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The radon concentration activity in buildings is influenced by various factors, including meteorological elements like temperature, pressure, and precipitation, which are recognized as significant influencers. The fluctuations of indoor radon in premises are related to seasonal change. This study aimed to understand better the effects of environmental parameters on indoor radon concentration levels in the Aksu school. Indoor and outdoor temperature differentials heavily influence diurnal indoor radon patterns. The analysis indicates that the correlation between indoor radon and outdoor temperature, dew point, and air humidity is weak and negligible for atmospheric pressure, wind speed, and precipitation, as determined by the obtained values of R2 and the Chaddock scale. The multiple regression model is characterized by the correlation coefficient rxy = 0.605, which corresponds to a close relationship on the Chaddock scale.

Northern Kazakhstan is considered a field of uranium, gold mining, copper–molybdenum ore, and other metals. The aims of the current work were to monitor the indoor radon levels in a school and a kindergarten and to determine the levels of teacher and student exposure to radon. High radon concentrations were detected in the school on the first floor at ca. 9600 Bq/m3, on the second floor at ca. 6800 Bq/m3, on the third floor at ca. 4900 Bq/m3, and in the kindergarten, the concentration was ca. 9500 Bq/m3. The annual effective dose of the students and teachers of the school and kindergarten varied from 4 mSv/y to 9 mSv/y, which is an order of magnitude higher than the upper annual dose limit. The excess lifetime cancer risk was 14–20% for students, 31.1% for school and kindergarten staff, and 34.9% for kindergarten children. The indoor radon concentrations varied with weather conditions, and it was evident that ventilation had a significant effect on the reduction in the concentration. At these premises, positive correlations between the radon concentrations, outside temperature, and relative humidity were obtained, showing that the concentration of radon is influenced by meteorological parameters. This study will help to identify buildings where continuous monitoring is needed in order to reduce indoor radon levels.

This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R 2 , mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matérn 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.

This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results., Competing Interests: The authors declare no conflicts of interest.

Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.

Epilepsy is a group of neurological disorders characterized by abnormal electrical activity in the central nervous system (CNS) and recurrent seizures representing the principal clinical manifestation. Sudden unexpected death in epilepsy (SUDEP) is the predominant cause of death in young epileptic patients. SUDEP patients displayed an increased cardiovascular (CV) risk, probably due to an impaired autonomic control of CV functions, but the underlying mechanisms need to be explored yet. Therefore, we aimed to examine the cardiac morphological alterations in a pentylenetetrazol (PTZ)-kindled rat model, a well-established tool for studying chronic epilepsy. To complete this, the distance between the atria, between the atrium and ventricle were measured, the heart was weighed, and the pathological morphology of dissected hearts was analyzed by histological assessment with hematoxylin and eosin staining. A significantly decreased distance between atria and a significant increase in heart weight were observed in PTZ-kindled rats which interestingly also displayed increased hemorrhagic content when compared with controls. Our findings provided evidence that changes in cardiac morphology may be related to autonomic CV dysfunctions occurring during SUDEP while also opening up more avenues to better develop novel drugs for the treatment of this disorder.