DECISION-MAKING SUPPORT METHOD FOR ISSUING WARNINGS AND SELECTION OF MITIGATION ACTIONS PARAMETERIZED BY WEATHER-CLIMATE DECISION INDEX BASED ON USER PREFERENCES

Abstract

A decision-making support method is presented for issuing warnings and selecting mitigation actions parameterized by the turn of weather and/or climate information into a single decision index. A decision-making support method was developed from the Global Weather Decision Index (WDI) or Climate Decision Index (CDI), which is based on user preferences in relation to three characteristics of weather-climate information: a) value of the weather-climate variable; b) probability of occurrence; and c) lead-time of weather-climate information. The presented embodiments were initially developed having as the field of application the area of aerospace meteorology, as motivation the rockets launch operations in space centers. However, the decision-making process under weather uncertainty is relevant in other applications where weather or climate conditions may cause some kind of impact on activities.

Claims

1. The method for decision-making support for issuing warnings and selection of mitigation actions is characterized by the global weather-climate decision index and comprises four steps, these being: step 1 (101): structure of the decision-making problem using weather-climate information; step 2 (102): construction of value functions, partial indexes and turning weather-climate information into a Global Decision Index based on the users preferences regarding three characteristics of weather-climate information: a) value of the weather-climate attribute; b) probability of attribute occurrence; and c) lead-time of weather-climate information; step 3 (103): development and parameterization of the Weather-Climate Decision Support Method (WCDSM); and step 4 (104): Results and Recommendations, this step comprising: a) levels for issuing warnings (105); and/or b) selection of mitigation actions/portfolios (106).

2. The method for decision-making support according to claim 1, characterized in that step 1 (101) is the initial problem structuring and comprises three sub-steps: a) Interviews with actors, stakeholders and decision makers (201); b) Identification of vulnerabilities, risks and impacts (202); c) Definition of attributes and operational thresholds (203).

3. The method for decision-making support according to claim 2, characterized in that the sub-step of Interviews with actors, stakeholders and decision makers (201) engages in interaction with the decision makers/users involved in the decision-making context to establish preferences and model the structure of judgments.

4. The method for decision-making support, according to claim 2, characterized in that in the sub-step of Identifying the vulnerabilities, risks and impacts (202) is performed from the personal or user groups interviews (201), identifications of all the vulnerabilities, risks and their respective impacts related to weather-climate conditions.

5. The method for decision-making support, according to claim 2, characterized in that the relevant weather variables, considered as the attributes of the decision-making model, are defined in the sub-step of Definition of the attributes and operational thresholds (203) and the operational threshold levels of these attributes are defined, as well as the user's preferences regarding the probabilities and lead-time of weather-climate information are identified through their respective operating thresholds.

6. The method for decision-making support according to claim 5, characterized in that said operational thresholds (L) are divided into two categories, being: the first operational threshold L.sub.1 characterized as the best level (L.sub.1=1); and the second operational threshold L.sub.2 characterized as the worst level (L.sub.2=0); and for the construction of the weather-climate hazard classification table an intermediate operational threshold level (L*) was defined, considering the variable value where the value function is equal to zero point five (average between L.sub.1 and L.sub.2, L*=0.5).

7. The method for decision-making support according to claim 1, characterized in that in step 2 (102) the partial value functions of the information characteristics are constructed together with the user, step 2 comprising three sub-steps: a) Construction of the value functions of weather-climate information characteristics and calculation of partial weather-climate decision indexes (204); b) construction of weights between attributes (205); c) calculation of the global Weather Decision Index (WDI) or Climate Decision Index (CDI) (206).

8. The method for decision-making support according to claim 7, characterized in that in the sub-step of Construction of the partial Weather-Climate Decision Index functions (204) are established from three distinct value functions which, aggregated into a single index, called partial Weather Decision Index (WDI) or partial Climate Decision Index (CDI). These functions establishing for the specific aspects of weather-climate information a value on the scale [0,1], according to the user profile and behavior, being defined that the anchor values for the construction of the functions are the operational threshold (L).

9. The method for decision-making support according to claim 8, characterized in that the three functions are: partial value function integrating the function of the Weather Decision Index (WDI) or Climate Decision Index (CDI) related to the probability of the weather-climate information (I.sub.p); partial value function integrating the function of the Weather Decision Index (WDI) or Climate Decision Index (CDI) related to the lead-time of the information (I.sub.t); partial value function integrating the function of the Weather Decision Index (WDI) or Climate Decision Index (CDI) related to the set of weather-climate attributes selected by the user (I.sub.x).

10. The method for decision-making support according to claim 9, characterized in that the weather-climate decision index for the attribute x (wdi.sub.x or cdi.sub.x); value function for attribute x (I.sub.x); value function for the probability p of the information (I.sub.p); value function for the lead-time t of the information (I.sub.t); adjust parameter (p), the construction of the Weather Decision Index (WDI) or Climate Decision Index (CDI) function, adopt the following assumptions: the preferences of the probability p and the lead-time t are the same for any attribute x; I.sub.p or I.sub.t=0.fwdarw.wdi or cdi=1; I.sub.p and I.sub.t=1.fwdarw.wdi or cdi=I.sub.x; I.sub.p and I.sub.t=1, and I.sub.x=0.fwdarw.wdi or cdi=0; and wdi or cdi=f(I.sub.p, I.sub.t, I.sub.x) for every attribute x, from the aggregation into a single value being defined the partial Weather Decision Index (WDI) or Climate Decision Index (CDI) function for each attribute, with weather-climate decision index (wdi.sub.x or cdi.sub.x) for attribute x and adjustment parameter =0.5 to be based on the equation:
wdi.sub.x or cdi.sub.x=I.sub.x+(1I.sub.x)(1(I.sub.pI.sub.t).sup..

11. The method for decision-making support according to claim 7, characterized in that, in the sub-step of constructing weights among the attributes (205).

12. The method for decision-making support according to claim 7, characterized in that the value of the global WDI and/or CDI function is defined in the sub-step of the global Weather Decision Index (WDI) or global Climate Decision Index (CDI) (206), incorporating all the variables selected by the user, and considering the weather-climate information set t the global WDI and/or CDI function is constructed from the comparison of the effects among the attributes and can be determined by: $\mathrm{DI}\left(t\right)=\underset{x=1}{\overset{n}{.Math.}}.Math.k_x.Math.{\mathrm{di}}_x\left(t\right)$ where di.sub.x(t) being the (partial) wdi and/or cdi value of each attribute x in condition t and k.sub.x being the attribute weight, being: $\underset{x=1}{\overset{n}{.Math.}}.Math.k_x=1$ the global WDI and/or CDI function is an additive value function that determines the total values of each weather-climate condition, in which the recommended option for the user will be the alternative that obtains the numerical result according to the levels of warnings previously established and/or classification of decision alternatives.

13. The method for decision-making support according to claim 1, characterized in that Step 3 (103) is performed by parameterizing the Weather-Climate Decision Support System and comprises three sub-steps: a) Defining scenarios of unfavorable weather-climate events (207); b) Identification of levels for issuing warnings and selection of portfolios (208); c) Evaluation of performance in issuing warnings and selection of portfolios (209); where, at the sub-step of defining scenarios of unfavorable weather-climate events (207), the potential adverse/extreme weather-climate scenarios are identified and classified, and said scenarios are determined by varying the attributes values, based on the weather-climate hazard levels; at the sub-step Identifying the classes for issuing warnings and selecting the portfolios (208), by using the variation of the attribute values and, respectively, the minimum and maximum WDIs or CDIs for each scenario, the respective values of the multi-attribute function for each level of warning and/or decision alternative are identified; at the sub-step performance evaluation in the selection of warnings levels and portfolios (209) an evaluation of the results is performed employing a sensitivity analysis.

14. The method for decision-making support according to claim 1, characterized in that Step 4 (104) results and recommendations comprise the two types of possible operational results to be obtained: a) issuing warnings: classification and recommendation for issuing warnings by weather-climate information (105); and/or b) mitigation portfolios: classification and recommendation of mitigation actions by weather-climate information (106); where, the two types of operating results are dependent on the decision-making context using weather-climate information and the specific demands of stakeholders/users related to the problematic situation.

15. The method for decision-making support according to claim 1, characterized in that the in its general structure using Weather Decision Index (WDI) or Climate Decision Index (CDI), the information input (401) can be entered into the system by several categories of information being: real-time weather observations (402), weather forecast (403), seasonal climate prediction (404) and climate change forecast (405), from which information is evaluated values of the attributes and characteristics of the information (406) and individually transformed (407, 408 and 409) into a value function, from which said value values for each attribute are calculated the values of the partial WDI or CDI, by equation wdi.sub.x or cdi.sub.x=I.sub.x+(1I.sub.x) (1(I.sub.p I.sub.t).sup.) and the previously established weather-climate scenarios S (417) and the respective levels of warnings and/or decision alternatives (410, 411, 412 and 413) are also incorporated; from the partial IDM or IDC functions of each variable, the calculation of the multi-attribute WDI and/or CDI Function is performed using the equation: DI(t)=.sub.x=1.sup.nk.sub.xdi.sub.x(t) (414), the results are being presented according to the user's preferences, being able to be a classification with different levels of warnings in case of adverse/extreme weather-climate conditions, by the recommendation in the issue warnings by category in the lead-time information (105) and/or the classification for action selection/mitigation portfolio, by the recommendation of mitigation actions in the lead-time of the weather-climate information (106).

Description

BRIEF DESCRIPTION OF DRAWINGS

[0038] To describe the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings required for describing the embodiments. The accompanying drawings in the following description show merely some embodiments of the present application.

[0039] FIGS. 1 and 2 show an overview of the development of the Weather-Climate Decision Support Method (WCDPM), with sequential steps using the Weather Decision Index (WDI) or Climate Decision Index (CDI).

[0040] FIG. 3 shows the dimensional space of the WDI or CDI function, where it is possible to identify the possible values of Equation 1 (WDI or CDI[0.1]).

[0041] FIG. 4 is a flowchart of the general structure of the WCDSM using the WDI or CDI Function.

[0042] FIG. 5 shows the operational threshold defined by the user for the application example of this application, for each weather variable, for the probabilities and the weather forecast lead-time.

[0043] FIG. 6 is a table with the classification of the weather hazard levels (only for the value of the variable).

[0044] FIG. 7 illustrates the probability-related value function of the weather forecast for the application example of the present application.

[0045] FIG. 8 shows the value function relative to the weather forecast lead-time for the application example of the present application.

[0046] FIG. 9 shows the value function for the rain of the application example of the present application.

[0047] FIG. 10 illustrates the value function for the wind speed of the application example of the present application.

[0048] FIG. 11 shows the hierarchical structure of the decision problem with the two weather attributes (rain and wind) and the respective weights, for the application example of the present application.

[0049] FIG. 12 is a table with the classification of levels for issuing warnings in cases of adverse/extreme weather events in the application example of the present application.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] Decision-making support method for issuing Warnings and Selection of Mitigation Actions parameterized by Weather-Climate Decision Index follows four (4) steps:

[0051] Step 1 (101): Decision Problem Structuring that uses the weather-climate information;

[0052] Step 2 (102): Construction of value functions, partial indexes and turns weather-climate information into a Global Decision Index (multi-attribute);

[0053] Step 3 (103): Development and parameterization of the Weather-Climate Decision Support Method (WCDSM); and

[0054] Step 4 (104): Results and recommendations, the step of which may comprise:

[0055] a) Levels for issuing warnings (105); and/or

[0056] b) Selection of mitigation actions/portfolios (106).

[0057] Step 1 (101) comprises three (3) sub-steps: a) Interviews with actors, stakeholders and decision makers (201); b) Identification of vulnerabilities, risks and impacts (202); c) Definition of variables (attributes) and operational thresholds (203).

[0058] Step 2 (102) also comprises three (3) sub-steps: a) Construction of the value functions of weather-climate information characteristics and calculation of partial weather-climate decision indices (204); b) Construction of weights among weather variables (205); c) Calculation of global or multi-attribute Weather Decision Index (WDI) or Climate Decision Index (CDI) (206).

[0059] Step 3 (103), comprises three (3) sub-steps: a) Definition of scenarios of adverse weather-climate events (207); b) Identification of classes for issuing warnings and portfolio selection (mitigation actions) (208); c) Evaluation of performance in issuing warnings and selecting portfolios (209).

[0060] Finally, Step 4 (104), which comprises of two (2) situations: a) Issuance of warnings: Classification and recommendation for issuing warnings by weather-climate information (105); and b) Mitigation Portfolios: Classification and recommendation of mitigation actions by weather-climate information (106).

[0061] FIGS. 1 and 2 show the 4 steps of WCDSM development.

[0062] The following is a more detailed description of said steps.

[0063] Initially to establish preferences and model the structure of judgments, it is necessary interaction with the stakeholders/users involved in the decision-making context, considered as an initial structuring step of the problem (101). Based on users' personal or group interviews (201), all vulnerabilities, risks and their related impacts on weather-climate conditions (202) are identified. Next, the relevant weather variables, considered as the attributes of the decision model, are established and the levels of the operational thresholds (203) of these attributes are defined. This sub-step also identifies the user's preferences regarding the probabilities and lead-time of weather-climate information through their respective operational thresholds. These operational thresholds are divided into two categories: the first operational threshold (L.sub.1), considered the ideal value, where for the user there is no restriction due to weather-climate conditions. Therefore, it is characterized as best level (L.sub.1=1). The second operational threshold (L.sub.2) is the value that, despite adverse weather-climate conditions, can still be considered acceptable to the user, so that it will be considered as the worst level (L.sub.2=0). For the construction of the classification table of weather-climate hazards, a level of Intermediate operational threshold (L*) was defined, considering the value of the variable where the value function is equal to zero point five (average between L.sub.1 and L.sub.2, L*=0.5), as will be demonstrated in the application example of the present patent.

[0064] The second step is the process of changing weather-climate information into a decision index (102). The value functions for each characteristic of the information (probability, lead-time and of each selected variable) are constructed together with the user. In this process three distinct value functions are established which, aggregated into a single index, are called the partial Weather Decision Index (WDI) or partial Climate Decision Index (CDI) (204). These functions establish for the specific aspects of weather-climate information value on the scale [0,1], according to the user' behavior and profile. In the WCDSM it is defined that the anchor values for the construction of the functions are the operational thresholds (L) previously described. Among the anchor values, linear or more complex functions can be applied, such as logistic or exponential curves, to adequately represent the users' profile with the scale of [0,1].

[0065] The first partial value function that integrates the WDI or CDI function is related to the probability of the weather-climate (I.sub.p) information. The second value function is in relation to the lead-time or time of the information (I.sub.t). The third value function is related to the attributes, that is, the set of weather-climate variables selected by the user (I.sub.x). For the construction of the WDI and/or CDI Function, some assumptions were adopted, which:

[0066] i. The preferences of the probabilities p and the lead-time t are the same for any variable x

[0067] ii. If I.sub.P or I.sub.t=0.fwdarw.wdi or cdi=1

[0068] iii. If I.sub.P and I.sub.t=1.fwdarw.wdi or cdi=I.sub.x

[0069] iv. If I.sub.P and I.sub.t=1 and I.sub.x=0.fwdarw.wdi or cdi=0

[0070] Therefore, the innovation of this patent is: being wdi or cdi=f (I.sub.p, I.sub.t, I.sub.x) for every attribute x, from the aggregation into a single value is defined the function of partial Weather Decision Index (WDI) or Climate Decision Index (CDI) for each variable (attribute). According to the assumptions presented above, a general equation was developed for the decision index function for each specific attribute (Equation 1):

wdi.sub.x or cdi.sub.x=I.sub.x+(1I.sub.x)(1(I.sub.pI.sub.t).sup.)(1)

[0071] where:

[0072] wdi.sub.x or cdi.sub.x=weather-climate decision index for the attribute x

[0073] I.sub.x=value function for the attribute (variable)

[0074] I.sub.p=value function for the probability p of the information

[0075] I.sub.t=value function for the lead-time T of the information

[0076] =adjustment parameter (=0.5)

[0077] FIG. 3 shows the dimensional space of the WDI or CDI Function, where it is possible to identify the possible values of Equation 1 (301) according to the lead-time information (303). It is also possible to observe the curves of the assumptions adopted ii (302) and iii (304).

[0078] In the change step (102), weights or attribute weights (205) are also identified, since the WDI or CDI is characterized as a multiple criteria decision, since there is more than one weather-climate attribute in the decision-making context. The weights can be determined by several methods (trade-off method, peer-to-peer comparison, swing weights, among others). In the application example of this Patent, it will be described in detail how determining the weights by one of these approaches.

[0079] For the development of the global WDI or CDI function, where all attributes (variables) are considered, the concepts of Multi-attribute Decision Analysis with single criterion of synthesis described in the books Multiple Criteria Decision Analysis: An Integrated Approach, by Belton and Stewart (2002); and The Knowledge and Use of Multicriteria Decision Aid Methods, by Adiel T. Almeida (2011) were applied. Therefore, the value of the global or multi-attribute WDI or CDI function (206) is defined, incorporating all weather-climate variables selected by the user.

[0080] Considering the weather-climate information set t (observation+forecast) the global or multi-attribute WDI or CDI (or only DI) function was constructed from the comparison of the effects among the variables and can be determined by Equation 2:

DI(t)=.sub.x=1.sup.nk.sub.xdi.sub.x(t)(2)

[0081] where:

[0082] di.sub.j(i) is the (partial) wdi or cdi value of each attribute x in the condition t

[0083] k.sub.x is the attribute weights, where .sub.x=1.sup.nk.sub.x=1

[0084] The global or multi-attribute WDI or CDI function is an additive value function that determines the total values of each weather-climate condition, in which the recommended to the user will be the option that obtains the numerical result according to the levels of warnings previously established (e.g.: severity class) and/or ranking of decision alternatives (e.g.: mitigation actions).

[0085] In the parameterizing step of the Weather-Climate Decision Support Method (WCDSM) (103), it is necessary to establish and classify the potential adverse weather-climate scenarios (207). The construction of scenarios can be performed from several approaches, such as the use of climatological data, event registration, scenario planning techniques, among others. In the WCDSM the scenarios are determined by varying the values of the attributes, defined by the level of weather hazards (FIG. 6). Once potential weather-climate scenarios have been established, it is possible to determine with the users the classification of warning levels and/or the respective decision alternatives. Using the variation of the values of the attributes and respectively of the minimum and maximum partial WDIs or CDIs for each scenario, the respective multi-attribute function values can be identified for each warning and/or alternative decision level (208). Subsequently, an evaluation of the results is performed using a Sensitivity Analysis (209), that is, to evaluate if the weights and results are robust. In the application example of this Patent, further details of the development and parameterization of the WCDSM will be presented.

[0086] The WCDSM proposal is to obtain two types of operational results: 1) decision support in the classification and issuance of adverse/extreme weather warnings (105); 2) decision support for classification and selection of mitigation actions (106). These applications are dependent on the decision-making context that uses weather-climate information and the specific users demands to the problematic situation. For example, the issuance of warnings in case of high-intensity rainfall in the short term has different characteristics in relation to the warnings to a dry season climate prediction for several months.

[0087] FIG. 4 shows the overall structure of the WCDSM proposal using the Weather Decision Index (WDI) or Climate Decision Index (CDI) of this Patent. The input information (401) can be entered into the system through several categories, including: real-time weather observations (402), weather forecast (up to 10 days) (403), climate prediction/prognosis (months) (404) and prediction related to climate changes (several years/decades) (405). From this information, the values of attributes (variables), information characteristics (406) and individually transformed (attribute 1 (407), attribute 2 (408) and attribute n (409)) are evaluated in one of the value functions described above. From these value functions for each attribute, the partial WDI or CDI values can be calculated by Equation 1. The previously established weather-climate scenarios S (417) and the respective warnings and/or decision alternatives (410) (mitigation action/warning level 1 (411), mitigation action/warning level 2 (412) and mitigation action/warning level M (413)) are also incorporated in the system. From the partial WDI or CDI functions of each variable, the calculation of the multi-attribute WDI or CDI function is performed using Equation 2 (414). The WCDSM (414) presents the results, according to the user's preferences, and maybe the classification with different levels of warnings in case of adverse/extreme weather-climate conditions (recommendation in issuing category warnings in the lead-time the information) (105) and/or the classification for selection of mitigation portfolio (recommendation of mitigation actions in the lead-time information) (106).

[0088] Example of the Application

[0089] As way of demonstration (hypothetical example), a decision problem of issuing warnings for an extreme weather event will be used, using only two weather attributes (rain and wind). FIG. 5 shows the preferences indicated by a theoretical user about the operational threshold of the variables, probabilities and weather forecast lead-time (501), with the respective dimensional units (502). In column (503) are the best threshold, or the ideal values. In column (505) are the worst threshold when conditions are adverse but still acceptable to the user. Column (504) is the value L*, being valid only for the weather variables, as previously indicated.

[0090] FIG. 6 shows the weather hazard classification table, with four hazard levels (601) and respective value scales (602). Also, in Step 2 of transforming the weather forecast into an index, we have the first value function of this demonstration, related to the probability of the weather information. FIG. 7 shows the scale of the value function (I.sub.p) from 0 to 1 (701) and the probability scale, 0% to 100% (702). Values above 85% are considered to be the best level (=1) (705) and 20% is considered the worst level (=0) (703). In this demonstration, a linear function was adopted between the two anchor values (704). The mathematical expression for the probability value function (p) is given in (706).

[0091] The second value function is in relation to the lead-time of the weather information. FIG. 8 shows the scale of the value function (801) of the weather forecast lead-time, from 0 to 24 hours (802) and the anchor values, being for the terms of up to 2 hours, is the best (=1) (803) and above 24 hours, the worst level (=0) (805). Also adopted a linear function between the two levels (804). The mathematical expression for the value function for the lead-time (t) is given in (806).

[0092] The value function for the rain attribute is shown in FIG. 9, according to the operational threshold previously defined in FIG. 5. Thus, we have the scale of (I.sub.r) (901), the precipitation value (902), the best level (903), the worst level (905) and the respective linear function (904). The mathematical expression for the rain value function (r) is given in (906). The function for the wind speed attribute is shown in FIG. 10, also according to the operational threshold previously defined in FIG. 5. We have the scale of (I.sub.w) (1001), the wind speed (1002), the best level (1003), the worst level (1005) and the respective linear function between the two anchor values (1004). The mathematical expression for the wind speed value function (w) is given in (1006).

[0093] FIG. 11 illustrates the Hierarchical Structure between the two attributes, with the respective weights. In this demonstration, the decision problem is the issue of warnings (1101), considering the rain (1102), with a weight of 0.7 (1104) and the wind speed (1103), with a weight of 0.3 (1105). In order to identify the weights among the attributes in this demonstration, we used the Swing Weights approach, described in Decision and Risk Analysis for the evaluation of Strategic Options by Montibeller and Franco (2007) and also in Treatment of uncertainty through the Interval Smart/Swing Weighting Method: a case study by L. Gomes et al. (2011). This technique establishes a numerical index associated to the preferences among the attributes. That is, a way to determine the order of importance of the weather attributes, adopting a scale of value from 0 to 100, being the highest value the most important. The values in the scale are indicated for the different attributes, analyzing which is the preference for the user and establishing an equivalent value for the others, in relation to the first one. In this way, it is identified how much the user is prone to replace in one attribute to gain in another. Finally, once all the values in the scale for the attribute set have been established, the weight of each variable and the respective performances in each alternative are calculated.

[0094] From the variation in the values of the two attributes between the operational thresholds (FIG. 5), it is possible to question to the user what would be the decision if the weather scenario occurred for each of the levels of hazards (FIG. 6). In this demonstration, shown in FIG. 12, three levels of warning (1201) and the respective threshold for global WDI values (1202), according to the calculation using Equation 2.

[0095] As an illustration of the memory calculation of this demonstration, we have: the two attributes at the high weather hazard level (L*x<L.sub.2), i.e. the rainfall between 10.5 and 20 mm/h and wind velocity between 25 and 40 m/s. It is considered a weather forecast with probability >85%, lead-time <2 h (hence we have I.sub.p=1 and I.sub.t=1) and the values of the two attributes equal to L*(r=10.5 mm/h=25 m/s). Using Equation 1, we determined the partial WDIs for each variable, with I.sub.r=0.5 and I.sub.w=0.5. Therefore, the value of the Multi-attribute WDI Function according to Equation 2 and FIG. 11 will be WDI=0.5. Thus, in this demonstration, when the two attributes are at the high weather hazard level, the decision recommendation is to issue a severe/extreme red warning (FIG. 12).

[0096] Although some preferred embodiments of the present application have been described, persons skilled in the art can make changes and modifications to these embodiments once they learn the basic inventive concept. Therefore, the following claims are intended to be construed as to cover the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0097] Obviously, persons skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. The present application is intended to cover these modifications and variations provided that they fall within the scope of protection defined by the following claims and their equivalent technologies.