A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.

Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues.

Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images.

A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks.

Energy consumption prediction in an office building by examining occupancy rates and weather parameters using the moving average method and artificial neural network.

Occupancy rate refers to the level of usage and presence of individuals within a building or a specific space. This factor can have a significant impact on building energy consumption. When the occupancy rate in a building is high, naturally, energy consumption also increases.

Thermogravimetric and thermo-kinetic analysis of sugarcane bagasse pith: a comparative evaluation with other sugarcane residues.

In this study, thermogravimetric and thermo-kinetic analysis of sugarcane bagasse pith (S.B.P.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron.

Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events.The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes.

Investigation of chalcopyrite removal from low-grade molybdenite using response surface methodology and its effect on molybdenum trioxide morphology by roasting.

In this research, purification of molybdenite concentrate (MoS) using a nitric acid leaching process was employed for the improvement of molybdenum trioxide morphology during oxidative roasting in an air atmosphere. These experiments were performed using 19 trials designed with response surface methodology and three effective parameters being temperature, time, and acid molarity. It was found that the leaching process reduced the chalcopyrite content in the concentrate by more than 95%.

A systematic study on hydrogen bond interactions in sulfabenzamide: DFT calculations of the N-14, O-17, and H-2 NQR parameters.

A systematic computational study was carried out to characterize the hydrogen bond, HB, interactions of sulfabenzamide crystal structure by DFT calculations of electric field gradient, EFG, tensors at the sites of 14N, 17O, and 2H nuclei. The computations were performed with the B3LYP and B3PW91 DFT methods and 6-311+G and 6-311++G* standard basis sets using the Gaussian 98 package. To perform the calculations, a hydrogen-bonded heptameric cluster of sulfabenzamide was created by X-ray coordinates where the hydrogen atom positions were optimized and the EFG tensors were calculated for the target molecule.