Factors affecting the registration and counting of alpha tracks in solid state nuclear track detectors

In view of the fact that the radon progeny contribute the highest to the natural radiation dose to general populations, large scale and long-term measurements of radon and its progeny in the houses have been receiving considerable attention. Solid State Nuclear Track Detector (SSNTD) based systems, being the best suited for large scale passive monitoring, have been widely used for the radon gas (using a cup closed with a semi-permeable membrane) and to a limited extent, for the measurement of radon progeny (using bare mode in conjunction with the cup). These have been employed for radon mapping and indoor radon epidemiological studies with good results. In this technique, alpha tracks recorded on SSNTD films are converted to radon/thoron concentrations using corresponding conversion factors obtained from calibration experiments carried out in controlled environments. The detector response to alpha particles depends mainly on the registration efficiency of the alpha tracks on the detector films and the subsequent counting efficiency. While the former depends on the exposure design, the latter depends on the protocols followed for developing and counting of the tracks. The paper discusses on parameters like etchant temperature, stirring of the etchant and duration of etching and their influence on the etching rates on LR-115 films. Concept of break down thickness of the SSNTD film in spark counting technique is discussed with experimental results. Error estimates on measurement results as a function of background tracks of the films are also discussed in the paper.Factors affecting the registration and counting of alpha tracks in solid state nuclear track detectors K P Eappen* and Y S Mayya Environmental Assessment Division, Bhabha Atomic Research Centre, Mumbai-400 085, India E-mail : [email protected] Assessment Division, Bhabha Atomic Research Centre, Mumbai-400 085, Indi

Persistence of metabolic monitoring for psychiatry inpatients treated with second‐generation antipsychotics utilizing a computer‐based intervention

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135222/1/jcpt12368.pd

Deep learning integrated reinforcement learning for adaptive beamforming in B5G networks

Data availability statement: No data.Copyright © 2022 The Authors. In this paper, a deep learning integrated reinforcement learning (DLIRL) algorithm is proposed for comprehending intelligent beamsteering in Beyond Fifth Generation (B5G) networks. The smart base station in B5G networks aims to steer the beam towards appropriate user equipment based on the acquaintance of isotropic transmissions. The foremost methodology is to optimize beam direction through reinforcement learning that delivers significant improvement in signal to noise ratio (SNR). This includes alternate path finding during path obstruction and steering the beam appropriately between the smart base station and user equipment. The DLIRL is realized through supervised learning with deep neural networks and deep Q-learning schemes. The proposed algorithm comprises of an online learning phase for training the weights and a working phase for carrying out the prediction. Results confirm that the performance of the B5G system is improved considerably as compared to its counterparts with a spectral efficiency of 11 bps/Hz at SNR = 10 dB for a bit error rate performance of 10−5. As compared to reinforced learning and deep neural network with a deviation of ±3o and ±5°, respectively, the DLIRL beamforming displays a deviation of ±2o. Moreover, the DLIRL can track the user equipment and steer the beam in its direction with an accuracy of 92%

Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value