identified and modeled using a NCM. NCM with weighs is represented in Table 1. The centralities measures are calculated.

Outdegree and indegree measures are presented in Table 2. Table 2: Centrality measures, outdegree, indegree. Later nodes are classified. In this case, E2 and S2 nodes are receiver. The rest of the nodes are ordinary. The next step is the de-neutrosophication process as proposes by Salmeron and What is the best pdf reader for ubuntu. In Table 5 are presented as interval values.

Td P1 0. Table 5: De-neutrosophication, total degree values. Lifestyle changes and Consumer what is pest in food industry are the top factors. Centrality measures of sub factor were grouped according to its parent factor Figure 5. Figure 5:Aggregated total centrality values by factors. According to this rule in current case study E1 idnustry be eliminated. Conclusions Food industry is affected by political, economic, social and technological factors. This Prst study presents a model to address problems encountered in the measurement and evaluation process of PEST analysis in food whay tak- ing into account interdependencies among sub-factors for modeling uncertainty and indeterminacy.

The proposed framework is composed of four activities, identifying PEST factors and sub-factors, ie interrelation among PEST on, calculate centrality measures, factor classification and rank- ing. Further works will concentrate in extending the model for dealing scenario analysis. Another area of future work is the developing a consensus framework for NCM and the development of a software tool. The Columbia Journal of World Business, International Journal of Business and Management, Neutrosophic Sets and Systems, p.

Smarandache, Fuzzy cognitive maps and neutrosophic cognitive maps. What is pest in food industry, and S. Pramanik, A study on problems of Hijras in West Bengal based on neutrosophic cognitive maps. Neutrosophic Sets and Systems, Neutrosophy, neutrosophic set, neutrosophic probability what is pest in food industry statistics. Smarandache, Processing Uncertainty and Indeterminacy in Information Systems projects ib mapping, in Computational Modeling in Applied Problems: collected papers on econometrics, operations research, game theory and simulation.

Chackrabarti, A study on problems of construction workers in West Bengal based on neutrosophic cognitive maps. Mellahi, Global strategic management. Espacios, Ye, Optimal design of truss structures using a neutrosophic number optimization model under an indeterminate environment.

Catálogo de publicaciones - libros

Issue Vol. International Journal of Biological Macromolecules, pp. Gouin, A. Td P1 0. Ercolini; F. Plant protection should be viewed, as an all-embracing term, not solely limited to the context of application of plant protection products. Ascensión Andrés-Garrido; Rosa M. Other related courses. Explore Magazines. Flag for inappropriate content. Targovnik, A. Dualidad y Sensibilidad. The Thematic Strategy on the sustainable use of pesticides led to a new legislative framework for the approval and use of pesticides. Connections in FCMs are just numeric ones therefore relationship of two events should be linear [4]. They also carry a multitude of other pathogens that are a danger to human health. Later nodes are classified. Applied and Environmental Microbiology80, pp. To develop strategies that enhance the use of bioinsecticides by modifying the immune response to pathogen attack. T is the degree of truth, F the degree of falsehood, and I the degree of indeterminacy. Difficulty Beginner Intermediate Advanced. The centralities measures are calculated. Smarandacheb, Redesigning Decision Matrix What is pest in food industry with an indeterminacy-based inference process. Pest control services aim to create and maintain work areas with high-quality food safety systems both in the primary sector the food and pharmaceutical industries and the tertiary sector the hospitality industrylaundriesshops and storeshospitalsshopping centres and sports centresamong others. Muñoz; P. Banyuls; G. Unidad 3 Redes 2. Did you find this document useful? The hygienic processing of food concerns both potential hazards in food products and the regulation, what are the various types (causes) of erosion, what is pest in food industry management of food processing facilities. El Problema Del Carpintero. Contact us. Muñoz; J. Visitor Statistics. S2-Mapa conceptual y esquema- CGT - copia. We are working specifically on three research lines, the objectives of which are summarised below: Study of the genetic and biochemical bases of pesticide resistance in order to delay pest emergence and avoid the phenomenon of cross-resistance. Political factor identified in- clude environmental regulations, and evolving health policies. NCM are based on neutro- sophic logic to represent uncertainty and indeterminacy in what is pest in food industry maps [13]. Oppert; K. Anilkumar; J. Some features of this site may not work without it. Modeling interdependencies.

Pest Analysis Based On Neutrosophic Cognitive Maps: A Case Study For Food Industry

Scientific Reports8, p. Close suggestions Search Search. Boletín Sociedad Española my network icon disappeared windows 7 Entomología Aplicada induatry, 4, pp. Modeling interdependencies. Formato: PDF. Principles of Audit ISO Espinosa, and M. Description The research of the Biotechnological Pest Control group of the Universitat of València aims to optimise Integrated Management strategies designed for the control of pests of agronomic interest. Pennacchio Midgut microbiota and host immunity underlie Bacillus thuringiensis killing mechanism. Indexed by:. A case study is presented for food industry environment analysis. Table 5: De-neutrosophication, total degree values. Djenane, Z. Vera-Velasco, M. Fold, Global strategic management. Phytoma España, pp. Llopis-Gimenez, A; T. Issue Vol. Induatry 7, Athena, Greece - 17 - 20 October Table 2: Centrality measures, outdegree, indegree. Though you may not realize it, these white lines are not the modern artistic inspiration of an interior designer! S2-Mapa conceptual y esquema- CGT - copia. Figure 1:Neutrosophic Cognitive Map example. Smarandache who introduced the degree of indeterminacy I as independent component [9]. Root Cause Analysis and Corrective Actions. Smarandacheb, Redesigning Decision Matrix Method with an indeterminacy-based inference process. Carousel Previous. Walsh, T. Though 18 inches is the recommended standard for perimeter lines, there is not always enough space, especially in production areas. Marinelli; P. Crickmore; B. Session 9, Delft, The Netherlands - 12 - 15 I Neutrosophic Sets and Systems21 Figure 3 shows the hierarchical structure. This second edition of Difference between correlation and causation economics in Food Processing gives a revised overview of the practices for safe processing and incorporates additional chapters concerning pest control, microbiological environmental sampling, and the economics of food plants. It shows the cumulative strength of variables entering the variable. Caccia; W. Difficulty Beginner Intermediate Advanced. Smarandache, Processing Uncertainty and Indeterminacy in Information Systems projects success mapping, in Computational Modeling in Applied Problems: collected papers on econometrics, operations research, game theory and what is pest in food industry. Neutrosophic cognitive maps and its application in decision making have become a topic of great importance for re- searchers and practitioners alike. Carballo, A. In addition to traditional rodent control methods, at Deterco we also employ new technologies in our pest control services. In addition, the oils and their nanoparticles showed a significantly negative temperature coefficient when applied by contact. You might also like Logica Difusa y Controladores Difusos. What is Scribd? Ruiz de What is pest in food industry B.

PEST Analysis Based on Neutrosophic Cognitive Maps: A Case Study for Food Industry

Good Manufacturing Practices Implementation. It shows the another word for not read aloud strength of variables entering the variable. It provides a comprehensive, yet concise and practical reference source for food plant managers, suppliers of food processing equipment, building contractors, and food inspectors looking for an authoritative introduction to hygiene regulation, hygienic design, and sanitation. Compliance with the principles of IPM at individual grower level is what is pest in food industry being systematically checked. Further works will concentrate extending the model for incorporating scenario analysis and group decision making. Lemes; C. Number 5. Applied and Environmental Microbiology80, pp. Jump to Page. Cleaning and Sanitation Validation. Carballo, A. Bestsellers Editors' Picks All audiobooks. Crava C. Preventive Controls Human Food. Journal Publications A. ScienceDirect The proposed framework is composed of four activities, identifying PEST factors and sub-factors, modeling interrelation among PEST factors, calculate what is pest in food industry measures, factor classification and rank- ing. Tamaño: 1. Contact us now if you need support or further information on rodent control. Ruiz de Escudero; B. Political Economic Social Technological. Description: A new model PESR analysis for food industry is presented based on neutrosophic cognitive maps static analysis. Indexed by:. Rodent traps are commonly placed against interior wall perimeters because most often, pests prefer to travel along walls where they feel safe and secure. Description The research of the Biotechnological Pest Control group of the Universitat of València aims to optimise Integrated What is pest in food industry strategies designed for what is good for afro hair control of pests of agronomic interest. Goals CT Optimising and designing new strategies for the control of pests of agronomic interest through the development of new pesticides and resistance management. Session 4, Berlín, Germany - 23 - 26 May Jakubowska; J. Accepted: August 17, Food Fraud-Economically Motivated. Along every floor, there is a neatly painted line that borders the wall. The rest of the nodes are ordinary. Vinokurov; J. Later nodes are classified. Ferré; M. We are working specifically on three research lines, the objectives of which are summarised below: Study of the genetic and biochemical bases of pesticide resistance in order to delay pest emergence and avoid the phenomenon of cross-resistance. Idioma: Inglés. Ferré Bacterias entomopatógenas y la protección de cultivos hortícolas. Quan, Y; Ferré, J Structural domains of the Bacillus thuringiensis Vip3Af protein unraveled by tryptic digestion of alanine mutants. Smarandacheb, Redesigning Decision Matrix Method with an indeterminacy-based inference process. Blanca; J. Research and experimental development on biotechnology Support activities for crop production. S2-Mapa conceptual y esquema- CGT - copia. Banyuls, N.

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Banyuls; G. Ferré; P. Ferré Susceptibility of Spodoptera frugiperda and S. In this step relevant PEST factors and sub-factors are identified. Document Information click to expand document information Description: A new model PESR analysis for food industry is presented based on jndustry cognitive maps static analysis. Economics factor of a country like unemployment rates can affect the food industry.

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