The paper initially delineates the problem of aging in composite polymer insulators utilized in overhead transmission lines, followed by a comprehensive examination of the underlying basic mechanisms contributing to this phenomenon. Subsequently, the paper addresses aspects of accelerated artificial aging specific to this class of high-voltage insulation systems. A critical analysis of both standardized and selected non-standardized testing methodologies employed to simulate aging processes is presented. Furthermore, the study incorporates simulation modeling via COMSOL Multiphysics to identify and emphasize critical stress regions within the insulators, which are posited as key contributors to the overall aging behavior. Based on the findings, important conclusions relevant to practical application are drawn, offering valuable insights that can inform future strategies and decision-making processes.

This paper presents the design and evaluation of a Virtual Reality (VR) application developed to educate young adults on flood safety. The simulation, playable on the Oculus Quest 2, was created using Unity and features assets modeled in Blender. It adopts a scenario-based learning approach, set within a school setting, where users navigate a seven-stage flood emergency by locating survival equipment and making contextually relevant decisions. The effectiveness of the application was assessed using pre- and post-intervention Likert scale questionnaires. The results indicate improved knowledge retention, enhanced decision-making skills, and increased user engagement. Qualitative feedback highlighted the simulation’s realism and emotional resonance. This preliminary study highlights the potential of VR-enabled experiential learning in disaster preparedness, providing ethical considerations and recommendations for broader implementation.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Early and accurate forecasting of cognitive decline in AD patients is essential for personalized treatment planning and effective clinical trial design. However, modeling disease progression is complicated by the irregular timing of clinical visits and heterogeneous data sources. This study presents a Time-aware Long Short-Term Memory (T-LSTM) model that captures temporal dependencies in patient data by integrating time gaps between visits directly into the learning process. Data from multiple large-scale cohorts—including ADNI, NACC, and CPAD—are harmonized and preprocessed to construct a unified longitudinal dataset for training and evaluation. Our approach forecasts Mini-Mental State Examination (MMSE) scores for an unlimited time horizon, demonstrating strong predictive performance and highlighting the effectiveness of temporally sensitive neural network architectures for long-term cognitive trajectory modeling in AD.

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.

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide vari- ants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alter- ations due to inherent technical limitations. To overcome that, here we introduce an approach for long-read sequencing of single-cell derived subclones, and use it to profile 23 subclones of a mouse melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational frame- work for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by indepen- dent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development of new computational methods.

In paediatric oncology, imaging biomarkers play an increasing role in diagnostic imaging and research. They can be used for prediction, detection, staging, and grading of diseases, as well as for assessment of response to treatment. Imaging biomarkers are complementary to tissue-based biomarkers, enabling a more personalised approach in oncology care. In this white paper by the European Society of Paediatric Radiology (ESPR) Oncology Taskforce and European Association of Nuclear Medicine (EANM) Paediatrics Committee, an overview is given of the current knowledge on the use of imaging biomarkers in general and per tumour group.

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.

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.

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.

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 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.

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.