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This study proposes a hybrid analytical framework for detecting chondromalacia using vibroacoustic (VAG) signals from patients with knee osteoarthritis (OA) and healthy controls (HCs). The methodology combines nonlinear signal decomposition, feature extraction, and deep learning classification. Raw VAG signals, recorded with a custom multi-sensor system during open (OKC) and closed (CKC) kinetic chain knee flexion–extension, underwent preprocessing (denoising, segmentation, normalization). Ensemble Empirical Mode Decomposition (EEMD) was used to isolate Intrinsic Mode Functions (IMFs), and Detrended Fluctuation Analysis (DFA) computed local (α1) and global (α2) scaling exponents as well as breakpoint location. Frequency–energy features of IMFs were statistically assessed and selected via Neighborhood Component Analysis (NCA) for support vector machine (SVM) classification. Additionally, reconstructed α1/α2-based signals and raw signals were converted into continuous wavelet transform (CWT) scalograms, classified with convolutional neural networks (CNNs) at two resolutions. The SVM approach achieved the best performance in CKC conditions (accuracy 0.87, AUC 0.91). CNN classification on CWT scalograms also demonstrated robust OA/HC discrimination with acceptable computational times at higher resolutions. Results suggest that combining multiscale decomposition, nonlinear fluctuation analysis, and deep learning enables accurate, non-invasive detection of cartilage degeneration, with potential for early knee pathology diagnosis.

Psychotic disorders such as schizophrenia (SCH) and bipolar affective disorder (BD) are associated with lipid metabolism abnormalities and inflammatory dysregulation. The niacin skin flush test (NSFT) has long been investigated as a non-invasive indicator of these disturbances. This study used deep learning models to assess the diagnostic utility of SKINREMS, a computerized system for automated temporal analysis of skin flush responses. The study included a total of 188 participants, comprising individuals with psychotic disorders and healthy controls. Sequential skin images were recorded after topical application of methyl nicotinate. Five convolutional neural network architectures—ResNet50, ResNet101, DenseNet121, InceptionV3, and EfficientNetB0—were evaluated for their performance in analyzing these time-dependent dermatological responses in a binary classification task. Accuracy, precision, recall, F1-score, and AUC were calculated at four time points (frames 1, 10, 20, 30). The models demonstrated distinct temporal performance profiles. ResNet50 showed consistent high performance across all time points, making it suitable for clinical environments requiring stable predictions. DenseNet121 achieved the highest AUC (up to 0.99) after 15 min, indicating its potential in extended monitoring. EfficientNetB0 offered gradual performance improvement with lower computational demands, while InceptionV3 was most effective at intermediate time points. ResNet101 showed initial high performance but declined mid-phase. AUC remained stable across all models, suggesting robust discriminative capability over time. This study highlights the importance of selecting appropriate deep learning architectures based on the temporal dynamics of biological responses. The findings demonstrate potential for future clinical application of AI in non-invasive diagnostics of psychotic spectrum disorders.

This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%.

In the article, the authors attempted to analyze the impact of such materials factors as surface emissivity, surface roughness, air gap thickness, and type of concrete on heat transport in the microstructure of vertical multilayer building walls. The surface analysis conducted using three-dimensional modeling tools provided information about the formation of its microstructure before and after the application of a reflection-smoothing coating, which has a direct impact on the emissivity of the surface and was reduced from 0.93 to 0.29. Thermal analyses demonstrated that after applying the reflective coating, thermal resistance increased significantly in the air gap, by approximately 86%, which resulted in a 28% improvement of the evaluated walls samples. The studies have shown that increasing the gap thickness between concrete and thermal insulation results in a thermal resistance increase. It is feasible to enhance the thermal insulation of walls while simultaneously reducing their thickness, a development that holds significant potential for application in the production of prefabricated sandwich panels. The statistical analyzes performed showed significant differences between the analyzed configurations.

Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

This work experimentally addresses damage calibration of an unmanned aerial vehicle in operational condition. A wide range of damage level and types are simulated and controlled by an electric motor via pulse width modulation in this regard. The measurement is carried out via established protocols of using a piezo-patch on one of the 8 arms, utilising the vibration sensitivity and flexibility of the arms, demonstrating repeatability of such protocol. Subsequently, recurrence analysis on the voltage time series data is performed for detection of damage. Quantifiers of damage extent are then created for the full range of damage conditions, including the extreme case of complete loss of power. Experimental baseline condition for no damage condition is also established in this regard. Both diagonal-line and vertical-line based indicators from recurrence analysis are sensitive to the quantitative estimates of damage levels and a statistical test of significance analysis confirms that it is possible to automate distinguishing the levels of damage. The damage quantifiers proposed in this paper are useful for rapid monitoring of unmanned aerial vehicle operations of connection.

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor.

This paper focuses on the influence of radial internal clearance on the dynamics of a rolling-element bearing. In the beginning, the 2—Degree of Freedom (DOF) model was studied, in which the clearance was treated as a bifurcation parameter. The derived nonlinear mathematical model is based on Hertzian contact theory and takes into consideration shape errors of rolling surfaces and eccentricity reflecting real operating conditions. The analysis showed characteristic dynamical behavior by specific clearance range, which reflects others in a low or high amplitude and can refer to the optimal clearance. The experimental validation was conducted with the use of a double row self-aligning ball bearing (SABB) NTN 2309SK in which the acceleration response was measured by various rotational velocities. The time series obtained from the mathematical model and the experiment were analyzed with the recurrence quantification analysis.

Despite global control efforts, tuberculosis remains the leading infectious cause of death, with rising incidence, pediatric cases, and drug-resistant strains posing major public health challenges. Mycobacteria, including Mycobacterium tuberculosis , possess a lipid-rich, dual-membrane cell envelope that contributes to their impermeability, drug resistance, and unique pathogenic mechanisms. Some lipids play key roles in modulating host immune responses, enabling survival within macrophages, and promoting granuloma formation. Since it is known that lipid remodeling of the cell envelope is correlated with the antibiotics tolerance in mycobacteria we used liquid chromatography coupled to mass spectrometry to analyze the lipid profiles of M. tuberculosis clinical isolates with diverse drug-resistance characteristics in order to investigate if there is any link between Mtb lipids composition, its drugs susceptibility and the antimycobacterial activity of natural small molecules used in combination with first line antibiotics. The results showed that among cross combinations of antibiotics and natural products (piperine and thymoquinone) the potentiation of antimycobacterial activity was obtained in all strains only for rifampicin. Drug-resistant isolates presented the shift in glycerophospholipids building the inner membrane towards molecules with shorter acyl chains, but the decreased membrane hydrophobic thickness was compensated in some strains by increased membrane rigidity. The pXDR/XDR isolates accumulated mycobactins loaded iron and showed dysregulation in the production of phthiocerol/phthiodiolone dimycocerosates and triacylglycerols.