Abstract: An apparatus for extracting lithium from a lithium-bearing material. A mixer is configured to receive and mix lithium-bearing material with gypsum, a sulfur-containing material, and a calcium-containing material and form a feed mixture having a moisture content of at least 20 wt %. A dryer is configured to dry the feed mixture and form a dried mixture having a moisture content of less than 20 wt %. A roaster is configured to receive and roast the dried mixture and form a roasted mixture including a water-soluble lithium compound. A leach tank is configured to form a lithium-containing leachate from the water-soluble lithium compound using an aqueous solution.

Abstract: A method of extracting lithium from a lithium-bearing material including: (i) mixing the lithium-bearing material, gypsum, a sulfur-containing material, and a calcium-containing material and forming a feed mixture having a moisture content of at least 20 wt %; (ii) drying the feed mixture to form a dried mixture having a moisture content of less than 20 wt %; (iii) roasting the dried mixture and forming a roasted mixture including a water-soluble lithium compound; and (iv) leaching lithium from the water-soluble lithium compound and forming a lithium-containing leachate by mixing the aqueous solution and the water-soluble lithium compound.

Abstract: A system for automated material master data harmonization that is extremely configurable and easy-to-use solution to standardize, normalize, attribute, rationalize and enrich the organization's material master data using embedded knowledge that leverages enterprise knowledge assets. The system provides various customer centric systems and processes by providing harmonization of data with dependencies of important embodiments such as data classification and MFR-MPN extraction that are not dependent on any other stage. Attribute extraction is dependent on data classification and data sheet definition. Post processing is dependent on data classification, data sheet definition and attributes extraction. Identify L2 dups is dependent on data classification, data sheet definition, attribute extraction and post processing. Non-Source enrichment and Identify L1 dups are dependent on MFR-MPN extraction.

Abstract: A system for automated material master data harmonization that is extremely configurable and easy-to-use solution to standardize, normalize, attribute, rationalize and enrich the organization's material master data using embedded knowledge that leverages enterprise knowledge assets. The system provides various customer centric systems and processes by providing harmonization of data with dependencies of important embodiments such as data classification and MFR-MPN extraction that are not dependent on any other stage. Attribute extraction is dependent on data classification and data sheet definition. Post processing is dependent on data classification, data sheet definition and attributes extraction. Identify L2 dups is dependent on data classification, data sheet definition, attribute extraction and post processing. Non-Source enrichment and Identify L1 dups are dependent on MFR-MPN extraction.

Abstract: A software solution is described for the management of contract obligations. The software deems manual management of contract obligations redundant and hence provides for an error-free, simple and efficient technique to ensure that all parties adhere to the stipulations in the contract. The contract manager can easily monitor the compliance of all contracts from his/her dashboard.

Abstract: Techniques describe preventing sensitive data from being misappropriated during a clipboard operation. A copy operation for data being copied to a clipboard is intercepted. Information describing a first application from which the data was copied is retrieved. The data and the information are stored into the clipboard. A paste operation is evaluated based on the data, and the information is evaluated against a policy to determine whether the paste operation should be blocked.

Abstract: Techniques describe preventing sensitive data from being misappropriated during a clipboard operation. A copy operation for data being copied to a clipboard is intercepted. Information describing a first application from which the data was copied is retrieved. The data and the information are stored into the clipboard. A paste operation is evaluated based on the data, and the information is evaluated against a policy to determine whether the paste operation should be blocked.

Abstract: Techniques describe preventing sensitive data from being misappropriated during a clipboard operation. A copy operation for data being copied to a clipboard is intercepted. Information describing a first application from which the data was copied is retrieved. The data and the information is stored into the clipboard. A paste operation is evaluated based on the data and the information is evaluated against a policy to determine whether the paste operation should be blocked.

Abstract: Techniques describe preventing sensitive data from being misappropriated during a clipboard operation. A copy operation for data being copied to a clipboard is intercepted. Information describing a first application from which the data was copied is retrieved. The data and the information is stored into the clipboard. A paste operation is evaluated based on the data and the information is evaluated against a policy to determine whether the paste operation should be blocked.

Abstract: A computer-implemented method for preventing data loss via temporary-file generating applications may include (1) identifying an application that is configured to update a file by generating a temporary file that includes updated content of the file and replacing the file with the temporary file, (2) detecting an attempt by the application to update the file by detecting an attempt by the application to generate the temporary file and/or an attempt by the application to replace the file with the temporary file, and (3) performing, in response to detecting the attempt by the application to update the file, a data-loss-prevention action on the file instead of the temporary file. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system for automated material master data harmonization that is extremely configurable and easy-to-use solution to standardize, normalize, attribute, rationalize and enrich the organization's material master data using embedded knowledge that leverages enterprise knowledge assets. The system provides various customer centric systems and processes by providing harmonization of data with dependencies of important embodiments such as data classification and MFR-MPN extraction that are not dependent on any other stage. Attribute extraction is dependent on data classification and data sheet definition. Post processing is dependent on data classification, data sheet definition and attributes extraction. Identify L2 dups is dependent on data classification, data sheet definition, attribute extraction and post processing. Non-Source enrichment and Identify L1 dups are dependent on MFR-MPN extraction.

Abstract: Method and apparatuses for detecting violations of data loss prevention (DLP) in NTFS removable media and non-NTFS removable media are described. In NTFS, when an application opens a data file, a DLP file system filter driver internally opens the same data file using file system transaction and is transparent to the application. Application read/writes are redirected to the remote transaction. When the application tries to close the file, the DLP agent scans the data being written to the file for detection of violations and commits or aborts the remote transaction depending upon the detection of a violation of the policy.

Abstract: A data loss prevention (DLP) agent manages DLP polices of a shared network file system. The DLP agent identifies a request by an application to access a file from a shared storage device over a network, and enables monitoring on a local data store to detect file system requests by the application in response to the identifying. The DLP agent also analyzes data associated with the file to determine if the data violates a data loss prevention (DLP) policy, and enforces a response rule associated with the file if the data associated with the file violates the DLP policy.

Abstract: A computer-implemented method for data loss prevention may include: 1) indentifying an external device, 2) intercepting a write attempt to a file on the external device, 3) creating a sandbox version of the file, 4) performing the write attempt on the sandbox version of the file, and then 5) analyzing the sandbox version of the file for potential data-loss violations. Various other methods, systems, and computer-readable media are also disclosed.