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Problems related to ventral hernia repairs (VHR) are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study is based upon data from 3339 patients from different European countries observed during the last 12 years (2012–2023), which were collected by specialists in hernia surgery. Most patients underwent standard surgical procedures, with a growing trend towards laparoscopic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair. Appropriate mathematical methods are employed to extract and classify the selected features, with emphasis on computational and machine-learning techniques. The paper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods. The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patients in the database. The accuracy of separating GHR mesh surgery with and without postoperative complications reached 74.4% using a two-layer neural network classification. Robotic surgeries represent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests both an interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell's membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks. [ABSTRACT FROM AUTHOR]

The integration of computational intelligence and augmented reality has become increasingly prevalent in dental practices. Three-dimensional (3D) printing techniques have now become routine in orthodontics, prosthetics, and maxillofacial surgery. The objective of this study is to assess the effectiveness of stereolithography (SLA) printed models compared to traditional plaster casts over a three-year period. The experimental dataset consisted of 36 orthodontic patients, each with SLA printed models and plaster casts for both the upper and lower jaws, resulting in a total of 72 scans in the form of Standard Template Library (STL) files and 72 traditional impressions. The upper dental arch models were constructed using an SLA 3D printer, employing a blue 405 nm laser beam to solidify a liquid polymer. The classical plaster casts were prepared in a dental laboratory. The models were retained for long-term orthodontic therapy control. To evaluate the differences between the dental models, a laboratory scanner was used to generate virtual casts. The meshes obtained were adjusted and pre-aligned using the best-fit algorithm. Subsequently, registration of the models was performed using the iterative closest point (ICP) algorithm. Distances between the point clouds and meshes for each point of the printed model were calculated by determining the nearest triangle on the reference mesh (cast). Additionally, the model surfaces were assessed using a scanning electron microscope and a stereomicroscope. The results from 22 experimental datasets indicated a high level of agreement between the plaster casts and virtual surfaces, with a mean absolute difference of 0.018 mm and a standard deviation of 0.17 mm. These values were obtained by averaging 231,178 data points for each comparison. Overall, this study demonstrates the comparability and accuracy of SLA printed models in relation to traditional plaster casts, supporting their potential as reliable alternatives in dental practice. [ABSTRACT FROM AUTHOR]

Augmented reality has a wide range of applications in many areas that can extend the study of real objects into the digital world, including stomatology. Real dental objects that were previously examined using their plaster casts are often replaced by their digital models or three-dimensional (3D) prints in the cyber-physical world. This paper reviews a selection of digital methods that have been applied in dentistry, including the use of intra-oral scanning technology for data acquisition and evaluation of fundamental features of dental arches. The methodology includes the use of digital filters and morphological operations for spatial objects analysis, their registration, and evaluation of changes during the treatment of specific disorders. The results include 3D models of selected dental arch objects, which allow a comparison of their shape and position during repeated observations. The proposed methods present digital alternatives to the use of plaster casts for semiautomatic evaluation of dental arch measures. This paper describes some of the advantages of 3D digital technology replacing real world elements and plaster cast dental models in many areas of classical stomatology. [ABSTRACT FROM AUTHOR]

Intra-oral scanning technology has brought a completely new approach to examination methods in dentistry. In comparison to traditional plaster casts, it allows more precise digital analysis of dental arch components during the treatment of dental disorders. Data acquired can also be used for the creation of three-dimensional (3D) models using 3D printers. This paper describes a data acquisition system, the mathematical processing of resulting three-dimensional model, and the statistical analysis of selected parameters of the dental arch. The study aims at comparing the results achieved from 20 models of the same individual acquired by different specialists. The proposed methodology includes mathematical rotation of objects into the optimal plane defined by the teeth tops' location to increase the accuracy of the resulting parameters. The mean evaluated distances between canines (3-3) and premolars (5-5) are 24.29 mm and 37.98 mm, respectively, for the lower dental arch. The proposed object rotation increased these distance by 0.9% and 1.4%, respectively, reducing the rotation error. Moreover, the variability of results decreased and the mean value of the coefficient of variation was lowered by 12%. Image registration was then used to evaluate changes of dental arch parameters. This paper presents the advantages of digital models for visualisation of the dental arch allowing its flexible rotation and evaluation of its parameters. [ABSTRACT FROM AUTHOR]

Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man–machine interaction. [ABSTRACT FROM AUTHOR]

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction. [ABSTRACT FROM AUTHOR]

Background: Plaster casts of individual patients are important for orthodontic specialists during the treatment process and their analysis is still a standard diagnostical tool. But the growing capabilities of information technology enable their replacement by digital models obtained by complex scanning systems. Method: This paper presents the possibility of using a digital camera as a simple instrument to obtain the set of digital images for analysis and evaluation of the treatment using appropriate mathematical tools of image processing. The methods studied in this paper include the segmentation of overlapping dental bodies and the use of different illumination sources to increase the reliability of the separation process. The circular Hough transform, region growing with multiple seed points, and the convex hull detection method are applied to the segmentation of orthodontic plaster cast images to identify dental arch objects and their sizes. Results: The proposed algorithm presents the methodology of improving the accuracy of segmentation of dental arch components using combined illumination sources. Dental arch parameters and distances between the canines and premolars for different segmentation methods were used as a measure to compare the results obtained. Conclusion: A new method of segmentation of overlapping dental arch components using digital records of illuminated plaster casts provides information with the precision required for orthodontic treatment. The distance between corresponding teeth was evaluated with a mean error of 1.38% and the Dice similarity coefficient of the evaluated dental bodies boundaries reached 0.9436 with a false positive rate FPR = 0.0381 and false negative rate FNR = 0.0728. [ABSTRACT FROM AUTHOR]

Background Diagnostic orthodontic and prosthetic procedures commence with an initial examination, during which a number of individual findings on occlusion or malocclusion are clarified. Nowadays we try to replace standard plaster casts by scanned objects and digital models. Method Geometrically calibrated images aid in the comparison of several different steps of the treatment and show the variation of selected features belonging to individual biomedical objects. The methods used are based on geometric morphometrics, making a new approach to the evaluation of the variability of features. The study presents two different methods of measurement and shows their accuracy and reliability. Results The experimental part of the present paper is devoted to the analysis of the dental arch objects of 24 patients before and after the treatment using the distances between the canines and premolars as the features important for diagnostic purposes. Our work proved the advantage of measuring digitalized orthodontic models over manual measuring of plaster casts, with statistically significant results and accuracy sufficient for dental practice. Conclusion A new method of computer imaging and measurements of a dental stone cast provides information with the precision required for orthodontic treatment. The results obtained point to the reduction in the variance of the distances between the premolars and canines during the treatment, with a regression coefficient RC= 0.7 and confidence intervals close enough for dental practice. The ratio of these distances pointed to the nearly constant value of this measure close to 0.84 for the given set of 24 individuals. [ABSTRACT FROM AUTHOR]

We investigated the parameters of colored noise in EEG data of 17 722 professional drivers aged 18–70. The whole study is based upon experiments showing that biological neural networks may operate in the vicinity of the critical point and that the balance between excitation and inhibition in the human brain is important for the transfer of information. This paper is devoted to the study of EEG power spectrum which can be described best by a power function with 1/fλ distribution and colored noise corresponding to the critical point in the EEG signal has the value of λ = 1 (purple noise). The slow accumulation of energy and its quick release is a universal property of the 1/f distribution. The physiological mechanism causing energy dissipation in the brain seems to depend on the number and strength of the connections between clusters of neurons. With ageing, the number of connections between the neurons decreases. Learning ability and intellectual performance also decrease. Therefore, age-related changes in the λ coefficient can be anticipated. We found that absolute values of λ coefficients decrease significantly with increasing age. Deviations from this rule are related to age-dependent slowing of the dominant frequency in the alpha band. Age-dependent change in the parameter and colored noise may be indicative of age-related changes in the self-organization of brain activity. Results obtained include (i) the age-dependent decrease of the absolute values of the average λ coefficient with the regression coefficient 0.005 1/year, (ii) distribution of λ value changes related to EEG frequency bands and to localization of electrodes on the scalp, and (iii) relation of age-dependent changes of colored noise and EEG energy in separate frequency bands. [ABSTRACT FROM AUTHOR]

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding. [ABSTRACT FROM AUTHOR]

The paper is devoted to computer simulations of the distribution and time evolution of the temperature in a wooden house in summer. The goal of simulations was to assess the effect of covering walls inside the house with a PCM (phase change material) on its passive cooling, which prevents the undesired overheating of the house and provides the required thermal comfort for the occupants under warm summer days. Computer simulations were performed by the COMSOL Multiphysics software (COMSOL Inc., Stockholm, Sweden). A model of a house without the PCM coverage was compared with models of houses in which the PCM was located on all walls, except a floor, and on a wall opposite the window. Results of simulations proved that the wood wall thickness and PCMs location influence overheating the wooden house. Under studied conditions, the coverage of a wall opposite the window best eliminated extremes of the air temperature inside the house. The maximum temperature decrease was 3.9 ° C (i.e., drop of 31.1%) comparing the house which wall opposite the window was covered by the PCM and the house without the PCM coverage. [ABSTRACT FROM AUTHOR]

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. [ABSTRACT FROM AUTHOR]

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20–35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an F 1 score of 92.2% Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data. [ABSTRACT FROM AUTHOR]

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities. [ABSTRACT FROM AUTHOR]

This paper is devoted to modelling of temperature distribution and its time evolution in rooms with specific thermal insulation and heat transfer for different external conditions. The simulation results should help to design the room architecture and wall materials to reduce energy losses due to heating or cooling, and to increase the inside thermal comfort. For this purpose, a methodological procedure using real data processing in the COMSOL Multiphysics modelling environment and spatial visualization of temperature evolution is proposed. This paper describes a mathematical model for simulation of the temperature evolution inside a space with thermally insulated walls under selected outside conditions. Computer simulations are then used to assess the temperature distribution inside the room and the heat flow through the room walls. Results of the simulations are used for subsequent determination of the time needed for the desired decrease of air temperature inside the tested room during its cooling due to the low ambient temperature, which is related to the thermal stability of the building, specific heat capacity, and thickness of the thermal insulation. Under the studied conditions, the time to reach the temperature drops by 20 percent in a room with windows was from 1.4 to 1.8 times lower than that in the room without windows. The proposed methodology shows the flexibility of computer modelling in the design of insulated building systems. The mesh density testing was performed by comparing the air temperature evolution in the model of the selected mesh density and the model with its maximum value enabled by the size of computer memory. The maximum temperature deviation calculated for the mesh of the presented model was 0.57%. [ABSTRACT FROM AUTHOR]

Fluidized bed porosity ɛ is a primary property of fluidized systems when determining the minimum floating velocity. The air flow rate in the fluidized bed (or in the fluid layer of the material) increases with diminishing bed porosity. This paper is devoted to porosity calculations for a fluidized bed consisting of spherical particles having different diameters (2, 4, 6, 8, 10 mm) and in differently shaped polygonal fluidized bed cells possessing different characteristic particle floating velocities. For testing purposes, porosity was experimentally measured and subsequently modelled by simulation using the Rocky code. Cells with regular triangular, tetragonal (square-shaped), pentagonal, hexagonal, heptagonal and circular cross sections were used for the experiment. All the cells possessed the same cross-section area S = 1256 mm². The weight of the spherical particle batch in the experiments was constant, 2 kg, for all of the fluidized bed cell cross section shapes described above.

The present article deals with investigation of geometric properties of surface modified titanium white with the help of silica oxide by various methods of shape and size identification of clusters made by processing by fluidisation. For the purpose of the investigation of geometric properties the artificially made titanium oxide (titanium white) was processed by fluidisation with a defined percentage of silica oxide additive. The selected additive was represented by hydrophilic pyrogenic silica (micronised silica oxide), known under commercial name Aerosil 200, Aerosil R972 and hydrophilic pyrogenic metal oxide Aeroxide P25. The investigation began by image acquisition of the individual additives and the titanium white with scanning electron microscope and continued by investigation of clusters created by fluidisation in a vertical fluidisation cell using state-of-the-art methods of particle size identification analysis. The research was oriented toward the area of mutual impact of particles in the titanium white clusters with particles of additives.

Background: Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space., Methods: The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data., Results: The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications., Conclusions: Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.