This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.

The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.

Представленные результаты исследования проведены с целью определение особенностей краниометрических показателей лицевого черепа зрелого возраста по данным компьютерной томографии. Под исследование попали 40 человек мужского (20) и женского (20) пола от 44 до 60 лет, при спи-рально-компьютерной томографии – которым проводилось в связи с причинами, не связанными с патологией костей черепа (подозрение на инсульт, которое не подтведилось). Исследовались череп-ной индекс, верхний лицевой индекс, ширина лба. В ходе выполнения работы определено, что среднее значение черепного индекса равнялось 77,3±1,85%. Показатели верхнего лицевого индекса составляли 53,37±3,57%, а средняя ширина лба составляла 13,75±0,5х10-2м. Для подавляющего большинства женщин была характерна мезокрания (95% исследуемых). Лишь у 5% определялась долихокрания. Среднее значение черепного индекса для этих женщин равнялось 78,1%. Среднее значение верхнего лицевого индекса составляло 53,37±3,57%. Эти показатели строения лицевого черепа могут быть полезными при планировании оперативных вмешательств в области лицевого черепа, позволяя более точно анализировать индивидуальные особенности пациентов и определять оптимальные подходы к хирургическому лечению. Məqalədə yetkin yaşlı şəxslərdə üz kəlləsinin kraniometrik göstəricilərinin kompüter-tomoqrafiya vasitəsilə tədqiqinin nəticələri təqdim edilmişdir. Tədqiqata kəllə sümüklərinin zədələnmələri ilə əlaqədar olmayan (insulta şübhə olan, lakin diaqnozu təsdiq edilməyən, səbəblərə görə KT müayinəsi aparılmış, yaşı 44-dən 60-a qədədr olan 40 nəfər (20 qadın, 20 kişi) cəlb edilmişdir. Kəllə indeksi, yuxarı üz indeksi və alının eninə ölçüsü tədqiq edilmişdir. Tədqiqat göstərmişdir ki, kraniometrik göstəricilərdən - kəllə indeksi 77,3±1,85%-ə, üz indeksinin qiyməti isə 13,75±3,57%-ə, alının eni isə 15±0,5x102 m-ə bərabərdir. Qadınların böyük əksəriyyətinin (95 %) kəllə quruluşu mezokraniya xarakterli olmuş, yalnız 5%-də dolixokraniya müşahidə edilmişdir. Bu qadınlar üçün yuxarı kəllə indeksinin qiyməti 78,1 % idi. Müəlliflərin fikrincə, kəllə strukturunun bu göstəricilərinin öyrənilməsi həmin sahəyə cərrahi müdaxilələrin planlaşdırılması zamanı faydalı ola bilər. Çünki bu, operativ müdaxilələr zamanı xəstənin fərdi xüsusiyyətlərini nəzərə almağa və müdaxilə prosesinin planlaşdırılmasına optimal yanaşmaya imkan verər. The presented results of the study were conducted to determine the features of the craniometric indicators of the facial skull in mature individuals based on computer tomography data. A total of 40 participants, comprising 20 men and 20 women aged between 44 and 60 years, were included in the study. CT scans were performed on these individuals, and the following cranio-metric parameters were examined: cranial index, upper facial index, width of the upper jaw, and maximum width of the forehead. During the study, it was found that the average cranial index was 77.3±1.85%. The upper facial index showed values of 53.37±3.57%, while the average width of the forehead was 13.75±0.5x10-2 m. Among the female participants, the majority exhibited a mesocranial type of skull (95% of individuals), with only 5% having a dolichocranial type. The average cranial index for these women was 78.1%. The mean cranial index for the entire sample remained consistent at 77.3±1.85%. Additionally, the mean upper facial index was 53.37±3.57%. The upper facial index is indicative of the ratio of the width of the upper face to its length and serves as a valuable tool for analyzing facial profiles. The average forehead width measured 13.75±0.5 x 10-2 m. These facial skull structure indicators can prove valuable in the planning of surgical interventions within the facial skull region, enabling a more precise analysis of individual patient characteristics and the determination of optimal surgical approaches.

Reliable personal identification remains a major challenge in forensic medicine, particularly in cases involving decomposition, thermal injury, or absence of DNA and dental records. In this context, anatomically protected and morphologically unique structures such as the sphenoid sinus may serve as valuable biometric markers. The aim of this study was to evaluate the forensic applicability of sphenoid sinus morphology using computed tomography (CT), automated segmentation, and three-dimensional (3D) computational analysis. The proposed framework integrates CT-based image preprocessing, automated 3D segmentation using nnU-Net architecture, and geometric comparison of reconstructed sphenoid sinus models through point cloud analysis and deep learning approaches. Morphological variability, spatial configuration, and structural stability of the sphenoid sinus were analyzed as discriminative biometric features. The GSA-Net–based identification model demonstrated high recognition performance, achieving Top-1 accuracies exceeding 97.8% and Top-3 accuracies up to 100% in controlled datasets. The results support the concept that the sphenoid sinus possesses sufficient individuality and anatomical preservation to enable reliable ante-mortem and post-mortem identification. The study highlights the potential of integrating automated segmentation and AI-driven 3D analysis into forensic workflows and emphasizes the importance of standardized imaging protocols and larger annotated datasets for future clinical and forensic implementation.