Paper Title (use style: paper title)



Identification of Territorial Vulnerability Index based on Hierarchical and Heuristic Models using SOA

Wilmer David Oidor Bolaños

Universidad Católica de Colombia

Bogotá, Colombia

e-mail: woidor08@ucatolica.edu.co

Luis Alejandro Rodriguez Torres

Universidad Católica de Colombia

Bogotá, Colombia

e-mail: larodriguez27@ucatolica.edu.co

Abstract— In the project design and development of a Web service is performed by each of the decision -making models (AHP , Fuzzy AHP , ELECTRE and PROMETHEE ) , which would be responsible for processing field data in the first phase of the project " Retrospective of Natural Disasters in Colombia As Input for Building a Decision Support System " , conducted through surveys , interviews formats , workshops and analysis methodologies . The data were processed according to the 4 decision models, generating a final outcome indicator territorial vulnerability.

To proceed with the analysis and further development each web services, first a literature review of each of the models above decision making was carried out to identify the structure, methodology and performance. Further analyzed and identified the data obtained in the field in the first phase to fulfill their function as input information for each model and the corresponding algorithm treatment process it and generate the final result.

Keywords- Decision Making, Decision Theory, Disaster Prevention, Natural Disaster, Systems Design.

INTRODUCTION

Currently worldwide there are many systems or technologies that are responsible for informing and bringing the consolidated few natural disasters have caused and what places have occurred, but do not have a system to report or provide the information necessary to know what vulnerable is some territory and what might be the possible actions to take according to the degree or level of vulnerability.

In this regard and in order to strengthen risk management in the country, this research responds to the second phase of the project entitled "Retrospective of natural disasters in Colombia as input for the construction of a decision support system", where necessary to advance a decision support also incorporate response mechanisms in the territory in the short, medium and long term system. Specifically contributing to the prioritization of intervention actions when a natural disaster occurs.

In the first phase of a system of indicators which aimed to review the involvement in the territorial system after a natural disaster and found that dimensions such as socio-cultural and political institutions, needed special treatment is proposed. This considering that information to support the system of indicators should be generated through field surveys formats interviews, workshops and analysis methodologies.

For this, during the second phase aims to build a system that generates greater impacts, building different strategies for information such as the welfare and development, community organizing, psychological, and cultural beliefs that identify a territory. Additionally, the design of the data model of the first phase was achieved conceptually structuring a model where the input data are indicators and output alarm levels of involvement in the territorial system, the latter intended to generate signals to people skilled in making decisions about which dimensions concentrate prevention plans, emergency mitigation and reconstruction.

The problem is how to identify the territorial vulnerability index using data and indicators collected and analyzed in the field of stage I of the project "Retrospective of Natural Disasters in Colombia As Input For Building a Decision Support System" .

According to the above account raises the following research question that will lead the development of this proposal:

What is the treatment and adaptation will have to give the decision-making models to build and calculate the territorial vulnerability index, based on the data collected?

RELATED WORK

1 Prioritized Multi-Criteria Decision Making Based on the Idea of PROMETHEE.

This article shows the concept to develop a method based on the idea of ​​Promethee model that compensates the imperfection of aggregation operators to prioritize all possible situations according to the pairwise comparison when implementing existing models multicriteria decision because the existing models or multi techniques are very useful to provide solutions to complex problems, but these models do not take into account all the possible connections between the various criteria [1].

Current research in multi-criteria models mainly focus on how to add information regarding the assessment of prioritization criteria including how to build prioritized aggregation operators, but often do not take into account the type of prioritization or the order of priority [1].

The objective is to develop a new multi precedence over the idea of ​​promethee model is to compare alternatives in pairs with respect to criteria one by one, using as a first step the definition of the preference function, then calculate the index of preference, it is here where preference function or revision levels are expected or influence of the decision maker on alternative and / or criteria followed this intuitionistic preference ratio (combination of technology decision making is constructed and used Fuzzy theory) containing the levels of certainty expressed in a matrix, and two indexes of preference for the alternative, an average is obtained; Finally a range of alternative preferred ratio is obtained , thus obtaining a vector of classification that can be used for ranking the alternatives.

2 Revised PROMETHEE II for Improving Efficiency in Emergency Response

This article proposes to implement several steps of traditional Promethee II model for the calculation time and increase the number of incidents emergency management plan, to strengthen the emergency response system and strengthen public dialogue. [2].

In all studies for emergency management have been used different models or theories of multi-criteria decision-making, but rarely has been used promethee model or theory, as it is very difficult to meet the timeliness requirements management emergency due to the multiple steps, the large number of calculations and comparisons of the traditional algorithm promethee II.

3 Multicriterion analysis of a vegetation management problem using ELECTRE II. Appl. Math. Modelling

One objective of this work done, is to use the ranking or classification Electre II of management actions watershed, actions, alternatives and their estimated impacts that may have a vegetation specifies oriented to increasing water production a forested watershed of 38.8 square miles in the White Mountains of central Arizona. [3].

The alternatives of the problem are evaluated on seven criteria: The wood and fodder, agriculture, water supply, maintenance, floods, hydropower generation and reservoir-based recreation. Stochastic models of precipitation structured around a deterministic watershed model and a system of hypothetical reservoir, were used in the computer simulation to evaluate these management options in terms of their respective impacts on the criteria.

4 Land acquisition and resettlement action plan (LARAP) of Dam Project using Analytical Hierarchical Process (AHP): A case study in Mujur Dam, Lombok Tengah District-West Nusa Tenggara, Indonesia.

This article is published as the AHP model is used to decide the best location for the people who are in the area where you want to build a water dam, taking into account the land acquisition plan of the local government of the people of Mujur . As expected in the dam project area recovers to be more productive in order to increase the prosperity of the people. However, as this project often leads to other problems, and the most striking is the resettlement of people. Therefore, the study of land acquisition and Resettlement Action Plan (LARAP) should be implemented prior to construction of a dam in order to have a precise project disadvantages consideration of the advantages and those affected [4].

The study seeks to find relocation advantages and disadvantages suffered by those affected, which must consider and take into account many factors, socio-economic, comprehensive planning of the acquittal of land, resettlement, and schema compensation. The study requires only that the resettlement areas should be as close as possible to the areas of acquisition and displaced persons should be satisfied, generating great community involvement, to minimize environmental risks, and finally that people have good access transport.

5 Framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology.

The theme that relates to the article due to propose a framework to measure the performance of technical education in India, because it crosses many challenges today because of globalization and liberalization of the economy in that country. For the study are based on data collected in 2007 and 2008 7 institutes of technology preference criteria analyzing stakeholder model using the combination of AHP and FUZZY multiattribute proportional method to the evaluation of alternatives (COPRAS).

The performance of the technical institutions in the absolute sense, it is very difficult to measure. There are many factors / criteria / attributes / objectives affect the performance of institutions and the result of the measurement is very sensitive to the selection criteria [5]. You have to carefully consider when making the selection of criteria to measure the performance of these educational institutions as there are criteria that depend on others to do this are based on the study of historical data pertaining to education models in India and the opinion of experts in the field.

METHODOLOGY

Based on information from the document review and analysis of the models making multi -criteria decision, proceed with the application of concepts related to systems engineering for the project implementation. The project is structured around the steps defined in the agile development methodology AUP (Agile Unified Process), showing the role, phases and components thereof.

UPA methodology "covers, plus a set of procedures and tools designed to correct modeling of the business during the life cycle of software development, a framework of good practice for the construction phase of the software " [6 ] .

The deliverables required by this methodology was adapted to reality and life time of the project and are also relevant to the nature of the software solution; together with the existence of a greater number of open source tools, aimed at modeling systems generating UML artifacts needed for the analysis and design phases of the web services.

The selected agile development methodology has four stages throughout the process. The stages are defined in the following items structured as follows:

• Initiation: It corresponds to the theme developed in the application of decision models for identifying the vulnerability index.

• Preparation: the class diagram and sequence is developed.

• Construction System: functioning and structure associated to Web services are explained.

• System transition: the process of developing tests that apply to Web services is displayed.

IMPLEMENTATION OF DECISION MODELS FOR IDENTIFICATION OF VULNERABILITY INDEX.

The operation of each of the decision-making models are used to make the calculation of the territorial vulnerability.

1 Model Hierarchical Analysis Process (AHP).

Here's a simple operation of AHP model to calculate the Territorial Vulnerability Index.

1 Read File. Sort by territorial dimension, for storage in a data matrix (criteria), then read all the variables belonging to one dimension and store them in a data matrix (sub) by distributing the information as a hierarchical tree as follows:

Objective: Index of territorial vulnerability.

Criteria: Dimensions (1, 2, 3, 4, 5 ...)

Sub-criteria: Variables belonging to each territorial dimension

5 Square MAtrix Criteria. Criteria which in this case corresponds to the territorial dimensions and a square matrix formed from the elements are selected.

6 Comparison Par. A paired comparison of a dimension relative to the other, indicating how important criterion is performed against the other according to the scale of Saaty, which is listed in Table I.

SAATY SCALE

|VALUE COMPARED|INTERPRETATION |

|PAIR IJ | |

|1 |THE CRITERION I AND J ARE EQUALLY IMPORTANT CRITERION|

|3 |THE CRITERION I IS SLIGHTLY MORE IMPORTANT THAN J |

|5 |THE CRITERION I IS STRONGLY MORE IMPORTANT THAN J |

|7 |THE CRITERION I IS VERY STRONGLY MORE IMPORTANT THAN |

| |J |

|9 |THE CRITERION I IS ABSOLUTELY MORE IMPORTANT THAN J |

|OTHERS VALUES |EXPLANATION |

|2,4,6,8 |INTERMEDIATE VALUES ​​BETWEEN TWO ADJACENT JUDGMENTS |

| |USED |

| |as consensus values ​​between two trials. |

|increment 0,1|Intermediate values ​​for finer gradations to trials |

| |(For example 7.3 is a valid entry). |

The scale according to a preference value or level of importance with respect to other criteria being compared is assigned, as shown in Figure 1.

|TERRITORIAL |Poli|Envir|Sociocu|economic |buil| |

|DIMENSION |tica|o |ltural |productive |t | |

| |l-In|nment| | |(urb| |

| |stit|al | | |an -| |

| |utio| | | |regi| |

| |nal | | | |onal| |

| | | | | |) | |

|Sociocultural |S1 |S2 | |Económic- |S1 |S2 |

| | | | |Productive | | |

|Built |S1 |S2 | | | | |

|S1 |1 |4 | | | | |

|S2 | 1/4|1 | | | | |

Matrix Matrix Subcriteria (Variables) Criterion (Dimension)

After assigning comparison values ​​between pairs of elements, we proceed to normalize each of the matrices, and then obtain the relative priority of each of the elements compared, averaging each row of the normalized matrix.

|D1 |S1 |S2 |Priorit|D2 |D2 |S1 |S2 |Priorit|

| | | |y | | | | |y |

|D3 |S1 |S2 |Priorit| |D4 |S1 |S2 |Priorit|

| | | |y | | | | |y |

|D5 |S1 |S2 |Priority | |

|1 |(1,1,2) |M1 |Equally important |The two elements |

| | | |from both |likewise contribute |

| | | |elements |form the target |

|3 |(2,3,4) |M3 |Moderate importance|Experience and |

| | | |of |judgment |

| | | |element of another |Slightly favor an |

| | | | |element on the |

| | | | |other. |

|5 |(4,5,6) |M5 |Importance of a |One of the elements |

| | | |strong |is |

| | | |element on the |strongly favored |

| | | |other | |

|7 |(6,7,8) |M7 |Very strong |One of the elements |

| | | |importance of |is |

| | | |element of another |strongly dominant |

|9 |(8,9,9) |M9 |A paramount |The evidence |

| | | |element of another |favoring |

| | | | |  one of the |

| | | | |elements |

| | | | |is the highest order|

| | | | |of |

| | | | |assertion |

|2,4,6,8 |(1,2,3) |M2,M4 |Intermediate values|Used for |

| |(3,4,5) |M6,M8 | |intermediate |

| |(5,6,7) | | |judgments |

| |(7,8,9) | | | |

Source: [8].

1 Construction of fuzzy judgment for AHP. Based on the hierarchy built in step one and step two fuzzy scale is applicable to the construction of the parent trial. The hierarchy of criteria and alternatives is the subject of pairwise comparison for AHP. After building the nest, the team responsible for making the decision has to compare the elements in given levels to estimate their relative importance in relation to the top-level element.

To do this, the triangular numbers (M1 - M9) is used to express preferences between different criteria with respect to the goal. For example, if i think that the element is strongly preferred to item j with respect to the goal, then a ij = (4, 5, 6) rating is set; Comparison of element j with respect to element i must be reversed so that the judgment is consistent and should express aji = (1/6, 1/5, 1/4). From these scores the first comparison matrix is obtained by pairs between criteria with respect to the goal.

Apart from this matrix must be constructed matrices pairwise comparison for each of the levels of the tree hierarchy, that is, comparison matrices between the sub with respect to each of the criteria and the alternatives in relation to the sub . But the dynamic construction is the same as above.

4 Math Operations. Once the pairwise comparison matrices are constructed, the calculations must be made relevant to the development of the methodology, which are: the calculation of the weight vectors for each level of the hierarchy using the extended analysis and comparison principles of fuzzy numbers.

3 Model Elimination et Choixtraduisant the Realité (ELECTRE)

The process to be performed when using this model or technique must take into account six steps needed to end up having a ranking of alternatives, which are:

1 Defining the problem(finite set of alternatives, criteria, weights). It must identify what will make the alternatives is a finite set and evaluation criteria which aim to prioritize each of the alternatives, where the alternatives are the rows of our matrix and criteria columns, obtaining the matrix criteria alternatives NxM..

The weights associated with each of the criteria and scales measuring qualitative and / or quantitative. Remember that not all criteria necessarily have the same specific weight to the decision maker, so you have to assign a value. Also, not all aspects can be measured with the same measurement scale and thus may also have different ranges.

3 Filling the matrix-criteria alternatives. Following the evaluations for each of the alternatives based on the various criteria set are captured. These can be obtained by conducting various studies, such as surveys, expert opinion, simulations, among others.

4 Generación de la matriz de concordancia (medida ordinal). Teniendo las evaluaciones e de la matriz de alternativas- criterios se construye la matriz de concordancia. Esta matriz expresa qué tanta preferencia hubo en las evaluaciones de las alternativas con base en los criterios establecidos.

5 Generatión discordance matrix (cardinal measure). From data-alternatives matrix mismatch criteria matrix is constructed. This matrix expresses how much indifference was in evaluations of the alternatives based on the criteria.

6 Analysis on classification relationships. An analysis of the information on classification using the concordance and discordance matrices using the following rule is:

An element ak R (on classified or dominates) to another if it meets:

There is an indicator of most criteria for which we can say that k is at least as good as the. (Concordance index).

No criteria disagreed with this mostly shows a too strong superiority that is better than ak. (Discordance index).

To understand the concept of superiority or two most known parameters are set: matching parameter p, and q parameter mismatch.

11 Ranking of the alternatives. Finally, after performing sensitivity analysis with several different pairs of parameters p and q, and taking several parametric graphs associated with each of the analyzes, a conjunction of them is made and alternatives are ranked, expressing a graph synthesis..

4 Model Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)

The following process describes the actions and procedures necessary for data processing regardless of the form of how the data were obtained, based nxm matrix where n is the number of dimensions and m the amount of data in that dimension . The model is divided into the following stages:

2 Calculate Differences Matrix. A comparison of data was performed by comparing pairs a row of the matrix with all the others, here you should consider whether each of the variables m are to be maximized or minimized..

Should Maximize, If the data to compare is greater than or equal compared to the data, we take the data to compare. If MINIMIZE; If the data to be compared is less than or equal compared to the data, the data is stored to compare. As a result the matrix all the differences, where each row of the matrix are pairwise comparisons of the original matrix is ​​obtained.

4 Calculate Preference Function. At this stage the difference matrix is used and one of the six criteria of preference applies to each of the columns of the array or variables m, it should be noted that the value 0 means that the data is irrelevant and that one is strictly preferred value. Within this stage is used each q, p, σ thresholds according to the type of function or preference criterion used. Results in a matrix is ​​obtained with outcomes between 0 and 1.

5 Calculate Preferred Indices. This is when using weights or importance levels established by the decision maker is made, the procedure to be performed is to take the preference matrix and multiply each data value of each assigned weight. For each row of the matrix preferably a sum of their weight and multiplied data is performed and is stored in the preference matrix index. As a result a square matrix with zeros in the diagonal and preferably indicies of each row of the matrix is obtained preferably..

6 Overcoming Calculate Positive Cash Flow. An array is created from the values ​​of the array indices preferably by adding the values ​​of each of the rows of the matrix and placing the value of the sum at each array dimensions. At the end an arrangement of a dimension n according to the number of row of the matrix is obtained preference index. The aim of this process is to obtain the positive expressing outranking flow as an alternative to dominate all other.

7 Calculate Flow Negative improvement. An array is created from the values ​​of the array indices preferably by adding the values ​​of each of the columns of the matrix and placing the value of the sum in each array dimensions. At the end of an array of dimension m according to the number of columns of the matrix preference index is obtained. The aim of this process is to obtain the negative expressing outranking flow as an alternative to it sobrepujada or exceeded by all the others.

8 Get Net Flow of Accomplishment or Full Ranking. Is obtained by performing a pre subtracted from positive to negative flow minus flow, obtaining a settlement of the amount of alternatives and their value to the decision making process according to the concerns as PROMETHEE II model, which states that all alternatives are comparable and that the resultant information may be moot because information is lost by considering only the differences. The higher the net flow is the best alternative.

5 Requirements Analysis.

After making a theoretical review of the 4 models of multiple criteria decision making (AHP, Fuzzy AHP, and Promethee Electre) proceeds to establish the necessary requirements to be met by each of the web services for the calculation of the territorial vulnerability noteworthy are a total of 4 web services each corresponding to a model of decision making, in Table III the list of associated functional requirements shown and identified with the design, creation and operation of web services to calculate territorial vulnerability index.

FUNCTIONAL REQUIREMENTS

|ID |NAME |

|RF01 |READ XML FILE |

|RF02 |PROCESS INFORMATION AND GENERATE |

| |VULNERABILITY INDEX FROM THE AHP MODEL |

|RF03 |PROCESS INFORMATION AND GENERATE |

| |VULNERABILITY INDEX FROM THE MODEL |

| |PROMETHEE II |

|RF04 |PROCESS INFORMATION AND GENERATE |

| |VULNERABILITY INDEX FROM FUZZY AHP MODEL |

|RF05 |PROCESS INFORMATION AND GENERATE |

| |VULNERABILITY INDEX FROM THE MODEL |

| |ELECTRE III |

|RF06 |GENÉRATE XML FILE. |

6 Use Case Diagrams.

Following the process of AUP methodology (Agile Unified Process) is proposed for the development of web services implementing different diagrams in the UML standard. In this project the use case diagrams, class diagrams and sequence diagrams are provided.

1 Diagram Use Case Diagram Use Case is defined by the abstracted functional requirements of the document review of each decision making models adapted to calculate the index of territorial vulnerability.

[pic]

Use Case Diagram.

MODEL FOR THE CALCULATION OF VULNERABILITY INDEX FROM HIERARCHICAL DECISION MODELS AND HEURISTICS.

After the analysis regarding the operation of models in multicriteria decision-making in order to calculate the rate of territorial vulnerability to further define the functional requirements and use cases associated with the development of web services, we proceed to propose the model dynamic and static system.

1 Dynamic System Model.

The sequence diagram is responsible for showing the steps, the process or interaction between objects, which represents the sequence of messages between instances of classes, components, subsystems and actors [10].

The sequence diagram is depicted in Figure 6 shows the overall interaction process that makes each of the web services to be developed, it should be noted that according to the type of model (AHP, FUZZY AHP, PROMETHEE, ELECTRE), there may be more than one iteration between classes "Model", a process that does not alter the overall process and the end result or purpose of the web service.

[pic]

Sequence Diagram.

2 Static Model System.

The class diagram is responsible to describe and / or show the structure of the system showing its classes, interfaces and / or objects. Within this type of model attributes, associations and generalizations of each class is also shown [10].

The following class diagram shows the general structure is developed as web services. In the general structure shown three packages, webService wherein the method is exposed in the web service with their respective interfaces, the package that contains useful utilities web service that are read and create the XML, and package model, in which all classes are associated with each model in Figure 7 the overview diagram of all classes to develop web services shown.

[pic]

Class Diagram.

PROTOTIPO PARA EL CÁLCULO DEL ÍNDICE DE VULNERABILIDAD A PARTIR DE MODELOS DE DECISION MULTICRITERIO.

Within the process for calculating the index of territorial vulnerability shows a view as developed in Figure 8, for this run each of the web services associated to each decision models. In this view or screen is obtained as a result the XML generated by the application in text format.

[pic]

Initial Vista Web Services.

In the normal web services to run through sight or front end developed in order to test the performance of each of the web services process, view some validations are performed, as shown in Figure 9, between which they are not selected model or web service to use or errors in reading or reading the XML file does not contain the name envio.xml regardless of uppercase and / or lowercase file path.

[pic]

Validations screen WS

Below successful by running each of the web services and the response result of each sample, showing in the result, the rate of territorial vulnerability and vulnerability index for each of the dimensions. In Figure 10 the execution of one of the web services associated with one decision models and XML shown.

[pic]

AHP model execution.

[pic]

XML result of AHP model execution.

To perform the test operation of web services associated with decision models already known (AHP, Fuzzy AHP, Electre, Promethee) and view or front-end developed to show the execution of web services installed or deployed each one of the component services and view an application server (Oracle WebLogic). Keep in mind that these web services can be installed on another application server following the installation manual provided by each developer and / or manufacturer.

CONCLUSIONS

The treatment was given to the data collected and analyzed in the first phase of "Retrospective of Natural Disasters in Colombia As Input For Building a Decision Support System" project was key to be used as input to the system developed, as it has different types of data (index, indicators), so it became necessary to investigate what each meant.

The calculation of the territorial vulnerability is evident with each of the implemented algorithms that represent the entities and attributes of each model required to show the final result, supported and based on the documentation of each decision making model.

The structure of the proposed architecture for the development of Web services, intended as the basis for future development and implementation of other decision-making models, since there are many. This aims to provide diversity to the person skilled in decision making to compare and evaluate the results generated by various models and analyze the territorial vulnerability index generated by each of them, in order to take appropriate actions to help mitigate the impact of natural disasters in Colombia.

References

1] X. Yua, Z. XUB y Y. MAC, Prioritized Multi-Criteria Decision Making Based on the Idea of PROMETHEE. En: SciVerse ScienceDirect. Procedia Computer Science 17 (2013) 449 – 456. p. 1-2

2] H. ZHAOA, Y. PENGA y W LIA, Revised PROMETHEE II for Improving Efficiency in Emergency Response. En: SciVerse ScienceDirect. Procedia Computer Science 17 (2013) 181 – 188. p. 1.

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4] K. Evi, T. S. Alexander y A. A. Okti, Land acquisition and resettlement action plan (LARAP) of Dam Project using Analytical Hierarchical Process (AHP): A case study in Mujur Dam, Lombok Tengah District-West Nusa Tenggara, Indonesia. En: SciVerse ScienceDirect. Procedia Environmental Sciences. 17 (2013) 418 – 423; p 419. [citado en 17 de Abril de 2014] Disponible en ScienceDirect Database.

5] DAS, Manik et al. Framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology. En: SciVerseScienceDirect. Socio-EconomicPlanningSciences 46 (2012) 230-241; p 231. [citado en 17 de Abril de 2014] Disponible en ScienceDirectDatabase.

6] L. Dean, Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise. Massachusetts: Addison-Wesley Professional, Primera Edición. Año 2001. p8.

7] S. Thomas, The analitic Hierarchy and analytic network processes for the measurement of intangible criteria and for decisión-making. Multicriteriadecisión analysis. State of the art surveys. Springer International Series. Springer science + business media. Inc. 2005.

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9] H. U, María Fernanda, O. G. Juan Carlos, Modelo para la gestión de proveedores utilizando ahp difuso En: Estudios Gerenciales [en línea]. 2006, (abril-junio) [citado 30 de Febrero de 2014].

10] Construx Software. Object Modeling with UML. [Citado 20 Abril, 2014]. Disponible en internet:

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