code: public void modify(double activationDegree, TNorm implication) {
          if (!isLoaded()) {
              throw new RuntimeException(String.format(
                      "[consequent error] consequent <%s> is not loaded", text));
          }
          for (Proposition proposition : conclusions) {
              if (proposition.getVariable().isEnabled()) {
                  if (!proposition.getHedges().isEmpty()) {
                      final int lastIndex = proposition.getHedges().size();
                      ListIterator<Hedge> rit = proposition.getHedges().listIterator(lastIndex);
                      while (rit.hasPrevious()) {
                          activationDegree = rit.previous().hedge(activationDegree);
                      }
                  }
                  Activated term = new Activated(proposition.getTerm(), activationDegree, implication);
                  ((OutputVariable) proposition.getVariable()).fuzzyOutput().getTerms().add(term);
                  if (FuzzyLite.isDebugging()) {
                      FuzzyLite.logger().log(Level.FINE, "Aggregating {0}", term.toString());
                  }
              }
          }
      }
docs: Modifies the proposition set according to the activation degree (computed
  in the Antecedent of the Rule) and the implication operator (given in the
  RuleBlock)
  
  @param activationDegree is the activation degree computed in the Antecedent
  of the Rule
  @param implication is the implication operator configured in the RuleBlock