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The present invention provides a rule based process control system which automatically learns how to control a process by inducing control rules from the process as it changes. During process operation data samples from the process are collected which include the controllable variables in the process along with the dependent variable (goal or goals) of the process. If a sample is not predicted by the current rules that control the process, new rules are induced from the new sample and previously collected samples by creating a decision tree. The controllable variable having the greatest effect on the goal occupies the highest tree node. New rules are produced from the decision tree which predict process behavior based on the new and old samples. From these new rules, control rules which satisfy system constraints and improve on the goal(s) are selected. The control rules are then used to control the process until another sample is not predicted by the new rules. In this manner, data samples are collected and saved which represent the possible states of the process and from which rules for controlling the process are produced. As the sample set grows, the domain in which the process can be predictably controlled also grows. If the sample set is allowed to grow infinitely, all the rules for controlling the system can be produced. As a result, the system learns how to control the process under varying conditions and induces rules that optimize the process toward the desired goal.
1. A method of controlling a process having a process goal and control settings, comprising the steps of:
(a) operating the process at current settings in accordance with a current rule;
(b) sampling the process and determining whether a current sample representing a current state of the process is predicted by the current rule;
(c) inducing a new rule when the sample is not predicted by the current rule and combining the current rule with the new rule to produce a combined rule;
(d) changing the control settings in accordance with the combined rule when the combined rule will move the process toward the goal; and
(e) operating the process at the changed control settings.
2. A method as recited in claim 1, wherein step (b) comprises the steps of:
(b1) comparing the current sample goal with a rule goal;
(b2) discarding the current sample if the current sample goal indicates the rule goal is satisfied; and
(b3) saving the current sample in a rule sample data set if the current sample goal indicates rule goal is not satisfied.
3. A method as recited in claim 2, wherein step (c) comprises the steps of:
(c1) applying a classification function to the rule sample data set to produce a decision tree;
(c2) generating a rule from the decision tree.
4. A process as recited in claim 3, wherein step (c1) comprises the steps of:
(i) calculating information gain for rule attributes using a weighting factor for each attribute;
(ii) selecting as a decision tree node the attribute with the greatest information gain;
(iii) subdividing the rule sample data set into subsets in dependence on the attribute selected; and
(iv) repeating steps (i)-(iii) for each subset.
5. A process as recited in claim 4 wherein step (c1) includes determining whether rules induction constraints have been met.
6. A process as recited in claim 4, wherein an attribute comprises one of a numeric control variable and a logical control variable.
7. A method as recited in claim 3, wherein step (d) comprises the steps of:
(d1) discarding rules which do not satisfy the goal;
(d2) discarding rules which do not satisfy system constraints;
(d3) selecting a rule which allows improvements in the goal; and
(d4) changing the control settings in accordance with a change control strategy for the rule selected.
8. A process control system apparatus using control settings and having a process goal, comprising:
process sensing means for sensing values of controllable variables in the process;
process control means for controlling the process in dependence on the control settings; and
control setting means for controlling the process in dependence on a current control rule having current control settings, inducing a substitute control rule including substitute control settings replacing the current control rule and current control settings when a data sample of the controllable variables is not predicted by the current control rule and outputting the substitute control settings to said process control means to control the process when the substitute control settings will move the process toward the goal.
9. An apparatus as recited in claim 8, wherein said control setting means stores plural data samples and includes:
classification means for applying a classification function to the data samples and producing a decision tree; and
means for generating a rule from the decision tree.
10. An apparatus as recited in claim 9, wherein said classification means includes information gain means for calculating the information gain of each controllable variable using a weighting factor for each variable and selecting attributes for nodes of the tree that provide the greatest information gain.
11. A method of controlling a process having control settings and a goal, comprising the steps of:
(a) operating the process at current control settings in accordance with a current rule;
(b) sampling the process and determining whether a current sample representing a current state of the process is predicted by the current rule;
(c) inducing a new rule if the sample is not predicted by the current rule by applying an information gain classification function to produce a decision tree;
(d) discarding rules which do not satisfy the goal and control setting change constraints and changing the control settings in accordance with the new rule; and
(e) operating the process at the changed control settings.
12. A process control system apparatus using control settings and having a process goal, comprising:
process sensing means for sensing values of controllable variables in the process;
process control means for controlling the process in dependence on the control settings; and
control setting means for controlling the process in dependence on a current control rule having current control settings, for inducing a substitute control rule, by applying a classification function to produce a decision tree and including substitute control settings, when a data sample of the controllable variables is not predicted by the current control rule and for outputting the substitute control settings to said process control means to control the process when the substitute control settings will move the process toward the goal.
13. An apparatus as recited in claim 12, wherein said classification means includes information gain means for calculating the information gain of each controllable variable using a weighting factor for each variable and selecting attributes for nodes of the tree that provide the greatest information gain.
1. Field of the Invention
The present invention is directed to an automated rule based process control method and apparatus that includes feedback and, more particularly, to a system in which rules concerning the operation of a process are inferred from data samples of the process being controlled, the rules are used to move the process toward the desired goal and as the process operates new data samples allow the inference of new rules, so that the process is automatically optimized and the rules for controlling the process are automatically generated.
2. Description of the Related Art
Process control when attempting to optimize toward a desired goal has, been characterized by statistical analysis methods which analyze the behavior of a process and attempt to predict process behavior when controllable variables of the process are modified. In processes such as performed in a chemical plant, the control variables relate to the chemical process under consideration such as water temperature and chemical flow rates. In a process such as a commercial or individual loan analysis system, the variables relate to economic considerations such as whether a bank reference is good or bad.
In prior art plant process control systems, plant experimentation programs have been used to find out what happens to a system when it is perturbed to allow characteristics which can lead to optimization to be discovered. Plant experimentation is a form of controlled interference used to learn more about the process without radically upsetting it. One approach to plant experimentation involves executing one or more sets of experiments in a limited amount of time to get process improvement information on selected sets of variables. The experimental case history is then analyzed using statistical analysis techniques such as a regression or correlation analysis to determine which variables significantly control a desired output such as yield. These prior art methods are very unwieldy in practice and result in an extended amount of time elapsing between the experiment and a determination to adjust the variables which control the desired output.
An object of the present invention is to provide a process control system which learns how to obtain a desired goal or set of goals from operating the process to be controlled.
Another object of the present invention is provide a system which includes feedback to a rules inference system to induce new rules for controlling the process toward the desired goal or goals.
An additional object of the present invention is to provide a rule set for controlling a process that has plural rules thereby providing multiple control strategies for the process.
A further object of the present invention is to provide a control algorithm which is automatically guided by the process being controlled.
A still further object of the present invention is to provide control rules which automatically change with time allowing the system to adapt to wear characteristics of the process, modifications or improvements in the process, a change in economic factors governing the process such as material costs, changes in the constraints such as the operating range of control variables, changes in external conditions and changes in the goals of the process without requiring that a new control system be produced from scratch.
Yet another object of the present invention is to provide a control algorithm which can produce control rules even when data are missing.
An object of the present invention is to provide the ability to control a process having both numeric and symbolic controllable variables or attributes.
An additional object of the present invention is to provide a control system that can balance plural goals.
Another object of the present invention is to provide a process control system with easily modifiable rules.
A still further object of the present invention is to provide a process control system that can control and balance plural outputs.
An object of the present invention is to provide a control system that does not require knowledge of the physics of the system being controlled.
The present invention obtains the above objects by providing a rule based process control system which learns how to control a process by inducing control rules from the process as it changes. During operation of the process, data samples are collected which include the controllable variables in the process along with the dependent variables (goals) of the process. If a sample is not predicted by the current control rules, new rules can be induced, depending on new rules production criteria, which predict the behavior of the process based on the new samples and the previous samples from which the current rule was produced. The new rules are then used to control the process until another sample is not predicted by the new rules. In this manner, data samples are collected and saved which represent the possible states of the process and from which rules for controlling the process are produced. As the sample set grows, the domain in which the process can be predictably controlled also grows. If the sample set is allowed to grow to the entire finite set which is representative of the total domain of system operation, all the rules for controlling the system can be produced. As a result, the system learns how to control the process under varying conditions and induces rules that optimize the process toward the desired goal.
These together with other objects and advantages which will be subsequently apparent, reside in the details of construction and operation as more fully hereinafter described and claimed, reference being had to the accompanying drawings forming a part hereof, wherein like numerals refer to like parts throughout.
FIG. 1 illustrates an industrial process to which the present invention can be applied;
FIG. 2 depicts the development of the control rules for a process in accordance with the present invention;
FIG. 3 illustrates the steps performed by a system designer during development of initial control rules;
FIG. 4 is a flowchart of the control system task associated with collecting and testing samples;
FIG. 5 illustrates the step of determining whether to perturb the system being controlled;
FIG. 6 is a flowchart of the steps taken to change control settings for the process;
FIG. 7 illustrates the steps in a decision to change the control rules;
FIG. 8, including 8(A) and 8(B) is a flowchart of the process for creating a decision tree from which rules are produced;
FIG. 9 depicts a simple decision tree produced in accordance with the algorithm of FIG. 8;
FIG. 10 is a decision tree including an example of the data structure for a tree produced in accordance with the present invention for a process control plant such as a chemical plant; and
FIG. 11 is a decision tree for a credit evaluation process at a bank from which rules for granting a loan are produced.
A typical application of the present invention to a chemical plant is illustrated in FIG. 1. In this system, controllable units 10-24 in the process are monitored by a sensing unit 26 which samples various controllable variables associated with the process such as temperature and flow. The sensing unit 26 sends the data samples to a control computer 28 which produces and transmits control values to a control unit 30 using rules induced from the process. The control unit 30 adjusts the controllable variables associated with the process. Process control systems such as depicted in FIG. 1 are very common, and a sensing unit 26 and a control unit 30 suitable for a particular process can be provided by one of ordinary skill in the art.
FIG. 2 depicts both the system designer tasks and control system tasks in developing and controlling a process in accordance with the present invention. An important aspect of controlling a process is properly defining the control structure and hierarchy. The control structure and hierarchy define the controllable variables and their position in the process with respect to the goal or goals of the process. For example, in a chemical process, temperature and flow rate can be the controllable variables while high yield and consistency in product quality can be the goals. Once the designer defines the control structure and hierarchy, the initial rules for operating the system must be defined 42. Development of the initial rules will be discussed in more detail later.
Once the initial rules are defined, the control system itself takes over and the process is operated 44 in accordance with the current rules. During operation, data samples are collected and tested 46 against the current rules. It is also possible to perturb the system 48, an event which can occur due to normal drift of the controllable variables or can be an intentional adjustment or change 50 in one of the controllable variables causing the process to change. If such a perturbation loop is executed, the system will again be operated 44 for a period of time during which samples are collected and tested. If the system is not perturbed, a decision must be made 52 whether to change the operating rules or, in other words, substitute a new set of operating rules. For example, one decision making strategy is to change the rules each time a sample is not predicted by the current control rules. Another strategy is to change the rules when a specified error threshold is exceeded. The threshold could be defined as the percent of samples not predicted, or when the absolute deviation from the goal exceeds a certain value. To change the rules, new rules are induced 54 from the collection of samples. If the new rules require different controllable variable settings, then the settings must be changed 50. Strategies for changing the control settings will be discussed in more detail hereinafter.
FIG. 2 also includes optional stages 58 and 60 at which human review can be provided to prevent a catastrophic system change. If the system is critical in nature or includes a long response time the human review stages could and should be added. In an automated process as intended by present invention, in the human review stages the system would notify the operator that a change is recommended by printing out or displaying the various changes which will occur in the system if the operator allows that the system to change automatically.
FIG. 3 illustrates the system designer tasks of FIG. 2 in more detail. Once the control structure and hierarchy of the system are the defined 62 then the attributes and classes for the system must defined, selected and a weight value for each selected. Classes and attributes must be properly defined 64 and 66 with respect to their relationship to the structure being controlled and the hierarchy of the system. A class represents a goal or an answer that is to be produced by the process such as yield in a chemical process control environment or whether or not to grant a loan in a financial analysis environment. Attributes are the independent variables which determine the value of the class or dependent variable, such as temperature and flow rate in a chemical process and whether bank references are favorable in a financial situation. All important attributes or controllable variables and class values or goals must be selected 68 by the user or systems designer. In a situation where there is a high degree of uncertainty concerning whether a particular controllable variable affects the output or goal, all controllable variables should be selected as attributes. An interesting benefit of the present invention is that attributes which are not important to obtaining the goal will be automatically identified so that they can be eliminated from the data to be sampled if desired.
It is possible within a particular hierarchy that attributes or class values are determined in accordance with a substructure or subproblem. That is, an attribute may actually be a goal or class value of a previous stage in the process and the attribute or class value would require the solution of a sub-problem to obtain the necessary variable value. If such subproblems exist, these must also be defined.
In inducing the control rules in accordance with the present invention, a decision tree, which tends to be the minimum size decision tree, is generated; however, such a minimum size tree may be undesirable because of cost or other considerations. For example, in a chemical process, the minimum size tree may always require that the temperature of a fluid be kept very high to obtain a satisfactory yield and this will raise fuel bills. In contrast, a non-minimum tree may allow lower temperature but more of an expensive catalyst thereby reducing overall costs. By attaching weights to attributes, cost or time considerations can be introduced into the rules induction process to find a minimum decision tree which reflects such considerations. A high weight causes an attribute to be more likely to be selected as a high node in the decision tree during the induction process. If minimum cost rules are desired, controllable variables or attributes which have the lowest costs should be given higher weights.
Once the above-listed values are selected the initial operating rules can be defined by referring to process control history from which the designer himself can produce 76 the rules. The initial rules can also be developed from physical constraints such as laws of nature. However, more efficiently, as will be discussed hereinafter, the initial rules can be induced 78 from historical data or samples of the controllable variables or attributes of the process from the recent past, such as during a start-up or debug phase of process development. As a result, the system designer need not be an expert in the process.
FIG. 4 illustrates the process of collecting and testing samples. First, the input variables of the process and the output goal is sampled 80 by a sensing unit 26, as depicted in FIG. 1 for a chemical process or a case history of a loan transaction is selected in a loan analysis system. The sample is tested 82 against the current rules and if the sample is predicted by the current rules, the process continues. If the sample is not predicted 84 by the current rules a decision can be made to discontinue the system. Such a decision to stop the process would be made, for example, when the sample indicates that the process has reached a point where an alarm should be issued and the process shut down. If, however, the system should continue, the sample is saved as a part of a sample data set in, for example, an array type structure and a current rule evaluation flag can be set which indicates that a decision needs to be made concerning whether to change the operating rules that is, substitute a new set of operating rules. Once the flag is set the process continues. An alternate sample collection strategy would be to save only those samples which improve toward the goal.
Next, as illustrated in FIG. 5, the decision to perturb the system is made by determining whether the results of the process being controlled are those desired 94. That is, the class value of the current sample is examined to determine whether the goal is being met. This determination can be made based on designer selected criteria such as in a chemical process by determining whether current yield is greater than or equal to the previous sample's yield. That is, the question at this point is whether the system is tracking toward the desired goal. The results may be acceptable but not optimal. If optimal results are desired, then the control settings can be changed. If the results are acceptable, the process moves on to the decision concerning whether to change the operating rules. If the current results are not yet optimum, the control settings are changed. The option also exists to intentionally make small changes in the control settings from the current state to determine if the performance of the system changes. The systematic perturbation of the system may give samples which allow performance to be improved.
FIG. 6 illustrates the process of changing the control settings. First, from the list of rules, the rules which will allow improvement towards the goal or goals are selected 96, thus substituting a new rule for the previous controlling rule. That is, associated with each rule is a class value which will result if the rule is followed. The class value, for example yield, of each rule is compared with the class value of the current sample (current yield) and the rules which will maintain or improve yield, that is, maintain or improve the process toward the goal, are selected. The rules that do not satisfy this criteria can be discarded. Since each class value can represent a different goal (that is, the output for different stages of the process or the satisfaction of plural goals) the rules must be examined to select only those rules which will allow satisfaction of all the goals. These rules are then combined 98. After the rules are combined, the settings for the attributes or dependent controllable variables of each rule are compared 100 with the system constraints. System constraints can be items such as range limits for a particular controllable variable or limitations that prevent simultaneously changing two or more controllable variables. After comparing the settings of the rules with the system constraints, the rules which do not meet the constraints can be discarded 102. Next, the changes dictated by the remaining rules are evaluated and new control settings are selected.
Various strategies are available to the system designer in determining how to evaluate and select new control settings, for example: (a) the control rule and associated settings which will require the minimum change from present conditions can be selected; (b) the control rule which will allow the changes in the minimum time from the present conditions can be selected; (c) the rule which is supported by the most number of data samples set can be selected or (d) some other selection criteria provided by the designer which is dependent on the process being controlled can be selected.
Once the settings are selected, the settings must be physically changed 106 by adjusting the control dependent variables indicated. These adjustments preferably are made by a process control unit 30, as illustrated in FIG. 1, which can control servos which change the position of valves, etc. However, when a system is in the development stages or when the system designer, due to economic or other considerations, chooses not to install a completely automated control system the system can output rule based instructions which tell the process control operator the values for the various controllable variables.
If the decision to perturb the system, as previously discussed, is negative, the decision to change the operating rules is made next, as illustrated in FIG. 7. First, the current rule is evaluated 108 by answering the question does the current rule predict the current sample. That is, the decision tree of the current rule is traversed using the current values of the attributes and the current class value or current goal is compared to the class value specified by the rule, and if the current and predicted class values are not coincident the rule does not predict the current sample. If a current rule evaluation flag is used, as illustrated in FIG. 4, the flag only needs to be tested at this stage. If the rule is predicted by the current sample, no further action is taken and process control using the current control settings is continued. Criteria for rules induction should be tested 109. In most situations it may be too costly or time consuming to change the rules when a single sample is not predicted. The rules change criteria may require that several samples be in error, or the magnitude of the error to be above some value, prior to inducing new rules. A user defined function including rule change criteria would be specified to decide when to re-induce the rules. If the current sample is not predicted and the rules induction criteria are satisfied, a new sample set for rule induction may be selected.
Various strategies are also available for selecting the contents of the sample set data base, so that the sample set data base does not grow beyond the storage capability of the computer 28 or the cycle time restraints on process control. For example, the oldest sample or the sample which produces a decision tree branch that minimizes improvement in the goal could be discarded.
Once the sample set is selected, new rules are induced as illustrated in FIG. 8. First, the samples are input 112 and stored in a data structure, such as an array, matrix or list which is suitable for storing samples having multiple attributes where the samples must be sorted and subdivided during processing. Next, an attribute is chosen 114 for testing to determine whether it is a appropriate split node for the decision tree. The attribute to be tested can be chosen arbitrarily, such as choosing the first attribute in the sample. The attribute is tested 116 to determine whether it is numeric. Assuming that the attribute is numeric, a determination 118 is made concerning whether the attribute has a value in each sample. If each attribute does not have a value, a suitable value for that sample is generated 120 by, for example, averaging the attribute values or randomly picking an attribute value. Another strategy for handling absent values is to create pseudo samples to substitute for the sample with the missing attribute. The missing attribute value for the pseudo samples can be generated based on the distribution of the attribute values for other samples in the sample set or on a distribution such as a bell curve.
The records are sorted 122 in ascending sequence according to the attribute value for the attribute selected, and then the sample set is split 124 into two sets between the first and second samples producing a split value and a split index. Next, an information gain function for creating a decision tree is evaluated 126 to produce the information gain for the split previously selected. The information gain will be used to determine whether the tested attribute is appropriate as the split node for the current level in the decision tree. The calculations necessary to evaluat this preferred function will be discussed in detail later. The resulting information gain along with split values and split index are stored 128. If the last sample has not been reached 130, the index is advanced 132 and the sample set is split between the next two samples and the decision tree generation function is again evaluated. Once the end of the samples is reached, the split which produces the maximum information gain, along with the split value and split index, is saved 134. If the end of the attributes has not been reached 136, then the next attribute is chosen and the process continues again.
After a similar process is performed for each symbolic attribute, the numeric or the symbolic attribute having the highest information gain and the corresponding indices are chosen 158 by comparing the information gain for each attribute. Next, the maximum numeric and symbolic information gain values are compared 160 with each other, the attribute with the maximum is chosen and a decision tree record entry is created. The data structure of the decision tree will be discussed in more detail hereinafter.
After the maximum is chosen, the samples are again sorted 162, based on the selected attribute, into ascending sequence if numeric and according to symbolic name if symbolic. Then the sample set is divided into subsets based on the attribute with the maximum information gain and the corresponding split value and split index. If all the samples in each subset have the same class values, then the decision tree for the subset structure and the corresponding rule is saved. If each sample in a subset does not have the same class values, then the algorithm is then executed for the subset as if it were the set. That is, the subset is treated as a set and the process of choosing attributes, sorting into ascending sequence, splitting and producing the information gain is executed.
If during the execution of the above-discussed rules induction algorithm an attribute is encountered which requires the solution of a subproblem to produce a subrule, the subrule is produced in the same manner as previously described. In the preferred embodiment, the subproblems which appear in the primary problem are rule induced prior to primary problem rule induction, so that when a subproblem indicator is encountered, the rule for that subproblem can be made available immediately. An alternative to performing subproblem induction before primary problem induction, is to temporarily halt rules production based on the primary sample set while a rule is produced for a subproblem sample set. Either the preferred or alternate approach can be applied whenever a particular class value requires the solution of a subproblem. The rules creation process for subproblems which occur for attributes and class values will be discussed in more detail hereinafter.
To illustrate the operation of the rules production algorithm of FIG. 8 using the preferred function, a simple symbolic sample set will be used to generate the appropriate decision tree and the general data structure of the decision tree will also be illustrated. The calculation for the function which is used to classify the samples in a sample set and to inductively create a rule from the sample set will be discussed.
A classification rule or process operation rule, in the form of a decision tree, can be constructed for any collection of C objects. M(C) is defined as the expected information content from a decision tree for the set of C objects where M(C) is defined as: ##EQU1## Where N equals the number of classes that the objects can be divided into, and Pi is the probability of the occurrence of a particular class where Pi is estimated by the relative frequency of the class.
When a particular attribute is chosen as the test attribute the expected information content based on the use of the attribute as the node in the tree and the resultant splitting of the sample set based on that attribute is defined by:
B(C,A)=(Probability that the value of attribute A is Ai) * M(Ci) * 1/Wi (2)
where Wi is the weight for the particular attribute.
The choice of the attribute for the particular node in the decision tree is the attribute which produces the most information gain which is calculated in accordance with:
Max. Information Content=M(C)-B(C,A) (3)
A more detailed discussion of this preferred classification procedure can be found in "Machine Learning, An Artificial Intelligence Approach" by Michalski et al, Tioga Publishing Co., Palo Alto, Ca, 1983, in Chapter XV entitled "Learning Efficient Classification Procedures And Their Application to Chess End Games" by J. Ross Quinlan.
Given the following data sample set which includes attributes for height, hair and eyes, and a class which arbitrarily indicates the represented people should fall into one of two groups, and assuming a weight of 1 for each attribute, the above discussed procedure as set forth in FIG. 8 will be performed.
TABLE 1______________________________________Record A1 A2 A3Number Height Hair Eyes Class______________________________________1 short blond blue +2 tall blond brown -3 tall red blue +4 short dark blue -5 tall dark blue -6 tall blond blue +7 tall dark brown -8 short blond brown -______________________________________
Height will be chosen arbitrarily as the attribute for testing. If the data set records are sorted on the height variable into the classifications short and tall the following will result.
TABLE 2______________________________________Record A1 A2 A3Number Height Hair Eyes Class______________________________________1 short blond blue +2 short dark blue -3 short blond brown -4 tall blond brown -5 tall red blue +6 tall dark blue -7 tall blond blue +8 tall dark brown -______________________________________
The information content for a split between the two variables having record numbers 3 and 4 is calculated in accordance with Equation 1 to produce:
M(C)=-3/8log2 3/8-5/8log2 5/8=0.954 (4)
When the information content for splitting between tall and short is calculated the tall side information content equals:
M(tall)=-2/5 log2 2/5-3/5log2 3/5=0.971 (5)
And the information content of the short side is:
M(short)=-1/3log2 1/3-2/3log2 2/3=0.918 (6)
The expected information content B(C, height) can be calculated in accordance with Equation 2 and is:
B(C,height)=(5/8)0.971+(3/8)0.918=0.951 (7)
Once the expected information content is produced the maximum information content or information gain in accordance with equation 3 can be calculated.
Max.Info.Gain(Height)=0.954-0.951=0.003 (8)
When the information content for hair is calculated by sorting the data samples into groups according to the hair attribute and performing Equations 1 and 2:
B(C,Hair)=(3/8)0+(1/8)0+(4/8)1.0=0.5 (9)
resulting in information gain for hair of:
Max.Info.Gain(Hair)=0.954-0.5=0.454 (10)
In the same way, the maximum information content for the remaining attributes, Eyes, results in Max.Info.Gain(eyes)=0.047. Reviewing the information gain for the three dependent attributes, hair is the appropriate attribute to be use for dividing the data set into three branches. A decision tree created for the above-discussed example using the classification procedure discussed above is depicted in FIG. 9, where the single line squares indicate attributes and the double line squares indicate the class values, while the labels associated with a particular branch of the tree indicate the value associated with that branch.
Even though a classification algorithm for producing a decision tree by calculating maximum information gain is preferred, other classification algorithms could be used.
The sample data set below is an example of data samples for ten attributes taken from, for example, a chemical plant where the class value is the yield of the process. The asterisk in the data indicate the absence of values for the attributes in the particular records and substitute values, are generated by for example averaging.
TABLE 3__________________________________________________________________________Record A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 ClassNumber 1st Temp 2nd Temp 1st Flow % Water 1st Ratio 2nd Flow 2nd Ratio 3rd Temp 4th Temp 3rd Yield__________________________________________________________________________ 1 134 65 1.72 * 1.84 2.73 1.48 110 60 2.4 LOW 2 134 65 1.72 23.4 1.84 2.73 1.48 109 62 2.4 LOW 3 135 65 1.71 23.2 1.84 2.73 1.48 109 63 2.4 OK 4 132 65 1.71 24.2 1.84 2.79 1.52 109 65 2.35 LOW 5 128 65 1.58 23.2 1.78 2.72 1.53 106 65 2.28 LOW 6 132 65 1.74 23.2 1.84 2.67 1.45 111 65 2.4 OK 7 132 65 1.64 24 1.74 2.61 1.46 109 60 2.31 LOW 8 134 65 1.74 24 1.84 2.71 1.47 110 64 2.4 OK 9 132 65 1.73 * 1.84 3.13 1.7 105 61 2.4 OK10 129 65 1.74 23.9 1.84 3.07 1.67 106 63 2.31 OK11 130 65 1.71 23.8 1.83 3.1 1.7 108 67 2.28 OK12 132 65 1.73 23.8 1.84 2.69 1.46 108 65 2.31 OK13 130 65 1.73 24 1.84 2.64 1.43 107 65 2.3 LOW14 126 65 1.74 24 1.84 2.65 1.44 106 65 2.3 LOW15 130 65 1.74 23.8 1.84 2.65 1.44 107 65 2.3 OK16 130 65 1.74 24.4 1.84 2.65 1.44 107 65 2.3 OK17 131 65 1.73 * 1.83 2.64 1.44 108 65 2.29 OK18 131 65 1.74 24.6 1.84 2.69 1.46 109 65 2.31 OK19 132 65 1.73 23.9 1.84 2.64 1.43 108 65 2.31 OK__________________________________________________________________________
Applying the rules production strategy previously discussed to the above-mentioned data (Table 3) set by sorting on each of the attributes and calculating the information gain for each attribute while assuming equal weights for the attributes produces the following:
Information Content M=0.94
______________________________________A1 A2 A3 A4 A5 A6 A7 A8 A9 A10______________________________________Inf.Gain. .16 .00 .22 .05 .16 .11 .11 .03 .16 .01______________________________________
As can be seen the attribute which should be used for the binary split is the A3 attribute because the information gain obtained by splitting on the A3 attribute is the highest (0.22). The split value within the attribute providing the highest information gain is 1.725, so that all the records with an A3 attribute less than 1.725 are grouped into one subset and all other records are grouped into another subset.
When this data set is used to induce the rules a tree with a data structure as illustrated in FIG. 10 is created. Each attribute record in the data structure, for example, record 186, includes a previous record pointer 188, a definition field 190 which indicates whether the record is an attribute record or a class record, an attribute name 192 and a split value 194. Also included are pointers 196 and 198 to the next record associated with the data split. The left-hand pointer points to a subset branch including records with attribute values less than the split value and the right-hand corner points to a subset branch for attribute values greater than or equal to the split value. A class record is different from an attribute record in that the type of the record type 204 is class. Only two other fields are provided, a field 206 for indicating the value of the class and a field 208 for indicating how many records fall within this leaf of the decision tree. The top node in the decision tree includes a top node flag 212 which is used when scanning the tree from the leaves to the top to determine that the top of the tree has been reached and one rule has been produced.
Comparison of the tree of FIG. 10 with the data set of Table 3 illustrates how the present invention ignores those controllable variables (attributes) that are not significant with respect to the goal. The variables that do not contribute do not get entered as part of the decision tree because the information gain for the subset of records associated with that attribute becomes equal to the information content. That is, all the records in the subset, even though they may have different attribute values, have the same class value.
After the tree is completed, several different strategies are available for producing the rules. In addition, several formats for the rule are possible depending upon whether the method of control is automatic or includes human intervention. For example, if the data structure of the FIG. 10 is used to produce a top down rule which indicates both positive rules (rules which, if followed, will result in goal accomplishment) and negative rules (rules which indicate the setting for control variables which will not result in goal satisfaction). The following rule will result:
The above rule would be appropriate in a process that does not include a control unit 30, where an operator must set the control settings. This rule indicates to the operator which settings will provide a bad yield and thus should be avoided.
Another rules production strategy suitable for a completely automated process, that does contain a control unit 30, generates the rules by starting only at the leaves of the decision tree which indicate goal satisfaction and traversing up the tree until the top node is reached and storing the settings for the control variables as the tree is bottom to top traversed. Such a set of rules produced by a traverse traversal of FIG. 10 is illustrated below:
Rule 1 A6≧2.945, A2<134.5, A3<1.725
Rule 2 A2≧134.5, A3<1.725
Rule 3 A2≧131, A7<1.435, A2≧127.5, A3≧1.725
Rule 4 A7≧1.435, A2≧237.5, A3≧1.725
After the constraints of the system (See FIG. 6) are used to eliminate the inappropriate rules and the rules necessary to reach other possible goals are also considered, and after additional rules are further discarded because of particular setting change control strategies, a single rule can be used to actually set the controllable variables in the process by the process control unit 30 of FIG. 1. For example, if after the rule elimination process Rule 2 remains then the first temperature (A2) would be set greater than 134.4 degrees and the first flow (A3) at less than 1.725.
The following data set (Table 4) is an example of the application of the present invention to a financial analysis system whereby determinations are made concerning whether loans should be granted. Such a sample set can be produced by obtaining case histories from a credit agency, including examples of loans for both good and bad customers, and assigning as attributes all the variables considered by loan managers in granting a loan. This example includes the solution of a subproblem associated with determining the value of an attribute and a subproblem for determining the value of a class.
The primary data set of Table 4 from which the principle rule for granting the loan illustrated above includes five attributes. The values of the first attribute A1 indicate whether or not a bank reference is favorable or unfavorable. The values of the second attribute A2 indicate whether the supplier reference for the company is favorable and the third attribute A3 value indicates whether the credit bureau rating is favorable. The fourth attribute A4 indicates whether a company is a closely held company and the fifth attribute A5, by using the pound sign in front of the attribute, indicates that this is an attribute subproblem that must be evaluated to determine the credit ratings of the principals in a closely held company. The class represents the overall evaluation and thus whether or not the loan should be granted. In the class column for record six the pound sign associated with the name indicates that a class subproblem must be solved to determine the rules for obtaining the class value of a further supplier evaluation (FSE).
The subproblem data set for evaluating the principals in a closely held company is as follows:
The definitions of the attributes A1 and A2 in Table 5 have already been defined. The subproblem data set for evaluating the further supplier evaluation class subproblem is as follows:
Attribute A1 indicates whether the situation is common in the industry and attribute A2 indicates whether the supplier still supplies the customer.
When executing the algorithms of the present invention using the data sets (Tables 4-6) listed above, whenever a subproblem is encountered the subproblem rule is retrieved. The subproblem, in the alternative, can act like a subroutine call which returns a rule for the class or attribute making the call. During the subproblem execution, the rule is generated in the same manner as in a principle data set. The execution of the algorithms of the present invention results in a decision tree as illustrated in FIG. 11. For simplicity purposes this decision tree does not illustrate the record formats and pointers for the various nodes in the decision tree. If this decision tree is used to generate a primary rule, a rule for principals in a closely held corporation and a rule for a further supplier evaluations the following rules will result. The format of these rules allows people in a credit department of a corporation or bank to determine whether or not to grant a loan.
The control settings in the above rules for a loan analysis are the attribute values, favorable or unfavorable, for each branch of the rule/decision tree. The rules produced include both the negative and positive rules so that bad loans can be eliminated. In automating a financial process as discussed above, the rules could be used to generate questions for the loan manager based on the attribute values for each branch, for example, "What is the bank reference? (favorable/unfavorable)". The answers would then be used to traverse the tree until a class value indicating whether the loan should be granted is encountered.
A new sample can be collected and a new rule produced for such a financial analysis system whenever a loan is granted based on an existing rule and the loan subsequently goes into default. The sample would include the particular attribute values for that loan and a class value of unfavorable. A new rule would be generated which would cover the latest criteria in discovering problem loans as well as the prior problem loans.
As can be seen from the prior discussion, the present invention provides a process control system which develops rules for governing the process which are themselves adaptive over time to changing conditions in the process itself. The rules developed also allow the process to be fine tuned for the particular goal and for economic as well as other considerations. Fine tuning can be performed by making minor perturbations in the controllable variables and sampling when progress toward the goal is made.
When applying this system to a process to be controlled, the portion of the system which evaluates the data sets and induces the necessary rules can be an IBM PC XT or AT if it includes an 8087 or 80827 math comprocessor. The minimum memory requirements is 320K with additional memory requirements being dictated by the number of samples in the sample data set and the number of attributes in each sample. If the number of samples times the number of attributes equals 1000 approximately 64K of additional memory will be required. If an interpreted LISP language is used, the induction of rules for a problem having 12 data samples and 5 attributes takes approximately 10 seconds. If a compiled LISP language is used or another language such a C or PASCAL, the execution would be an order of magnitude faster. If even faster execution and rules production is necessary, a faster processor with expanded mathematic capability and memory will be necessary. Thus, when applying the present invention to a particular process, consideration must be given to the number of attributes and the cycle time necessary to produce a control rule.
The many features and advantages of the present invention are apparent from the detailed specification and thus it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
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Cavey And The Albino Rhino
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Cavey And The Albino RhinoCavey And The Albino Rhino (1980) - Captain Caveman and the Teen Angels Cartoon Episode Guide
This page has been viewed 14 times this month, and 856 times total.
- Captain Caveman and the Teen Angels Cartoon Episode Guide
- Hanna-Barbera Studios
- Cartoon Characters: Captain Caveman, Dee Dee, Brenda, Taffy, Cave Bird.
- Originally Released in 1980.
- Season: 1
- Color
- Running Time: 30 minutes.
- Buy This On DVD
Cartoon Summary:
A witch doctor warns the gang not to remove a sacred statue.
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'One Night with Janis Joplin' a Powerhouse success
Asked just weeks before her death what young people were looking for, 27-year-old Janis Joplin was quick to answer: "Sincerity and a good time."
It's an apt description of what Joplin repeatedly delivered from stage, and it also neatly sums up "One Night With Janis Joplin," the amazing show - created, written and directed by Randy Johnson - now in town as a special Milwaukee Repertory Theater production. »Read Full Article(3)
Next Act gives wings, radio style to 'It's a Wonderful Life'
Imagine a Milwaukee without its musicians and dancers, its singers and actors, its highly reputed symphony and its world-class theater. It would look a lot more like Pottersville, that mean-spirited burg of cheap thrills and third-class entertainment that greets George Bailey before his great awakening.
Such were my thoughts Friday night as six veteran Milwaukee-based actors tugged at my heartstrings for the second straight year, in Next Act Theatre's reprise of "It's a Wonderful Life," as transformed by Mary MacDonald Kerr into a 1957 radio play, coming straight to us from the studio of Milwaukee's very own WNAT. »Read Full Article(1)


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Recreational use of marijuana is now legal (or, at least, kinda legal) in Washington state. Voters last November gave the green light to growing, selling and smoking pot.
And that means all of Washington state. Local governments can't opt out.
Yet, a least a few local officials around the state seem to be angling to keep legal marijuana sales out of their counties or cities.
For example, Walla Walla County Commissioner Perry Dozier said at a recent hearing on the subject he is struggling with the issue of providing public access to marijuana while simultaneously taxing people to fund substance abuse treatment for problems it might cause.
"It seems like an oxymoron to me," he said. "We need to have a substantial discussion before we create a situation" that will affect people, especially young people.
Other elected officials have pointed to the lack of support for Initiative 502 by local voters.
Concerns are understandable. We, too, had concerns. We urged voters to reject legalizing marijuana. We didn't see recreational marijuana as a positive.
But since the voters did approve I-502 it's the law. Political and personal views by elected officials or citizens on pot use are no longer relevant.
Last week Walla Walla County commissioners approved a one-year moratorium on adopting regulations to grow, process or sell pot. Earlier the College Place City Council took the same action.
The moratoriums make sense given the Liquor Control Board has yet to approve details for implementing the new law. With no clear state expectations, city and county governments have been frustrated in trying to establish rules to govern where marijuana operations will be located and how to issue permits to growers and sellers.
Local governments, however, will eventually have to put rules in place.
Brian E. Smith, director of communications for the LCB, said there is nothing in I-502 that allows a community or jurisdiction to opt out. The new marijuana law in Colorado, however, does.
The moratoriums in Washington state, he said, simply allow some breathing room in the transitions from rule-making to implementation.
An outright ban on the growing, selling and using of marijuana might be problematic, Smith said.
The LCB will issues a set number of licenses in the state's cities and counties to the applicants who meet the requirements. But, he said, if license holders can't operate due to a ban, there would be a risk of litigation at the local level.
Beyond that, I-502 calls for the Liquor Control Board to minimize the illicit pot market.
"To do so," Smith said, "consumers will need reasonable access to product."
Just saying "no" is not an option. Local government officials in Walla Walla County should use the cushion provided by the moratorium to create rules governing marijuana operations that will work for this community.
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Down-On-The-Farm Breakfast
20 Jan 2010
|Bacon strips
|8 , cut into 1/4 inch pieces
|Au gratin potatoes
|5 1⁄4 Ounce (1 Box)
|Boiling water
|2 1⁄4 Cup (36 tbs)
|2⁄3 Cup (10.67 tbs)
|Chopped green pepper
|1⁄4 Cup (4 tbs)
|Chopped onion
|2 Tablespoon
|Dried thyme
|1⁄4 Teaspoon
|1 Dash
|Shredded cheddar cheese
|1⁄2 Cup (8 tbs)
Cook bacon in a large skillet until crisp.
Drain, reserving 2 tablespoons drippings in the skillet with the bacon.
Stir in contents of au gratin potato mix, water, milk, green pepper, onion and thyme.
Bring to a boil, stirring frequently.
Reduce heat; cover and simmer, stirring occasionally, for 20 minutes.
Transfer to an ungreased 2-qt.baking dish.
Make six indentations in potato mixture with a spoon.
Break eggs into indentations.
Sprinkle with pepper.
Cover and bake at 350° for 20 minutes or until eggs reach desired done-ness.
Sprinkle with cheese.
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Down-On-The-Farm Breakfast Recipe


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Hotels in 26507 - Morgantown WV
Only hotels in 26507 zip code are listed below. Search for cheap and discount hotel rates in Morgantown, WV for your upcoming leisure or conference / group travel. We list the best 26507 hotels and motels so you can review the Morgantown hotel list below to find the perfect lodging. Hotels in Monongalia county and area code 304 are also listed.
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Hubby and I made a bet on a hockey game and I lost. Hubby had me wear heels, stockings, short skirt, no panties, sheer button up blouse and no bra. We went to a nearby hiking trail at a local conservation area.
We walked to about the middle of the trail, and hubby had me stand between 2 trees at the side of the trail. He then proceeded to tie my wrists and ankles to the trees so that I was standing spread eagle where I was to remain for one hour, the length of the hockey game we had bet on.
He then unbuttoned my blouse and hiked my skirt up above my waist leaving my tits and pussy out for public viewing and tied a stocking around my mouth so I couldn't talk my way out of anything.
I started to get wet almost immediately!Then he taped a note to the tree on my right stating that I had lost a bet and if you found me you could set me free or play with me!
Well, needless to say, every little sound and the warm breeze blowing on my pussy caused my pussy to become very wet.
After being there for what seemed like the full hour already, (it had only been about 40 minutes), we could hear something/someone coming down the trail. It ended up being a young male on a mountain bike about 17 – 20 years of age. I think I had an orgasm just knowing that somebody could see me, and could clearly see a tent popping up! He didn"t choose to untie me, but he did begin to fondle my nipples and clit and fingered my soaking wet pussy and although he was awkward and clumsy I did have a gushing orgasm!!!!
He untied my ankles from the tree, undid his jeans releasing his hard cock, put his arms under my knees lifting my legs in the air and proceeded to pound me with great speed, and the incredible stamina of a raging young man!! Iknow he came twice possibly three times before he finished with me.
After he left, hubby realeased me, and we had some pretty exciting nights for a while after that.
We have since then made many other bets and have had some naughty adventures in the process!!!!MOM BOUND NAKED - gay tied up for bet - tied and fucked stories - sexstories bounded naked raped - sex stories raped hiking -


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Doomsday is coming, again and again and again.
Today is the sixth day of the sixth month in the year 2006 -- and as superstitious people are well aware, "666" is a number that signifies the mark of the Antichrist and the coming of the Apocalypse, according to the Book of Revelation.
Hollywood, of course, is taking advantage of the curious date to release a remake of "The Omen," in which parents discover that their adopted child is really the devil.
The original, starring Gregory Peck and Lee Remick, is a horror classic. In the new version, filmmakers are happily using the calendar to put the fright into people.
"There has never been a more salient time to remind people that evil is neither a concept nor a theory," said John Moore, director of "Flight of the Phoenix" and "Behind Enemy Lines."
"It has a human face and it empowers itself through human actions. The true nature of evil has never been more apparent," he said. "In just the past four years alone, the world has been hit with devastating events -- political, natural and man-made. One can't help but notice a certain momentum."
Oh my gosh! First a tsunami, now a remake. Run for the hills!
Of course, there has never been a shortage of bleak predictions that the end is near. Over the last 20 years, a long list of biblical prophesy groups, UFO believers, and Satanists, among others, have all foreseen the Earth's imminent and abrupt demise.
"Funny, these far-flung groups don't agree on much, but they do believe the world is about to end," said paranormal investigator James Randi, who held a big "End of the World" party in 1999 to mark the date that the 16th century prophet Nostradamus pegged as doomsday.
"The most interesting thing about these end-time predictions is that the same groups just revise their date for doomsday and never explain why their first prediction turned out to be wrong."
If Earth survives June 6, it still may end soon, depending on whom you believe.
Some students of biblical prophesy peg 2008 as a key year in the Apocalypse watch. That year is considered critical because it marks the first generation -- 40 years -- since the Jews returned to Israel.
Other biblical prophets are more focused on 2033, which would mark the 2,000th anniversary of the crucifixion and resurrection of Jesus.
The year 2012 could be the end if ancient Mayan mysticism holds true. Centuries ago, the Mayans were the first to build a calendar, and that calendar ends in 2012. The Mayans disappeared suddenly and mysteriously around 900 A.D.
One of the largest groups of UFO believers, the Raelians, say aliens will begin arriving by 2035 to usher in a new world order.
Before you kiss your family and the world goodbye, just remember that doomsday predictions have been around since the dawn of time -- and we still haven't seen the sunset of all sunsets.
Remember the Y2K panic? When the year 2000 came, some thought the end was at hand -- some because of the coming of a new millennium, and some because they believed the computers of the world would discombobulate because of a programming glitch.
It's interesting to note that the same fear ran rampant in the late 900s -- the last time a new millennium rolled around.


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Other titles in the Art of the Novella series:
A Simple Heartby Gustave Flaubert
Synopses & Reviews
Publisher Comments:
The Art of The Novella
Too short to be a novel, too long to be a short story, the novella is generally unrecognized by academics and publishers. Nonetheless, it is a form beloved and practiced by literature's greatest writers. The Art of the Novella collection celebrates this renegade art form and it's most illustrious practitioners with 42 of the most famous novellas ever published.
"Elegant-looking paperback editions…a good read in a small package."
—The Wall Street Journal
The Art of the Novella collection includes one each of the following titles:
A Simple Heart by Gustave Flaubert
A Sleep and a Forgetting by William Dean Howells
Adolphe by Benjamin Constant
The Awakening by Kate Chopin
Bartleby the Scrivener by Herman Melville
The Beach at Falesa by Robert Lewis Stevenson
Benito Cereno by Herman Melville
The Country of the Pointed Firs by Sarah Orne Jewett
The Coxon Fund by Henry James
The Dead by James Joyce
The Death of Ivan Ilych by Leo Tolstoy
The Devil by Leo Tolstoy
The Dialogues of the Dogs by Miguel de Cervantes
The Eternal Husband by Fyodor Dostoevsky
First Love by Ivan Turgenev
Freya of the Seven Isles by Joseph Conrad
The Girl with the Golden Eyes by Honore de Balzac
The Hound of the Baskervilles by Arthur Conan Doyle
The Horla by Guy de Maupassant
How the Two Ivans Quarrelled by Nikolai Gogal
Jacob's Room by Virginia Woolf
Lady Susan by Jane Austen
The Lemoine Affair by Marcel Proust
The Lesson of the Master by Henry James
The Lifted Veil by George Eliot
The Man that Corrupted Hadleyburg by Mark Twain
The Man Who Would be King by Rudyard Kipling
Mathilda by Mary Shelley
May Day by F. Scott Fitzgerald
Michael Kohlass by Heinrich Von Kleist
My Life by Anton Chekhov
The Nice Old Man and the Pretty Girl by Italo Svevo
Parnassus on Wheels by Christopher Morley
Rasselas, Prince of Abyssinia by Samuel Johnson
Stempenyu: A Jewish Romance by Sholem Aleichem
Tales of Belkin by Alexander Pushkin
The Touchstone by Edith Warton
The Duel by Giacomo Casanova
The Duel by Joseph Conrad
The Duel by Anton Chekhov
The Duel by Heinrich Von Kleist
The Duel by Aleksandr Kuprin
"I wanted them all, even those I'd already read."
—Ron Rosenbaum
Too short to be a novel, too long to be a short story, the novella is generally unrecognized by academics and publishers. Nonetheless, it is a form beloved and practice by literature's greatest writers. In the ART OF THE NOVELLA series, Melville House celebrates this renegade art form and its practitioners with titles that are, in many instances, presented in book form for the first time.
With an attention to the details of bourgeois life considered almost scandalous at the time, A Simple Heart will remind many why Gustave Flaubert was acclaimed as the first great master of realism. But this heart-breaking tale of a simple servant woman and her life-long search for love meant something else to Flaubert. Written near the end of his life, the work was meant to be a tribute to George Sand—who died before it was finished—and was written in answer to an argument the two were having over the importance of realism. Although the tale displays his virtuosic gift for telling detail, and is based on one of his actual servants, Flaubert said it exemplified his belief that "Beauty is the object of all my efforts." This sparkling new translation by Charlotte Mandell shows how impeccably Flaubert achieved his goal.
Vivid realism captures the attitudes of Felicite the housemaid and her life of domestic servitude in this deceptively simple tale. This beautifully packaged series of classic novellas includes the works of masterful writers. Inexpensive and collectible, they are the first single-volume publications of these classic tales, offering a closer look at this underappreciated literary form and providing a fresh take on the world's most celebrated authors.
About the Author
GUSTAVE FLAUBERT was born in 1821 in Rouen, France, the son of a surgeon. He studied law but failed his exams and, after experiencing his first attack of what is thought to have been epilepsy—a condition he kept secret—he devoted himself to writing. His first novel, Madame Bovary, was published in 1857. It caused a scandal and led to Flaubert's prosecution on charges of immorality, but he was exonerated. His meticulous and realistic writing brought him continued recognition and esteem, although his subsequent books, such as A Sentimental Education and Salammbô, did not fare as well as Bovary, and he died in near-poverty in 1880.
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25 Hilarious Fantasy Football Team Names from the Movie Anchorman: Legend of Ron Burgundy
There's many different ways to express yourself in your fantasy football league. Perhaps you are the guy with the awesome avatar. Maybe you talk the best smack on the message board. But the surefire way to be the envy of your friends is to have an amazing fantasy football team name. Hell, it may even turn your season around!
The movie Anchorman produced dozens of quotables that you and your circle of friends, no doubt, still use today. Below, we'll help you recycle the best ones to use as your fantasy team name. Sure, some will need tweaking to make them fantasy football relevant, but you're up to the task. Now, on to the names.
1. Dr. Kenneth Noisewater: Brian Fantana (Paul Rudd) has names for his testicles. Apparently his right went to medical school. Perhaps your friends won't easily recognize this reference which will give you the chance to explain it. You win already.
2. Polite and Rarely Late: How Brick describes himself at the beginning of the movie. He also goes on to say that later in life a doctor tells him that he has an IQ of 48, and that he's what some people call "mentally retarded."
3. By the Beard of Zeus: What Ron says when he sees Veronica Corningstone for the first time at the ratings celebration party. If you really love the quote, why not wear it on your chest for all the world to see?
4. Kind of a Big Deal: One of the most quoted lines in the entire movie. How Ron describes himself to Veronica when she says she didn't know who he was. Through all of his ramblings to try and impress Veronica, while the timing was bad, the statement was true. In San Diego, Ron was a pretty big deal.
5. Smells of Rich Mahogany: When Ron is stumbling through his words trying to impress Veronica, he starts rattling off that he has "many leather bound books" and his apartment "smells of rich mahogany." All this time, we thought he lived in a house.
Have you tried daily fantasy sports? No season long commitment. Win money and get paid the same day! Check out our newest partners Draftster and Ballr.
Matt Brown
Latest posts by Matt Brown (see all)
- 5 Best Quarterback Themed Fantasy Football Team Names for 2014 - August 7, 2014
- Top ScoreStreak Plays for August 5 - August 5, 2014
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Why is Slumdog Millionaire rated R?
Official MPAA Rating: Slumdog Millionaire is rated R for or some violence, disturbing images and language.
This look at the fictitious life of a young man who appears on India's version of Who Wants To Be a Millionaire? deals with many mature situations and themes. Children are depicted living in a garbage dump before being abducted by adults claiming to operate an orphanage. Other scenes show children being physically abused, including having their eyes seared with heat and acid, and being beaten and verbally demeaned. Girls are groomed for sexual services, which are verbally discussed by adults and children throughout the film. Adults are also shown in violent situations involving knives, guns and hand-to-hand combat. Muslims (adults and children) are subjected to religious persecution—their homes are set on fire, some are beaten and people are seen with clothing in flames. A scene shows a young adult being tortured by electricity and near drowning (these tactics are administered by a police authority). Language includes two sexual expletives, about a dozen scatological slang words, some mild profanities and names of deity, along with some crude sexual anatomical terms. Alcohol and tobacco use is depicted, including by children and teens.


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Lippert's Flex Air Pin Box Creates Smooth Ride
September 25, 2014 by RVBusiness · Leave a Comment
Flex Air Pin Box from Lippert Lippert Components Inc. (LCI) has introduced its new Flex Air Pin Box, which combines LCI's proven Trailair Air Ride technology with its Rota-Flex pivoting head to significantly reduce fore to aft movement and vertical tug-of-war between a fifth-wheel RV and tow vehicle, producing a more comfortable ride. "RV owners know that towing can be an exhausting experience. Fifth-wheel RVs can really take a lot of abuse from bumpy roads," said LCI Chassis Product Manager... Read more
CURT Intros RAM Compatible Fifth-Wheel Legs
August 28, 2014 by Trina Nissley · Leave a Comment
New fifth-wheel legs from CURT CURT Manufacturing, a leading manufacturer of premium towing products, has developed OEM compatible fifth-wheel wheel legs made specifically to fit RAM 2013-2014 truck beds that contain the towing prep package. New in the 2013-2014 models, RAM created a towing prep package designed to make fifth-wheel and gooseneck installations easier and smoother. With this in mind, CURT saw a need for its consumers and developed RAM OEM compatible fifth-wheel legs. The legs mount... Read more
Blue Ox Introduces New Motorhome Sway Bars
August 26, 2014 by Trina Nissley · Leave a Comment
Blue Ox motorhome sway bar Blue Ox has introduced a new new line of sway bars for the motorhome chassis market. "With the addition of sway bars we now have a complete line of chassis performance products that will drastically increase the ride comfort of most motorhomes," said Ellen Kietzmann, president and CEO of Blue Ox, in a press release. Sway bars are designed to eliminate the excessive side to side movement which is often times referred to as body roll. This can be particularly noticed... Read more
Magellan's RoadMate Provides 'Real-Time' Info
July 31, 2014 by Trina Nissley · Leave a Comment
RoadMate RV9490T-LMB Magellan announced the availability of its RoadMate RV9490T-LMB, an RV GPS navigator with smartphone and cloud technology integration. The Magellan RoadMate RV9490T-LMB features a 7-inch HD glass capacitive touch screen and sleek tablet design for greater clarity and enhanced responsiveness. With the powerful Android OS, navigation software calculates and responds quickly to commands. Built-in Wi-Fi and Bluetooth connectivity gives users the freedom to choose how they... Read more
Dexter Axle Introduces Sway Control System
July 31, 2014 by Trina Nissley · Leave a Comment
Dexter Sway Control System Dexter Axle Co., a leading manufacturer of trailer axles and trailer brakes for over 50 years, announced the launch of the Dexter Sway Control system (DSC). According to a press release, the DSC system is a critical safety item to stabilize the sway of a trailer or RV by continuously monitoring trailer yaw – the side-to-side movement, left and right of the tow vehicle. The electronic system uses data collected in the event of sway to apply the brakes on the correct... Read more
Thetford Wash & Wax is Available in Toss-ins
July 25, 2014 by Trina Nissley · Leave a Comment
Thetford's New Wash & Wax Toss-Ins Thetford's new Wash & Wax Toss-Ins help RVers keep their RV clean and shiny in one easy step, all while using a convenient, pre-measured single dose package that eliminates waste and mess. "Consumers respond positively to the convenience of Toss-Ins," said Kevin Phillips, Thetford president, North America, in a press release. "This puts our industry-leading product in the most convenient form available on the market." Wash & Wax Toss-Ins... Read more
InterVac Designs Power Management System
June 9, 2014 by RVBusiness · Leave a Comment
New Power Management System for RoadVac InterVac Design Corp. Inc., creator of the Roadvac built-in vacuum system, announced the addition of the Power Management System for RVs with 30-amp power systems. According to a press release, the product will be available to recreational vehicle manufacturers in the summer of 2014. The Power Management System for RoadVac takes the guesswork out of operating on-board equipment, the company said. RVers can switch between two electrical devices – typically... Read more
KVH Rolls Out New 'TracVision RV1′ Antenna
June 2, 2014 by RVBusiness · Leave a Comment
KVH TracVision RV1 KVH Industries Inc. has introduced the TracVision RV1, a powerful and compact stabilized antenna that provides satellite TV reception in RVs and other vehicles. According to a press release, the new system is designed for simple, trouble-free operation. Measuring 12 1/2 inches, TracVision RV1 enables reception of satellite TV from moving vehicles by using a motorized antenna to constantly track orbiting satellites. This tracking ability is coupled with fast acquisition capability... Read more
SumoSprings Offer Comfort in Class C Coaches
April 30, 2014 by RVBusiness · Leave a Comment
SumoSprings for Class C's SuperSprings International has created new SumoSprings applications specifically designed for Class C motorhomes. According to a press release, the "airless airbags" can be mounted to the front and rear suspensions of the Ford E-Series Cutaway, a popular platform for recreational vehicles. "Motorhomes are always loaded, unlike pickup trucks or passenger vans that may only require extra support on certain occasions," says Mike Visser, president of SuperSprings... Read more
Dexter Introducing 'Removable Spindle Option'
April 22, 2014 by RVBusiness · Leave a Comment
Elkhart, Ind.-based Dexter Axle, a leading manufacturer of trailer axles and trailer brakes for over 50 years, announced the release of a removable spindle option on select Torflex axles. The removable spindle option, available on new axles, will allow trailer owners to simply remove and replace damaged spindles in the field without purchasing a new axle beam, the company stated in a press release. This new option features Dexter's E-Z Lube grease system. "Our customers were looking for an easy... Read more


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|One Bedroom
Prices, specials, features and availability subject to change.
Contact for details (508) 455-4601
Four Winds is a charming community in Fall River, close to Providence and Newport, RI. We are located only 50 miles from Boston with easy access to Rt. 24 195.
Four Winds Apartments are in a charming community in Fall River just minutes from the Freetown line and close to Providence and Newport, RI. The community's superior location provides easy access to Routes 24 and 195.Come see everything that Four Winds apartments have to offer!
Lease terms are variable. Please inquire with property staff.
Dogs Allowed. Cats Allowed. Pets Accepted. Call for service animal policy.
168 units
Check Availabilityor call
Complex is well maintained, conveniently located to major highways, it is very convenient having the washer an Read Mored dryer in the apartment
November 14, 2014
Monday-Friday: 9:00am-6:00pm
Saturday: 10:00am-2:00pm
Claremont Management
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Caller-Times College Football Countdown: #6 Nebraska
Head coach: Bo Pelini (Third season at Nebraska and overall, 20-8)
Last season: 10-4 (Beat Arizona in the Holiday Bowl)
Starters returning/lost: Offense, 7/4; Defense 8/3
Three offensive players to watch: RB Roy Helu; TE Mike McNeil; WR Niles Paul
Three defensive players to watch: CB Prince Amukamara; DT Jared Crick; PESO Eric Hagg
Breakout player: OT Jermarcus Hardrick. A hotly anticipated lineman? Must be Nebraska. The 6-foot-7, 320-pound JUCO transfer will mow down people once he gets his Division I legs under him. The line should be fine with Hardrick on hand.
Sept. 4 WESTERN KENTUCKY
Sept. 11 IDAHO
Sept. 18 at Washington
Sept. 25 SOUTH DAKOTA STATE
Oct. 7 at Kansas State
Oct. 16 TEXAS
Oct. 23 at Oklahoma State
Oct. 30 MISSOURI
Nov. 6 at Iowa State
Nov. 13 KANSAS
Nov. 20 at Texas A&M
Nov. 26 COLORADO
Six points
1. A Boy Named Suh. The quarterback race is a big deal in Nebraska, but the real issue is which people -- note the plural usage -- will replace Ndamukong Suh. The defensive tackle tossed around Colt McCoy like a stuffed animal in the Big 12 title game. But Suh already had made his mark as Heisman Trophy candidate, bringing glamour and national attention to college linemen for the first time since Hugh Green lined up at end for Pitt in 1980. Suh had 85 tackles, 24 for loss, 12 sacks and 26 hurries. Jared Crick, who lined up next to Suh, went 73/15/9.5/16 last season. The big problem is finding someone to play next to Crick. Baker Steinkuhler and Terrence Moore have the unenviable task of trying to replace the man named Suh.
2. Helu Goodbye. Where have you gone, I.M. Hipp? Nebraska has some of its best I-back depth since the late 1970s and early 1980s when Hipp, Jarvis Redwine and Mike Rozier were taking handoffs. Roy Helu was a revelation last season, rushing for 1,147 yards and 10 touchdowns. Rex Burkhead and Dontrayevo Robinson lend decent depth to the position. Important, because the Cornhuskers look like they might get back to being a run-first team.
3. The Race is On. Zac Lee led Nebraska last season, throwing for 2,143 yards. His 14 touchdowns against 10 picks were mediocre at best, however, and he was injured and missed spring ball. So now the Huskers have three QB candidates. Cody Green, who spelled Lee some last season, and Taylor Martinez, more of a running quarterback, pushed into the running at spring practice. Coach Bo Pelini hasn't tipped his hand about who has the upper hand. Whoever takes over will be expected to minimize mistakes, hand to Helu and hit Niles Paul (19.9 yards a catch) or Dallas Clark-like Mike McNeil for a nice gain now and then.
4. Black is Black. The Blackshirts finally seem to be back. The famed tough Huskers defense returned last season under Pelini after a few excruciating seasons of shootout football. A lot of credit can be directed in Suh's direction, but this is a talented group, especially in back. Prince Amukamara is an All-America candidate at corner and Eric Hagg has developed into a fine hybrid linebacker-safety, known as a Peso in the team's parlance. Suh was a bad man and the linchpin of the defense, but there are a lot of fine players returning from a unit ranked seventh nationally in total defense.
5. Think About Your Troubles. Pelini has not shown a lot of optimism during the preseason, pointing out the various holes that need to be patched. The team also was hit rapid-fire by season-ending injuries to three contributors, including starting linebacker Sean Fisher. Obviously, these are setbacks. But is Pelini poor-mouthing his team just to keep the spotlight off Nebraska? Everyone will know by Week 3 when the Huskers meet Washington.
6. Alexander's Feast. Alex Henery got his kicks -- plenty of them -- last season. Henery handled the kicking and punting chores last season. He knocked through all 38 extra points and hit 24 of 28 field goals. Three of his misses were from 50 yards or beyond and the other from 43. His 41.4 average on punting seems good not great, but Henery dropped 30 inside the 20-yard line against seven touchbacks.
Season at a glance
On the Huskers: "I like what we're doing scheme-wise. I like what we're doing personnel-wise. I feel real good about where our offense is." -- Nebraska coach Bo Pelini to Nebraska.statepaper.com on his offense.
Key game: Oct. 16 vs. Texas. Revenge for a controversial ending to the Big 12 title game? Perhaps. But, more importantly, this will show Nebraska where it stands in the conference pecking order. A loss here and the Oct. 30 game against Missouri has Big 12 North title showdown written all over it. A win and the team can at least think title.
Why they could be No. 1: Great defense and a run-first attitude. Sounds like an old-fashioned approach.
Why they will be No. 6: The defense will take a hit without Ndamukong Suh. But if the Huskers can't get settled on a QB, the offense could misfire badly.
Trouble if: Nebraska's offense looks like the strikingly bad one that showed up against Iowa State, Oklahoma and Texas last season, when it combined for 29 total points in those games against Big12 foes.
-- Lee Goddard
The Caller-Times Countdown
For the 10th season in a row, the Caller-Times sports department voted on a top 15 for national college football. The teams will be previewed and counted down to No. 1. Here is the schedule:
No. 15 North Carolina
No. 14 Oregon
No. 13 Wisconsin
No. 12 Miami
No. 11 Iowa
No. 10 Pitt
No. 9 Virginia Tech
No. 8 Oklahoma
No. 7 Florida
TODAY: No. 6 Nebraska
TUESDAY: No. 5
WEDNESDAY: No. 4
THURSDAY: No. 3
FRIDAY: No. 2
SATURDAY: No. 1
Subscribe to the Caller-Times
Need Help? Call us at 1-800-566-8098.
Monday-Friday: 6am-3pm / Saturday: 7am-11am / Sunday: 7am-11am


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Hours of Operation: Sunday 9:00 a.m. – 2:00 PM | Market closes only on Easter Sunday, April 5, 2015
Harvest Market and Marketplace
Halloween Trick-or-Treat Spooktacular
October 31st Montrose Halloween Trick-or-Treat Spooktacular Throughout the Montrose Shopping Park along Honolulu Avenue Starts at 5 pm – 'til the treats are gone!
Montrose Founders Day February 22, 2015
Bring the family and join the fun! The Montrose Merchants invite you on February 22, 2015 Hours: Sunday 9:00 a.m. – 4:00 p.m.
Montrose Old-Town Christmas
November 27th to December 25th, Sat. & Sun. 11am to 3pm – Tree Lighting on November 27, 2015.
Other Events in the Montrose Shopping Park
38th Annual Montrose-Glendale Christmas Parade | Saturday, December 6th, 2014 at 6:00 pm


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Enjoy the fresh profile of the Serenity occasional wingback chair with low profile arms, T-style seat cushion, welt details, and tall tapered legs.
The Klaussner Living Room Serenity Chair is available from Klaussner Home Furnishings.
Prices subject to home office audit & correction.
Dealer Login >>
Development: MicroD Inc.
Website Powered by ePiphany


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I'm working on my personal website portfolio (theeggs.biz) with a new design, still wip.
Here is how I'm thinking to organize the main layout, the animations of the opening pages, the icons, and the color palette.
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SIGN YOUR DOG UP TODAY!!!"TRAINED 2 GO DOG ACADEMY" 4 PAYPAL PAYING SLOTS!!! AND 4 CASH & CHECK ONLY SLOTS AVAILABLE!!)I have 15 years of protection/obedience k-9 training under my belt.I also did 4 years of volunteer work for the anti-cruelty society and work at 2 dog school and a guard dog service company. Have a protection dog does not mean gave a dog so aggressive it going to kill you ...
Jul 4 -
Chicago :
Chicago - Dogs & puppies
.hi I have two female pitbull puppies 8 weeks old they come wit the shots you KC registered papers they are ready to go give me a call to set up appointment to come and see them they are very nice healthy puppies thank you and have a great day no email
Jun 26 -
Grand Rapids :
Grand Rapids - Dogs & puppies
I have two litters of pups
Mom of first litter is a pocket bully she's 16inch tall standing
Mom to second litter is an American pitbull (tri)
Father is a XL bully
I have 1 fawn male left(350) 5 red nose (250) and 4 tris(450) left pups come with first shots and deworming! Contact me if interested 708.466.0380
This ad was posted with the eBay Classifieds mobile app.
Jun 25 -
Chicago :
Park Forest - Dogs & puppies
We have 2 left (Out of a litter of 9) Beautiful UKC Registered Pitbull Puppies for sale 2 females. Both Parents on site! Sire (Dad) - is UKC Registered as (Purple Ribbon) "PR" Big Red Buddy. He is primarily Razors Edge with a touch of Gotti. Dam (Mom) - She is UKC registered with a bloodline of mostly Watchdog/Greyline. Our dogs and pups are family raised around kids. They have amazing ...
Jun 24 -
Detroit :
Southgate - Dogs & puppies
These puppies are mixed with boxer and pitbull!!!!! Thats what makes them so special. The mother is a rare blue brindle pitbull and the father is a rare all white boxer. The puppies are mostly all white with either brown or black spots. Theyve been wormed, potty trained, and promised obediance training. Theyre ready to go by the end of june, theyve been around (small) animals, babies, and ...
Jun 24 -
Columbus (OH) :
Columbus - Dogs & puppies
Big beautiful blue 2weeks 10" puppies (thick)
Mom &Dad on site
Mom gator/bully
Dad Razor (RedNose)
Don't pass up and awesome litter
Info call 630-202-1544
I have a few sold
No low ballers please
Jun 23 -
Chicago :
Elgin - Dogs & puppies
I have two litters of pups mom is an American pitbull dad is a xL bully
I have fawns,tri colors and red nose
FAWNS(350) TRI COLOR (450) red nose (250)
Serious inquiries only
Please don't waist my time first litter will be ready in 3weeks second litter will be ready in 8 contact Jasmine 708.466.0380
This ad was posted with the eBay Classifieds mobile app.
Jun 22 -
Chicago :
Park Forest - Dogs & puppies
SIGN YOUR DOG UP TODAY!!!"TRAINED 2 GO K-9 ACADEMY" 2 PAYPAL PAYING SLOTS!!! AND 3 CASH & CHECK ONLY SLOTS AVAILABLE!!)I have 15 years of protection/obedience k-9 training under my belt working dogs in obedience,protection,law enforcement,drug work,ppda and more.I also did 4 years of volunteer work for the anti-cruelty society and also worked at 2 dog schools and a guard dog service company ...
Jun 18 -
Chicago :
Chicago - Animal services
Blue female pitbull/ American bully for sale. White markings will be ready for a new home In 2 weeks. Dad is blue and mother is the black and white dog in pictures, APBT and American bully breeding. Mother and father on premises.
This ad was posted with the eBay Classifieds mobile app.
Jun 17 -
Chicago :
Chicago - Dogs & puppies
Beautiful blue nice pupps
Father Razor edge (red noise
Mother Bully/gator
Parents are on site 27/4
Don't Miss on on Some awesome pretty pupps
Call 630-202-1544
Mike for info
Jun 12 -
Chicago :
Elgin - Dogs & puppies
Beautiful blue nice thick pupp
F) razor edge red noise
M bully/gator
Parents on site 24/7
Don't miss out on an awesome pretty pupp
Info call 630-202-1544
Jun 11 -
Chicago :
Elgin - Dogs & puppies
Beautiful blue puppies BoD June 9/15
F) razor edge (redNoise
Both Parents on site
Don't Miss out on and awesome pretty
Beautiful blue pitbull puppies
Nice thick bones
(F) Razor edge Red noise
(M) bully/Gator
Both Mother And Father On Site
Don't Miss out on Nice Great awesome Pupps
Jun 10 -
Chicago :
Chicago - Animal services
Not selling. Taking deposits only. First come first serve. I have 8 pups. Ranging from 350 to 450. Will have first shots and dewormed. They are half xl bullie. Puppies can gonhome at 6 weeks.
Jun 9 -
Chicago :
Chicago - Dogs & puppies
Have diz beautiful blue fawn female at only 6 months have color eyes n have all her shots n is ready to go to a nice loving home im selling her wit paperz or wit out price not the same your choice more info text me for faster respond at 815-514-1710
Jun 6 -
Chicago :
Joliet - Dogs & puppies
Here we have some of the finest blue nose pit bulls out there game dogs mixed with the so popular bully dogs future puppy's are always put aside for interested buyers stud is available also text for price 3312341014 are quality is due to improve drasticlly it is already at its finest litters coming very soon very fine results 100% great family pets guard dogs teperment of a shitzu great ...
May 20 -
Chicago :
Joliet - Dogs & puppies
I have a beautiful 13 week old puppy born 2/2/15 has papers and UKC registration all his shots are up to date, he is really stocky, huge bones and paws, gonna be a big boy... parents are in pictures razor edge blood line, dad is a champion. Selling cheap so that It will going fast. NOT GONNA FIND ANOTHER OPPORTUNITY LIKE THIS.
May 8 -
Chicago :
Hammond - Dogs & puppies
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Out of Stock
© 2015 Replacements, Ltd. All rights reserved.
With Our "Find This For Me" Service we notify you via email once we have stock!
Piece Code: GW
Piece Name: Water/Wine Goblet
Size: 6 7/8 in
© 2015 Replacements, Ltd. All rights reserved
Piece Code: GWM1
Size: 7 in
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Piece Name: Beer Glass
Size: 6 1/4 in
Piece Code: CHB
Piece Name: Highball Glass
Size: 5 1/8 in
Piece Code: TUM
Piece Name: Flat Tumbler
Size: 4 in


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|« Back to Article
Deadly roadway to get traffic signal
By Carol Christian | September 21, 2012 | Updated: September 21, 2012 10:20pm
Photo By James NielsenPolice investigate the scene of a fatal accident at Eldridge Parkway and Patterson road Friday, April 23, 2010, in Houston. James Nielsen Chronicle
Photo By Johnny HansonA guard rail along the 2500 block of Eldridge Parkway is damaged Thursday, June 21, 2012, after an accident between two cars and a pickup truck during a rain storm Wednesday resulted in three dead, in Houston. The crash occurred about 5:40 p.m. Wednesday when the driver of a southbound blue Mustang was coming around a curve hydroplaned and swerved into oncoming traffic, Houston police Sgt. Nora Perry said. The Mustang was struck in the side by a northbound red Corolla, Perry said. The driver of a red Toyota Tacoma pickup trying to avoid the accident struck the back of the Mustang. A passenger in the Corolla, a 33-year-old woman, was killed instantly. The driver of the Corolla, a 40-year-old woman, and a passenger in the Mustang, a 40-year-old man, were taken by helicopter to a hospital where they later died, Perry said. ( Johnny Hanson / Houston Chronicle )
In the latest fatal accident on June 20, three people died when a southbound Ford Mustang hydroplaned into oncoming traffic on a curve and was struck by two other vehicles.
To help manage traffic at an intersection known for fatal accidents, the city of Houston will install a traffic signal at North Eldridge Parkway and Patterson Road, just north of Interstate 10.
The intersection marks one end of a curving stretch of parkway between Patterson and Clay Road where 23 fatal accidents have occurred since 2003, resulting in 26 deaths, according to the Houston Police Department.
Between Jan. 1, 2008, and June 30 of this year, 532 accidents were reported on the strip of road less than two miles long, HPD said.
Depends on drivers
An examination of the crash reports showed that most were the result of speeding or related traffic violations such as failure to stay in a single lane.
Although installing the signal is intended to make the intersection safer, results will depend upon drivers, officials said.
"There is nothing you can do, engineering-wise, to control driver behavior," said Alvin Wright, Public Works and Engineering spokesman. "However, what we can do is put in devices that provide more efficient traffic control."
In the latest fatal accident on June 20, three people died when a southbound Ford Mustang hydroplaned into oncoming traffic on a curve and was struck by two other vehicles. Heavy rain had fallen just before the crash, police said.
Police determined that the cause of the accident was the Mustang driver's failure to stay in a single lane, HPD spokesman John Cannon said.
Although speed was not cited as a factor, Cannon said the department urges drivers to use caution in that area.
From time to time, the police department uses electronic signs known as "speed trailers" or traffic-enforcement officers to monitor speeds on that stretch, Cannon said.
Speeder crackdown
Officers monitored the road last weekend, he said, following a three-vehicle accident Sept. 12, which was attributed to high speed. A woman was transported by helicopter with life-threatening injuries but was expected to recover, HPD spokeswoman Jodi Silva said.
This week, officers were on the road mornings and afternoons watching for speeders, said Harry Sharkis, a Copperfield commuter who has driven North Eldridge every work day since 1995.
"I've never seen them patrol five days in a row that aggressively," he said.
The new traffic signal is already designed, but installation has been postponed until electrical power is available at the intersection, which is on Addicks Reservoir land, Wright said.
Construction is expected to begin in April.
Engineers are also considering other, unspecified modifications, he said.
"We're looking at any possible solutions or changes that could be an option," he said. "Nothing is written in stone."


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Potato Boats Baked and Broiled
|Baking potatoes
|3 Large
|Corn oil
|1 Tablespoon (As Desired)
|1 Large, thinly sliced
|Unsalted butter
|2 Tablespoon
|Low sodium low fat milk
|1⁄3 Cup (5.33 tbs)
|White pepper
|1⁄4 Teaspoon
|Onion powder
|1⁄2 Teaspoon
|Liquid smoke
|1⁄2 Teaspoon
|Liquid smoke seasoning
|1⁄2 Teaspoon
|Parmesan cheese
|1 Tablespoon, grated
|To Taste (As Desired, For Garnish)
1) Preheat the oven to 400 degrees.
2) Scrub the potatoes thoroughly to clean the skin. The skin should be clean enough to eat.
3) Then, with corn oil, rub the outside of each potato.
4) Cut each potato into half, lengthwise.
5) Between the 6 potato halves, evenly divide the onion slices.
6) Bring the potatoes back together with the onions in between, and with aluminum foil, wrap tightly.
7) In the center of a baking dish or a cookie sheet, place the potatoes and bake in the oven for 1 hour.
8) From the oven, remove the potatoes and unwrap.
9) From the potatoes, remove the onions.
10) In a large mixing bowl, put the removed onions.
11) From each potato half, gently scoop out the cooked potato. Do not tear the skin. Reserve the potato skin to use as boats, for serving.
12) To the onions in the bowl, add the cooked potato.
13) To this, add the butter or margarine.
14) In a bowl, combine the milk, white pepper, onion powder and liquid smoke. Mix well.
15) To the onion-potato mixture, add this.
16) With an electric mixer or a whisk, whip the potato mixture until it is blended well and is smooth.
17) Into the 6 boats (the reserved skin), divide the potato mixture evenly.
18) On top of each serving, sprinkle 1/2 teaspoon of Parmesan cheese.
19) Garnish with a little paprika.
20) Finally, place the potatoes under the broiler until they turn light brown.
21) Serve hot with rice or crusty bread, according to your choice.


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The Pennsylvania Behavioral Health and Aging Coalition, in collaboration with the Pennsylvania Association of County Drug & Alcohol Administrators will be sponsoring a conference entitled Older Adults and Substance Abuse: Issues for Pennsylvania, June 19 – 20 at the Penn Stater Conference Center, State College Pennsylvania.
For Registration and hotel information please click on the following web link: http://www.pacdaa.org/pacdaa/cwp/view.asp?a=2518&q=554963&pacdaaNav
© 2015 National Sexual Violence Resource Center – Founded by the Pennsylvania Coalition Against Rape
This site is supported by Grant/ Cooperative Agreement No. 1UF2CE002359-02 from the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention.