This paper shows that if the off-beam idle detectors in the detection ring of a fourth-generation x-ray computed tomography (CT) system are used to measure the scattered radiation, it is numerically feasible to reconstruct electron-density images to supplement the conventional attenuation-coefficient images of transmitted radiation. It is also shown that by combining these two images, composition changes can be detected with the aid of an effective-atomic-number indicator. The required image-reconstruction algorithms are developed and tested against Monte Carlo simulated measurements, for a variety of phantom configurations. In spite of the relatively poor statistical quality of scattering measurements, it is demonstrated that electron-density images of reasonable quality can be obtained. In addition, it is shown that composition discrimination is possible for materials of effective atomic number greater than five, in the photon energy range of a typical medical x-ray CT system operating at 102 kVp. The obtained supplementary electron-density and composition images can be useful in radiotherapy planning and for studying tumour histology, as well as in industrial and security applications where identification of materials based on density and composition is important.

No abstract

Background
Asynchronous breathing (AB) during mechanical ventilation (MV) may lead to a detrimental effect on the patient's condition. Due to the massive amount of data displayed in a large ICU, a machine learning algorithm (MLA) was proposed extensively to extract the patterns within the multiple continuous-in-time vital signs, to determine which are the variables that will predict the AB, to intervene in the MV as an early warning system, and finally to replace a highly demand of clinician's cognition. 
Objective
This study reviews the MLA for prediction and detection models from vital signs monitoring data for MV intervention. 
Methods
Publication on MLA development on MV intervention based on vital signs monitoring to support clinicians' decision-making process was extracted from the three electronic academic research databases Web of Science Core Collection (WoSCC), ScienceDirect, and PUBMED Central to February 2023. 
Findings
838 papers from the electronic academic research databases are extracted. There are 14 review papers, while 25 related papers that pass with the Quality Assessments (QA). 
Conclusions
Few studies have been published that considered VS monitoring data along with the MV parameters waveforms for MV intervention. Vital signs monitoring data is not the only predictor in the developed MLA. Most studies suggested that developing the MLA for direct MV intervention requires more concern in the pre-processing of real-time data to avoid false positive and false detection than developing MLA itself. 


Hemodialysis is one of the medical treatment methods for the patients with end stage renal disease. It is conducted through the use of artificial kidney, located outside of the human body. During the hemodialysis therapy, small air bubbles may infiltrate the blood tubing and coalesce to perform the larger bubble which can be harmfull for the patient if entering the patient's blood circulatory system.The objective of this work is to develop an capacitance based air bubble detection system for hemodialysis machine. The research method covers conceptual design, detail design, prototyping and performance testing for the capacitance sensor as well as the data acquisition system.The validation test was conducted to verify the functionality of the system. For testing purposes, an experimental setup was constructed and several test runs were conducted. Comparison between measurement conducted using capacitance technique and using ultrasonic technique was also presented in this work. It shows that close agreement between the results obtained using ultrasonic technique and capacitance technique is observed in this work.

Reaktor Serba Guna-G.A. Siwabessy (RSG-GAS reactor) is a pool type research reactor, located in Serpong village, Indonesia. For safety of RSG-GAS reactor, the maintenance level criterion of System, Structure and Component (SSC) can be carried out by assessing on occurrence likelihood of a potential risks due to ageing degradation and a Risk Priority Number (RPN) value for assigning maintenance criterion, respectively. The objective of present study is to propose using the Failure Mode and Effects Analysis (FMEA) for assigning the maintenance level criterion of SSC and implementing on JE01-AP01 primary pump within a RSG-GAS reactor. In this research paper, SSC screening and failure based maintenance methodology is identified and classified into suitable classes at radar diagram. Results show that SSC can be prioritized based on failure analysis so that maintenance level criterion for SSC can be determined. On the implemented study, preventive maintenance can be performed to JE01-AP01 primary pump because of its RPN value 270.