Abstract: A method includes forming a first sealing layer at a first edge region of a first wafer; and bonding the first wafer to a second wafer to form a wafer stack. At a time after the bonding, the first sealing layer is between the first edge region of the first wafer and a second edge region of the second wafer, with the first edge region and the second edge region comprising bevels. An edge trimming process is then performed on the wafer stack. After the edge trimming process, the second edge region of the second wafer is at least partially removed, and a portion of the first sealing layer is left as a part of the wafer stack. An interconnect structure is formed as a part of the second wafer. The interconnect structure includes redistribution lines electrically connected to integrated circuit devices in the second wafer.

Abstract: In an approach for JAVA Virtual Machine (JVM) heap memory tuning, one or more computer processors obtain a feature vector of an application running on the JVM. The one or more computer processors input the feature vector to a predictive model trained with historical application data collected in one or more production environments. The one or more computer processors receive an output of the predictive model based on the feature vector with at least one memory tuning recommendation for the JVM. The one or more computer processors tune the memory of the JVM based on the at least one memory tuning recommendation.

Abstract: A processor may identify that a user has been added to a group of two or more users. One or more communications between the group may be hosted on one or more communication platforms. The processor may analyze each of the one or more communications. The processor may identify, from the analyzing, content of the one or more communications. The processor may generate a report. The report may indicate the content of the one or more communications. The processor may display the report to the user.

Abstract: In an approach for JAVA Virtual Machine (JVM) heap memory tuning, one or more computer processors obtain a feature vector of an application running on the JVM. The one or more computer processors input the feature vector to a predictive model trained with historical application data collected in one or more production environments. The one or more computer processors receive an output of the predictive model based on the feature vector with at least one memory tuning recommendation for the JVM. The one or more computer processors tune the memory of the JVM based on the at least one memory tuning recommendation.

Abstract: A processor may identify that a user has been added to a group of two or more users. One or more communications between the group may be hosted on one or more communication platforms. The processor may analyze each of the one or more communications. The processor may identify, from the analyzing, content of the one or more communications. The processor may generate a report. The report may indicate the content of the one or more communications. The processor may display the report to the user.