A comprehensive dataset and neural network approach for named entity recognition in the Uzbek language.

In this study, the authors presented a dataset for named entity recognition in the Uzbek language. The dataset consists of 2000 sentences and 25,865 words, and the sources were legal documents and hand-crafted sentences annotated using the BIOES scheme. The study is complemented by the fact that the authors demonstrated the applications of the created dataset by training a language model using the CNN + LSTM architecture, which achieves high accuracy in NER tasks, with an F1 score of 90.

Synthesis, structure, Hirshfeld surface analysis, and molecular docking studies of the cocrystal between the Cu(II) complex of salicylic acid and uncoordinated piracetam.

The cocrystal (or supramolecular complex) between the Cu(II) complex of salicylic acid and uncoordinated piracetam has been synthesized. Its structure is characterized by elemental analysis, FT-IR, UV-Vis spectroscopy, and X-ray crystallography. Spectroscopic methods confirm the formation of the metal complex, while X-ray crystallography establishes the molecular and crystal structure of the obtained compound.

Prediction of radiological decision errors from longitudinal analysis of gaze and image features.

Medical imaging, particularly radiography, is an indispensable part of diagnosing many chest diseases. Final diagnoses are made by radiologists based on images, but the decision-making process is always associated with a risk of incorrect interpretation. Incorrectly interpreted data can lead to delays in treatment, a prescription of inappropriate therapy, or even a completely missed diagnosis.

Centerline-guided reinforcement learning model for pancreatic duct identifications.

Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis.

Approach: We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action.

8-Hy-droxy-quinolinium tri-chlorido-(pyridine-2,6-di-carb-oxy-lic acid-κ ,,')copper(II) dihydrate.

The title compound, (CHNO)[CuCl(CHNO)]·2HO, was prepared by reacting Cu acetate dihydrate, solid 8-hy-droxy-quinoline (8-HQ), and solid pyridine-2,6-di-carb-oxy-lic acid (Hpydc), in a 1:1:1 molar ratio, in an aqueous solution of dilute hydro-chloric acid. The Cu atom exhibits a distorted CuONCl octa-hedral geometry, coordinating two oxygen atoms and one nitro-gen atom from the tridentate Hpydc ligand and three chloride atoms; the nitro-gen atom and one chloride atom occupy the axial positions with Cu-N and Cu-Cl bond lengths of 2.011 (2) Å and 2.