The Unicode standard was supported in the TVirtualQuery component. Three new properties were added in LiteDAC: JournalMode, LockingMode, and Synchronous. In PgDAC, a single TPgConnection object can now be used in multiple threads, and a new property called MultipleConnections allows or denies creation of additional internal connections.
![whats new in rad studio 10.2 whats new in rad studio 10.2](https://www.surfspot.nl/media/wysiwyg/RAD_Studio_10.1_Berlin_screenshot_.png)
#WHATS NEW IN RAD STUDIO 10.2 UPDATE#
Also automatic detection of computed fields when generating update statements was improved in IBDAC. The Over-the-Wire (OTW) encryption feature of InterBase was supported in IBDAC to allow you to secure your data during the transmission process with SSL/TLS encryption. The FindFirst, FindNext, FindLast, and FindPrior methods to search for records in a dataset using filters now work much faster in all DACs. The PrefetchRows property, which allows you to set the number of rows to be prefetched during query execution, was supported in the Direct mode (previously available only in the OCI mode). The data fetch speed was also improved for Oracle and ODBC drivers. The LOBs read/write speed was improved for Oracle, SQL Server, DBF files, and ODBC drivers. We also reduced memory consumption in batch operations for InterBase and Firebird. During batch processing, SQL statements are grouped into a single unit of work, known as a batch, and submitted to the database server in a single call, thereby reducing the network latency. The performance of batch operations was improved in all DAC products. Additionally, PostgreSQL 13 was supported in PgDAC.ĭata access speed with default settings was significantly increased in LiteDAC and the SQLite provider.
#WHATS NEW IN RAD STUDIO 10.2 ANDROID#
DAC products are now also compatible with macOS Big Sur, iOS 14, and Android 11. Both methods fall under radiomics, the data-centric, radiology-based research field.Following the release of RAD Studio 10.4.2 Sydney from last week, we are excited to announce support for the new versions of Delphi and C++ Builder IDEs in our data access components. As layers learn increasingly higher-level features (Box 1), earlier layers might learn abstract shapes such as lines and shadows, while other deeper layers might learn entire organs or objects. It comprises several layers where feature extraction, selection and ultimate classification are performed simultaneously during training. b | The second method uses deep learning and does not require region annotation - rather, localization is usually sufficient. The most robust features are selected and fed into machine learning classifiers. Examples of these features in cancer characterization include tumour volume, shape, texture, intensity and location. a | The first method relies on engineered features extracted from regions of interest on the basis of expert knowledge. This schematic outlines two artificial intelligence (AI) methods for a representative classification task, such as the diagnosis of a suspicious object as either benign or malignant.
![whats new in rad studio 10.2 whats new in rad studio 10.2](https://i.stack.imgur.com/zQoTH.png)
Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks.