diff --git a/README.rst b/README.rst
index 2d92098..ef1cc5e 100644
--- a/README.rst
+++ b/README.rst
@@ -53,9 +53,14 @@ performing an aggregation function over these values::
 
 Version Notes
 =============
-0.1.1 (TBD)
+0.1.1 (2024-08-20)
 -------------------
 * Addition of array-like, dataframe label-based, and callable ``length`` parameter options to the ``EventsCollection.to_windows()`` method and ``distance`` parameter to the ``EventsCollection.shift()`` method. This allows for the creation of segments based on a variable length. This will be made available to other methods in future versions as well.
+* Address ``numpy>=2.x`` compatibility issue in ``EventsCollection.dissolve()`` method which was causing errors in some cases. More fixes related to ``numpy`` updates are expected to come in future versions.
+* Address datatype loss issues for LRS columns during ``EventsCollection.to_windows()`` and ``EventsUnion.union()`` methods. This was causing some instances of key columns to be converted to incorrect dtypes.
+* Restructure .targets property to be ``keys + [beg, end]`` for more intuitive and consistent performance.
+* Addition of ``min`` and ``max`` aggregation methods to the ``EventsMergeAttribute`` class.
+* Several example Jupyter Notebooks for common use cases have been added to the ``examples`` folder for user reference. More to come.
 * Performance improvements
 * Various bug fixes, minor features
 
diff --git a/linref/events/collection.py b/linref/events/collection.py
index 85a363c..ffcd695 100644
--- a/linref/events/collection.py
+++ b/linref/events/collection.py
@@ -874,6 +874,9 @@ def dissolve(self, attr=None, aggs=None, agg_func=None, agg_suffix='_agg',
             rc = RangeCollection(begs=begs_i, ends=ends_i, centers=None,
                                  copy=None, sort=False)
             consecutive = rc.are_consecutive(all_=False, when_one=True)
+            # Address numpy 2.x compatibility
+            if type(consecutive) is bool:
+                consecutive = np.array([consecutive])
             splitter    = (np.where(np.invert(consecutive))[0] + 1).tolist()
             
             # Get aggregation data