This paper presents a PMU-data-based methodology for estimating regional inertia constants in power systems during the initial transient period following a disturbance. The power system is partitioned into dynamically coherent regions based on frequency signals from all monitored buses. Empirical Mode Decomposition (EMD) is applied to each nodal frequency signal to extract Intrinsic Mode Functions (IMFs), and the dominant IMF is identified through an energy ratio criterion. Pairwise correlation analysis of these dominant IMFs is then used to group buses with similar dynamic behavior, forming coherent regions. Within each region, the active power imbalance is computed from Phasor Measurement Units (PMU)-measured tie-line power deviations, while the rate of change of frequency (RoCoF) is estimated from residual trends of EMD-processed frequency signals. These residuals are shown to accurately follow the center of inertia (CoI) frequency trajectory, allowing precise CoI RoCoF estimation. To improve robustness against noise and oscillatory distortions, an adaptive Least Mean Squares (LMS) filter is applied. The regional inertia constants are subsequently estimated using an adapted swing equation during the initial transient period. The method is validated on the IEEE 39-bus test system, yielding estimation errors below 3% relative to reference values, demonstrating its effectiveness for inertia monitoring in low-inertia systems.

This paper presents a discrete-time sliding mode voltage controller for a DC-DC boost converter. A disturbance estimator is integrated into the controller, ensuring a fast dynamic response and robustness against matched disturbances and parametric uncertainties, such as input voltage fluctuations, load variations and internal nonlinear dynamics that affect system performance. The disturbance estimator improves the rejection of unmeasured perturbations using only local measurements, without requiring additional sensors or complex adaptive structures. A cascade control scheme is adopted, with an inner inductor current loop and an outer voltage regulation loop. Both controllers utilize disturbance-compensated discrete-time sliding mode control strategies. The simulation results validate the effectiveness of the method, comparing it to the optimized discrete-time proportional integral (DT PI) cascade controller, demonstrating precise voltage regulation and tight performance under disturbances.

Tracking dynamic changes in plant leaves using deep learning models represents a new approach to plant trait analysis. Combining deep learning techniques with botany and agronomy can be of great significance in the future. This indeed marks a crucial step towards addressing the increasingly prevalent problems in agriculture, especially considering that the issue of food scarcity represents a real problem we might face in the coming period. In this paper, we present an adaptation of the Speedy Measurement of Arabidopsis Rosette Traits (SMART) program - a robust, parameter-free system for plant image segmentation and trait extraction. Our goal was to optimize performance on a diverse dataset that differs significantly in characteristics from the one originally used to evaluate SMART. To this end, we replaced the traditional feature extraction methods with a custom-designed semantic segmentation approach. This modification enabled significantly improved results on our target dataset. Furthermore, the enhanced model offers promising potential for future applications, particularly in estimating plant developmental stages.

While traditional sampling-based path planning approaches for robotic manipulators, such as RRT (Rapidly-Exploring Random Trees) and PRM (Probabilistic Roadmaps), provide feasible solution paths, convex optimization-based techniques offer some additional features. Some of these methods unfortunately require a representation of the manipulator’s configuration space as a set of convex volumes, which can be challenging to obtain due to the high dimensionality and complexity of the configuration space. This work presents an algorithm for computing convex volumes in the manipulator’s configuration space, called GBur-IRIS. The algorithm combines the structure known as the generalized bur of free C-space with the convex volume-inflating algorithm IRIS (Iterative Regional Inflation by Semidefinite Programming). It follows a simple iterative procedure. First, it computes a generalized bur. Then, it encloses the bur in an ellipsoid. Finally, it uses this ellipsoid to initialize the IRIS algorithm. The paper provides a detailed description of the algorithm and shows an extensive simulation study. This study is conducted on several robotic manipulators and environments, and the results are discussed and compared with existing approaches from the literature.

High-throughput plant phenotyping using RGB imaging offers a scalable and non-invasive solution for monitoring plant growth and extracting various traits. However, achieving accurate segmentation across experiments remains a challenging task due to image variability usually caused by shifts in pot positions. This study introduces a customized image stabilization method to align pots consistently across time-series images of Arabidopsis thaliana, enhancing spatial consistency. A large-scale RGB dataset was collected and prepared, with 4,000 manually annotated images used to train multiple encoder–decoder deep learning models. Various CNN-based encoders were paired with well-known decoders, including U-Net, $\mathbf{U}^{2}$-Net, PANet, and DeepLabv3. Stabilization significantly improved performance of models, with the $EffNetB1 +\mathbf{U}^{2}$-Net encoder-decoder combination achieving the highest precision score of 0.95 and Intersection over Union of 0.96. These results demonstrate the value of spatial consistency and offer a robust, scalable pipeline for automated plant segmentation in indoor phenotyping systems.

The study aimed to evaluate the audiologic profile of preschool children with hearing loss, i.e., to determine the type, degree, and configuration of hearing loss, amplitude of otoacoustic emissions, and word recognition performance. This retrospective study included 260 children examined in a secondary healthcare setting. For statistical data analysis, we used the Chi-square test with a level of significance p < 0.05. Conductive, sensorineural, and mixed hearing loss was present in 93.1%, 4.6%, and 2.3%, respectively. Mild hearing loss was present in 96.1%, moderate in 2.3%, and severe hearing loss in 1.6%. Type B tympanogram was the most common (p = 0.00001). The mean amplitude of otoacoustic emissions was -7.6 dB in sensorineural hearing loss and 12.3 dB in normal hearing. The maximum word recognition score was frequently obtained at presentation levels of 25-40 dB SL (p = 0.009). The majority of children had mild conductive hearing loss with normal word recognition ability.

Objectives The primary objective of this study was to examine the potential association between glutathione S-transferases (GSTT1/GSTM1) deletion polymorphisms and the development of apical periodontitis (AP) in a population of patients at two university centers: the Faculty of Medicine at the University of Banja Luka in Bosnia and Herzegovina and the School of Dental Medicine at the University of Belgrade in Serbia. Materials and Methods The study involved 200 patients with AP in the experimental and 250 healthy individuals without AP in the control group. As a source of genomic DNA, sterile buccal swabs were taken from each patient. Genotyping of GSTM1 and GSTT1 deletion polymorphisms was conducted using multiplex Polymerase Chain Reaction (PCR). The risk of AP development with regard to the genotypes was evaluated based on odds ratios (ORs) and 95% confidence intervals (CIs) that were calculated via unconditional logistic regression. Results There were significant differences in demographic characteristics between the investigated groups (p = 0.446, p = 0.154, respectively). GSTM1 and GSTT1 deletions were associated with a 3.05-fold and 5.69-fold risk (OR = 3.05, 95% CI = 2.07–4.49, OR = 5.69, 95% CI = 3.66–8.86, p < 0.001, p < 0.001, respectively) for the AP development. The co-occurrence of both deletions posed a significantly higher risk for AP development (OR = 52.76. 95% CI = 18.20–152.94, P < 0.001). Conclusions The carriers of null GSTT, null GSTM, and double null GSTT/GSTM genotypes are more susceptible to AP development in the populations examined at the two centers.

OBJECTIVE Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and schizophrenia (SCZ) are highly heritable and linked to disruptions in fetal neurodevelopment. Epigenetic processes, such as DNA methylation (DNAm), are considered a key pathway of interest. Yet, it is unclear whether: (i) genetic susceptibility to neurodevelopmental conditions associates with DNAm patterns already at birth; (ii) DNAm patterns are unique or shared across conditions, and (iii) neonatal DNAm patterns can be leveraged to enhance genetic prediction of neurodevelopmental outcomes. METHODS We conducted epigenome-wide meta-analyses of genetic susceptibility to ASD, ADHD, and schizophrenia (measured with polygenic scores [PGSs]) and cord blood DNAm in four European population-based cohorts (npooled=5,802; 50.2% female). We estimated DNAm pattern overlap between PGSs using heterogeneity statistics. Further, we built methylation profile scores for each PGS to test incremental variance explained over genetic data alone in 130 developmental outcomes from birth to 14 years. RESULTS In probe-level analyses, SCZ-PGS associated with neonatal DNAm at 246 loci (p<9x10-8), predominantly in the major histocompatibility complex, supporting an early-origins perspective on schizophrenia. Functional characterization confirmed strong genetic effects, blood-brain concordance and enrichment for immune-related pathways. 8 loci were identified for ASD-PGS (mapping to FDFT1 and MFHAS1), and none for ADHD-PGS. Differentially methylated regions were detected across PGSs (130-166 regions). Overall, DNAm signals were largely distinct between conditions. Incorporating neonatal DNAm data in genetic prediction models nominally increased explained variance for several cognitive and motor outcomes. CONCLUSIONS Genetic susceptibility for neurodevelopmental conditions, particularly schizophrenia, is detectable in cord blood DNAm in the general population.

OBJECTIVES The presence of altered sagittal cervical balance and faulty posture has been observed in individuals with neck pain. However, there is a lack of literature investigating the relationship between radiological thoracic inlet alignment and clinical sagittal cervico-thoracic posture. This cross-sectional study aims to investigate these clinico-radiological associations, analyze their correlation with pain variables, and explore the diagnostic significance of thoracic inlet parameters for chronic neck pain (CNP). METHODS 88 subjects (N = 44 each in CNP and Control group) were recruited. T1 Slope (T1S), Thoracic inlet angle (TIA), Neck tilt (NT) were assessed on lateral cervical radiograph, and Craniovertebral angle (CVA), High thoracic angle (HTA), Sagittal head angle (SHA) were photographically analysed. Pain was quantified in terms of intensity and functional disability. Craniocervical flexion test (CCFT) assessed the deep neck flexors (DNF) performance. RESULTS After normality check, between-group comparisons utilized Unpaired t-test or Mann-Whitney U test. CNP group had lower T1S, and higher TIA, NT. The TIA had significant correlation with CVA and HTA (rs = -0.32, -0.30 respectively) in asymptomatic group, but not in CNP subjects. Patients with severe pain/disability had weaker DNF. Symptomatic group showed age-related declines in SHA, CVA and CCFT. Logistic regression revealed T1S (<26.36°) and NT (>47.41°) had diagnostic significance for CNP. CONCLUSION Neck pain corresponded with distinct postural, radiological, and clinical alterations compared to controls. Thoracic inlet parameters (primarily TIA) influenced cervicothoracic posture in asymptomatic individuals, but pain disrupted these associations, highlighting the complex interplay between alignment, posture, and symptomatology.

Lung development is governed by tightly regulated signaling mechanisms, including endocytosis-mediated pathways critical for epithelial–mesenchymal communication and tissue remodeling. This study investigated the effects of Dab1 deficiency on the expression of endocytic and signaling-related proteins, Megalin, Cubilin, Caveolin-1, GIPC1, and Dab2IP, during embryonic lung development in yotari mice. Using immunofluorescence and quantitative image analysis, protein expressions were compared between yotari and wild-type embryos at gestational days E13.5 and E15.5. Results showed significantly reduced expression of Caveolin-1 in the yotari epithelium across both stages, along with diminished mesenchymal levels of Megalin and GIPC1 at E13.5. Cubilin and Dab2IP expression patterns showed no statistically significant differences, although developmental and compartmental shifts were observed. These findings suggest that Dab1 deficiency selectively disrupts endocytic and signaling scaffolds crucial for branching morphogenesis and alveolar maturation. The altered spatiotemporal expression of these proteins underscores the essential role of Dab1 in regulating lung epithelial–mesenchymal dynamics and maintaining developmental homeostasis during critical stages of organogenesis.

Introduction: Stress is an essential element of students' academic lives, especially as exams approach. Due to academic demands, social pressure, and personal expectations, stress can negatively impact students' mental and physical health and lead to an increased risk of chronic illness. This study investigated the effects of oral exams on students' physiological parameters. Material and Methods: The study involved 35 physiotherapy students. The "Smart Bracelet TK 30" monitored heart rate, blood pressure, and blood glucose levels before and during the exams. Results: Significant blood pressure and pulse changes were observed between resting and exam conditions. Blood glucose levels were higher in men than in women during the exams. Conclusion: Exam stress significantly impacts students' physiological responses. Given the potential long-term health risks, preventive health strategies should be developed to help students cope with academic stress.

Background/Objectives: This study investigates the antimicrobial properties of pomegranate peel extract (PoPEx) and its major polyphenolic constituents against Gram-positive and Gram-negative bacteria, employing six clinical isolates of Staphylococcus aureus and five isolates of Escherichia coli. The study further aims to elucidate mechanisms of action through molecular docking and transport studies. Methods: Chemical composition was analyzed using liquid chromatography–mass spectrometry (LC–MS). Antimicrobial activity was determined by the broth microdilution method. Molecular docking was performed with the AutoDock Vina algorithm, and transport studies through porin channels were carried out using Caver software. Results: PoPEx showed stronger activity against Gram-positive (MICs 15.62–500.00 μg/mL) than Gram-negative bacteria (MICs 125.00–500.00 μg/mL). Punicalagin was most active against S. aureus, while gallic acid was most effective against E. coli. Docking revealed high affinities of punicalagin and punicalin, whereas transport studies highlighted the advantage of smaller phenolics like gallic acid in crossing porins. Conclusions: Larger tannins exhibited strong target binding but limited porin permeability, reducing efficacy in Gram-negative bacteria. These findings provide insights into structure–activity relationships of pomegranate polyphenols and support their potential as natural antimicrobial agents.

Abstract This study presents the first comprehensive ecological assessment of the Dabar River, a small karstic watercourse in the Sanski Most area, using biological indicators focusing on the macrozoobenthos community, microbiological parameters, and ichthyofaunal composition. Sampling was conducted at three sites (headwaters, middle course, and downstream section), applying standard methods for the analysis of macrozoobenthos composition, bacterial abundance (including Escherichia coli), and biological indices (SI, BMWP, ASPT, EBI). The results indicate a good ecological status of the river, particularly in the headwater section, with a slight decline in water quality downstream. A rich and stable benthic fauna was identified, dominated by Gammarus fossarum, Sadleriana fluminensis, and representatives of the EPT groups. Microbiological findings revealed low to moderate levels of contamination, with no presence of fecal bacteria. Dabarska Cave, which hosts endemic subterranean crustaceans and olm Proteus anguinus, represents a particular ecological value of the area. Guidelines for conservation and sustainable management are proposed, including legal protection of the watercourse, pollution control, and the development of ecotourism.

Academic writing is a crucial skill for university students, with argumentative essays being a fundamental form of written expression. Despite various instructional approaches, students, particularly those with lower foreign language proficiency, continue to struggle with essay writing. Grounded in the socio-cognitive theoretical framework and metacognitive strategies, this study examines the impact of explicit writing instruction on English major students. It explores the effectiveness of strategies such as modelling, practice with feedback, and independent practice in enhancing students’ essay-writing performance. A total of 110 argumentative essays, produced by experimental and control groups over one semester, were analysed. The findings reveal a statistically significant improvement in overall writing performance across seven assessed criteria, though not all elements improved equally. The results underscore the necessity of teaching essay writing explicitly, demonstrating that explicit instruction is an effective approach to enhancing academic writing skills among university students.

Four new heteroleptic neutral paramagnetic mononuclear oxidovanadium(IV) complexes, designated as [VOL(phen)], where L corresponds to acetophenone isoniazid hydrazone or its 5-halogenated derivatives and phen stands for 1,10-phenanthroline, were synthesized and thoroughly characterized using chemical analysis, various spectroscopic techniques, and diffraction methods. Single-crystal X-ray diffraction revealed the molecular and crystal structures of two complexes, showing an octahedral coordination environment around the vanadium(IV) center. The coordination includes a tridentate ONO donor hydrazone ligand in its deprotonated enol-imine form, 1,10-phenanthroline as a bidentate NN donor ternary ligand, and one terminal oxygen atom. The biochemical and hematological effects of these complexes were evaluated in a streptozotocin-induced diabetic rat model. All synthesized complexes showed cholesterol-lowering effects compared to the diabetic rat group, with the vanadium complex lacking a substituent on the acetophenone ring of hydrazone showing the strongest effect. Complexes exhibited comparable and significant antidiabetic activity in vivo, effectively reducing hyperglycemia within 1 week of treatment. Additionally, the histopathological effects of complex (4) on liver, kidney, and brain tissues were investigated. All four complexes were found to have low bioaccumulation levels, with total absolute bioaccumulation in all tested organs less than 0.35% of the administered dose.