Saša Cvetković

Objective . To develop and validate a continuous, mathematically well-defined model for representing ventilatory dynamics during incremental treadmill exercise that overcomes the limitations of conventional linear, exponential, hyperbolic sine, and breakpoint-based approaches, while retaining interpretability and robustness at the signal level for modeling and system identification tasks. Approach. We propose the Dual-threshold muscular breathing inequality (MBI) model, a parametric formulation that represents minute ventilation as a function of oxygen uptake using a linear baseline combined with two logistic transition components. This structure enables explicit parameterization of transition timing, magnitude, and sharpness within a single smooth and differentiable function. Model parameters were estimated using a hybrid global–local optimization strategy designed to ensure numerical stability and practical identifiability. The model was evaluated using a large dataset of 981 maximal treadmill cardiopulmonary exercise tests, comprising over 420 000 breath-by-breath data points. Performance was benchmarked against established linear, exponential, hyperbolic sine, PWL, and continuous nonlinear models using goodness-of-fit metrics, information criteria, and non-parametric statistical comparisons at both pooled and individual levels. Main results . The proposed MBI model provided a stable and accurate representation of ventilatory dynamics across the full range of exercise intensity. It achieved strong descriptive performance at both population and individual levels, maintained robustness across intensity domains, and demonstrated statistically significant advantages over competing formulations. The model yielded interpretable parameters that separated transition location from transition magnitude and sharpness, revealing conserved features of ventilatory organization alongside highly variable response kinetics. Systematic differences between MBI-derived transition midpoints and conventional breakpoint estimates were observed, reflecting differences in mathematical definitions rather than inconsistencies in the underlying data. Significance. By modeling ventilatory transitions as continuous processes rather than discrete events, the dual-threshold MBI framework provides a compact, model-based approach for quantitative analysis and parameter estimation of ventilatory time-series data in treadmill cardiopulmonary exercise testing. The proposed formulation supports reproducible system identification and comparative analysis across individuals and conditions, offering a practical engineering-oriented tool for measurement-based analysis of treadmill exercise ventilation.

Distributed Denial-of-Service (DDoS) attacks remain a serious threat to web servers, causing significant consequences. The Tor network, designed for privacy, is increasingly being exploited for launching DDoS attacks. This paper examines the challenges of detecting and mitigating DDoS attacks originating from the Tor network. It analyzes the characteristics of Tor that make it attractive to attackers, existing detection and mitigation methods, and the integration of protective mechanisms-rate limiting, iptables, and blocking Tor exit nodes-to reduce the impact of such attacks. The combination of these techniques for improved protection is considered, and their effectiveness in defending against Tor-based DDoS attacks is evaluated. It has been observed that many websites lack even basic defense mechanisms and that even fundamental DDoS protection can be beneficial.

This paper explores the intricate dynamics surrounding the motivation and perspectives of university students born after the mid-1990s (Generation Z). It brings to light the generation gap and the clash between the expectations of educators and the realities of changing student interests. The paper uncovers a new paradigm where many students lack intrinsic motivation, struggle with study commitment, and prefer easy solutions, often relying on readily available information without critical evaluation. If left unaddressed, these trends pose profound challenges to the educational system, potentially leading to a society reliant on information consumerism. Additionally, the paper examines the role of educators in adapting to these changes, emphasizing the need for a holistic approach to education beyond mere knowledge transfer. Strategies to address these challenges are discussed, highlighting the importance of redefining the role of educators, cultivating intrinsic motivation and critical thinking skills, and fostering a more dynamic, effective, and supportive learning environment aided by student selection based on a genuine interest in study subjects and motivation to study.

This paper explores the application of artificial intelligence (AI) in education to improve teaching processes. Through the analysis of relevant literature, various aspects of the application of AI technologies in the educational sector are explored. It has been found that AI can provide a personalized academic experience and more efficient information retrieval; however, AI answers are perceived as often lacking in accuracy and correctness and should not be unquestioningly trusted. The research instrument was an online survey given to 105 university students with several types of questions to discover whether our students were familiar with the concept of AI tools. Through the analysis of the available literature and the student’s response, it is concluded that a viable option to improve the learning and teaching process is a balance between traditional teaching methods and the application of AI technologies, where AI could support teachers through dynamic and interactive lessons and provide additional resources tailored to individual student needs. At the same time, the need for careful development and use of AI technologies concerning ethical principles and continuous expert supervision is strongly emphasized, underlining the crucial role of educators, researchers, and policymakers in ensuring responsible AI use in education.

The wavelet spectral characteristics of three respiratory muscle signals (scalenus (SC), parasternal intercostal (IC), and rectus abdominis (RA)) and one locomotor muscle (brachioradialis (BR)) were analyzed in the time–frequency (T-F) domain during voluntary breath-holding (BH), with a focus on the physiological breakpoint that is commonly considered the first involuntary breathing movement (IBM) that signals the end of the easy-going phase of BH. The study was performed for an end-expiratory BH physiological breaking point maneuver on twelve healthy, physically active, naive breath-holders/apneists (six professional athletes; six recreational athletes, and two individuals in the post-COVID-19 period) using surface electromyography (sEMG). We observed individual effects that were dependent on muscle oxygenation and each person’s fitness, which were consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch glycolytic (type 2) muscle fibers. Professional athletes had longer BH durations (BHDs) and strong hypercapnic responses regarding the expiratory RA muscle, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and is dependent on the BHD. This is in contrast with recreational athletes, who had strong hypoxic responses regarding inspiratory IC muscle, which is activated faster and gradually in the frequency range of 250–450 Hz (independent of the person and BHD). This pilot study preliminarily indicates that it is possible to noninvasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining the T-F features of elevated myoelectric IC and RA activity during BH.

The wavelet spectral characteristics of three respiratory muscle signals (Scalenus (SC), Parasternal intercostal (IC) and Rectus abdominis (RA)) and one locomotor muscle Brachioradialis (BR) were analyzed in the time-frequency (T-F) domain during breath-holding (BH). Study was performed for an end-expiratory BH maneuver on twelve healthy, physically active, naive apneists (6 professional athletes; 6 recreational athletes, two in the post-COVID-19 period) using surface electromyography (sEMG). We observe individual effects dependent on muscle oxygenation and person’s fitness, consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch (oxidative) glycolytic (type 2) muscle fibers. Professional athletes had longer BH duration (BHD) and strong hypercapnic response on expiratory RA, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and dependent on BHD. This is in contrast with recreational athletes, who had strong hypoxic response on inspiratory IC, which is activated faster and gradually in the frequency range of 250-450 Hz (independent of the apneist and BHD). This pilot study preliminarily indicates that it is possible to non-invasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining T-F features of elevated myoelectric IC and RA activity during BH.

Lateral Chromatic Aberration is a defect of optical lenses in connection with focusing different wavelengths of light (red, blue, green) at different locations in the focal plane, which leads to resolution loss and color artifacts (fringes). To preserve image fidelity, we have developed a robust multi-phase unsupervised method (not using a reference image for calibration and without prior knowledge of the lens), that is based on edge matching in different color planes. We are able to estimate parameters of a lateral chromatic aberration correction model, which is then used to avoid these artefacts by correct resampling of red and blue color planes with sub-pixel accuracy. This method is suitable for real-time implementation in consumer-grade image/video systems and shows very promising results.

Modern diagnostics of athlete preparation involves the acquisition ofalargenumberof data,whichrequiressuperiorknowledge,serious logistics, protocols, staff, time, etc. Technological breakthroughs in surface electromyography (sEMG) in measuring the activities of respiratory muscles in vivo opened new possibilities in this direction. The correlation between physical preparedness and the ability to maintain breath has been a theoretical phenomenon for over a century.The result at the duration of the breath holding time (BHT) is generally considered a positive indicator of the volume of respiratory capacity during physical activity. Experimental research determined involuntary activities of auxiliary respiratory musculature at the end of the quiet retention of breath and are determined as a physiological break point of breath holding. The time from the start of the breath holding to the first involuntary breathing movement (IBM) is called the control pause (CP). Since this time is not the physiological maximum of breath holding, it is very important to determine the exact moment of reaching the first IBM and the time-frequency characteristics of sEMG signals during the IBM phase (work problem). Using Wavelet methodology, the analysis of sEMG signals is performed on three skeletal muscles, two inhaling (M. Scalenus - Anterior et Medium - SC, and M. Parasternal Intercostales-IC) and one exhalatory (M. Rectus abdominis - Ra), that in addition to others, have auxiliary role and function in the respiratory cycle, and that are sensitive to physiological changes due to apnea, so in their neuromotor response are a possible indicator of metabolic processes that are detected as involuntary breathing movements. Multiple growths in the electrical activity of these muscles during IBM in certain frequency ranges have enabled precise IBM measurement, thus determining the physiologically acceptable duration of the CP. Observation and analysis of the specific respiratory and muscular response indicate dominations of hypoxic or hypercapnic metabolic condition (subject of research). Based on monitoring these changes in 12 subjects classified in the group categorized amateur athletes, it was determined that better-trained subjects have longer CP and react hypercapnically. The conclusions of this non-experimental case study correspond to the practice of training preparation but open the space for new research, primarily those who should develop an affordable method for non-invasive realtime physical preparation.

One of the most popular image denoising methods based on self-similarity is called nonlocal means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings, e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable candidates for some nonrepetitive patches. In this paper, we propose to improve NLM by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework. Experimental results show that the proposed technique can perform denoising better than the original NLM both quantitatively and visually, especially when the noise level is high.

For real-time imaging with digital video cameras and high-quality display with TV systems, the obtained picture quality including visibility of details, local contrast, and absence of artifacts, is very important to ensure user quality acceptance. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Then we discuss the computation of commonly used local energy measurements and show that a selection of those measurements can be calculated efficiently. In our first contribution, we propose a new local energy measurement APS that can be calculated more efficiently than the existing metrics in a 2-D-separable fashion. In addition, we also show that the new APS measurement gives better performance than standard energy measurements. The second contribution is the use of local contrast and a modified contrast gain formula that can substantially improve the overall algorithm performance, especially when a high-level contrast enhancement is desired. Our algorithm trades off between added contrast and “halo” artifacts, resulting in a good balance between visibility of details and an acceptable level of artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their visual quality.