negin manavizadeh

Nowadays, ozone therapy has been proposed as a non-invasive and efficient method to treat many diseases. Excess amounts of this gas are harmful to the lungs and eyes and can have adverse effects on the treatment process. Therefore, it is important to monitor the amount of ozone gas used. In this article, in order to accurately measure and control the amount of this gas, the design and construction of the nanobiomechanical ozone gas sensor has been discussed. In this sensor, zinc oxide active material is used on a glass substrate and aluminum shoulder electrodes. The results of the characterization and measurement of this sensor show that this sensor has the best performance at a temperature of 120 degrees Celsius in the amount of 5 ppm of ozone gas with an efficiency of 84%. Also, the response and recovery time of this sensor has been measured as 15 and 120 seconds, respectively. The remarkable thing about the built sensor is the ability to measure ozone gas up to the minimum value of 1 ppm. Also, it has good selectivity to oxygen and acetone gases in equal amounts, so the response of the sensor to 5 ppm of oxygen and acetone gas is 21% and 31%, respectively. Also, the results showed that the proper design of the sensor packaging, looking at its mechanical aspects, does not make significant changes to the sensor performance.

Due to the damage that air can cause to the intestines and diaphragm, it is essential to prevent air from entering peritoneal dialysis fluid. Bubble detection systems and air infiltration control systems are mandatory components of peritoneal dialysis machines. An infrared sensor is one of the most efficient ways to detect bubbles in the catheter tube. As the dialysis fluid passes through the infrared sensors, an output of approximately 1200 millivolts is produced. The voltage produced by infrared sensors is independent of the device's speed. Approximately 1200 millivolts can be obtained when the motor of the dialysis machine operates at speeds of 50, 40, and 30 RPM. Continuous measurement of this voltage is performed, and the processor checks the correctness of bubble detection by limiting the threshold level of output voltage. This threshold control to detect bubbles in the dialysis machine tubes is 1000 millivolts. If the output voltage drops below this value, the operation of the dialysis machine is prevented to prevent possible injuries to the patient.

In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival.

Feature selection algorithms have been modeled in Matlab R2021a during April and May 2022 in the framework of statistical pattern recognition. First, the features are ranked based on normalized mutual information, as a metric of relevance and redundancy of features, and accordingly, an optimum feature subset with the highest accuracy of classification is selected. Two feature selection algorithms, i.e., inclusion of features enhancing the classification accuracy and exclusion of irrelevant features are applied to the interest datasets, subsequent to the mini-batch k-means clustering of records.

At the end of the execution of both feature selection methods, evaluation metrics including accuracy, precision, recall, and F1 score are measured and compared. Both proposed feature selection approaches for the molecular biology, hepatitis C virus (HCV), and E. coli bacteria datasets result in the precision and recall scores more than 98 percent, meaning that there are few false positives and false negatives in the linear support vector machine (LSVM) classification. Regarding the HCV dataset, selection of nine relevant features among the thirteen present ones using the feature exclusion method yields the classification accuracy and F1 score of 98.92 percent and 99.02 percent, respectively. The feature inclusion approach also results in an accuracy of 98.78 percent with a slight discrepancy.

The results reveal superior strength of the feature selection methods used here for life science datasets with higher-order features such as protein/gene expression database. The potentials to generalize to other classifiers and automatically specify the optimal number of features during the feature selection procedure make these approaches flexible in many data mining applications for the life sciences.

Cu2ZnSnS4 (CZTS), a quaternary material, has attracted the attention of many solar cell researchers due to its non-toxic constituents as well as their abundance on Earth. In this paper, the optical and electrical properties of the pure structure doped with the elements of the fifth group of the periodic table were investigated using the density functional theory and GGA approximation. Comparison of density of states for electrons in addition to band structures in both pure, nitrogen-doped, and phosphorus-doped bulk revealed a reduction in the direct band gap of the structure. The study of the obtained optical properties indicated the absorption coefficient increased from for the pure structure to and , respectively, for the replacement of nitrogen and phosphorus with sulfur atoms considering photons with lower energy. Substitution of arsenic, antimony and bismuth atoms instead of tin atoms, which has the lowest formation energy, causes an impurity level in the band gap due to the deganarated states. As a result, by increasing the number of electron transfer states from the valence to the conduction band, the absorption coefficient is increased to an average of for photons with lower energy in the pure structure. Consequently, reducing the gap in some cases, as well as increasing the absorption coefficient for low-energy photons after the addition of impurities, improves the performance of the CZTS plant as an adsorbent layer in solar cells.

In this paper, to improve the electronic parameters of silicon nanowires, the influence of As and P dopants are investigated. These electronic parameters include transmission spectra, mobility, mean free path and energy band diagram. To study the conduction mechanism and mobility of the Sinanowires, carrier transport along the long nanowires in presence of many defects (including dopant, vacancy, disorderly and surface roughness) has been investigated. In this paper, to simplify the calculations, short Si nanowires with small amount of defects are considered. Therefore, short length silicon nanowires have been doped by As and P and the effects of the dopants have been analyzed. Simulation results demonstrate that, by increasing the energy, transmission spectra increases. It is shown that mobility decreasesin carrier concentration more than and for concentration less than this value, mobility is approximately constant.