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.

Motion tracking achieved via conventional video processing and machine vision algorithms is often hindered by challenges such as motion blur and the lack of distinctive visual features, particularly when tracking fast-moving objects. To address these limitations, active visual markers are often used. In this paper, we present the design and prototype implementation of an active marker that is compact, detachable, and self-powered, making it well-suited for real-world tracking applications. Furthermore, the marker is fully configurable through an accompanying software solution and an additional wireless communication controller via an infrared protocol. The applicability of the developed markers is demonstrated using both conventional RGB and event-based cameras, highlighting their versatility and robustness across diverse sensing modalities. Their tracking capabilities are validated in both single- and multi-object scenarios. Overall, the developed multi-functional markers provide a flexible and practical foundation for high-speed motion tracking under challenging visual conditions, paving the way for further research and advanced applications in related fields.

Air pollution, particularly the concentration of particulate matter ($\mathbf{P M}_{\mathbf{1 0}}$), poses significant risks to human health and the environment. In this study, we employed the FB Prophet forecasting model to predict PM10 levels in Sarajevo and applied SHAP to enhance model interpretability. Using historical meteorological data and PM10 concentration records, we evaluated the model’s performance across three prediction horizons: 30, 60, and 90 days ahead. SHAP analysis identified the key meteorological drivers influencing $\mathbf{P M}_{10}$ concentrations. Accurate long-horizon predictions can support timely planning and decision-making, while also enhancing our understanding of air pollution dynamics in Sarajevo and providing valuable guidance for environmental management and public health strategies.

Manipulating deformable objects such as textiles remains a significant challenge in robotics due to their complex dynamics, unpredictable configurations, and high-dimensional state space. In this paper, we present an integrated planning and execution framework for robotic cloth manipulation that combines symbolic task planning, physics-based simulation, and vision-guided real-world control. High-level plans are generated using Planning Domain Definition Language (PDDL) and translated into low-level primitive actions executed on a Franka Emika Panda robot. To enable perceptual grounding, we employ a vision-based pipeline using an adapted CeDiRNet model for cloth corner detection and grasp point estimation. The system is first validated in IsaacSim using a particle-based deformable cloth model and then transferred to a real-world setup with consistent performance. Our results demonstrate successful Sim2Real transfer of high-level plans, enabling reliable execution of folding tasks in both simulated and physical environments.

With the emergence of advanced techniques in the field of artificial intelligence, risk management in the financial sector has undergone significant transformation. This paper proposes a deep learning–based approach for risk modeling using Bidirectional Long Short-Term Memory (BiLSTM) networks, adapted for tabular data by excluding explicit temporal dependencies. The model is tailored to support accurate decision-making in financial risk assessment. One notable component of this study is the use of automated hyperparameter optimization (HPO) methods, which further enhance the model’s overall effectiveness. These tuning strategies yield models that are less complex, faster to train, and capable of adapting to dynamic data environments, making them suitable for integration into automated credit approval systems. The model was evaluated against baseline approaches, demonstrating improved predictive performance across key evaluation metrics, with statistical significance confirmed by McNemar’s test.

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.

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.

In this paper, we analyze the secrecy outage performance of the classical Wyner’s model, where both the legitimate receiver and the eavesdropper experience gamma-shadowed two-wave with diffuse power (GS-TWDP) composite fading. We derive expressions for the lower bound of the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) in terms of Meijer’s G-function, which can be efficiently implemented in MATLAB® and MATHEMATICA®. The derived expressions are validated through Monte Carlo simulations and used to perform detailed analysis of the impact of shadowing and multipath severity on secrecy outage performance in channels with composite fading.

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.

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.

Introduction: The risk of cognitive impairment, including dementia and moderate cognitive impairment (MCI), is higher in patients with diabetes and prediabetes. The need for early diagnosis biomarkers has increased due to the rise in the prevalence of type 2 diabetes mellitus (T2DM) and its related cognitive problems worldwide, as well as the lack of clear biochemical indicators and efficient treatments for dementia or cognitive decline. Chronic low-grade inflammation, reflected by elevated complete blood count-derived inflammatory indices (CBCIIs), has been implicated in both metabolic dysregulation and neurodegeneration. However, their relationship with cognitive impairment in T2DM remains insufficiently explored. The objective of this study was to investigate the association between CBCIIs and cognitive function in patients with T2DM. Methods and materials: This cross-sectional observational study included 116 patients with T2DM recruited from diabetes counseling centers in the Public Institution Health Center of Sarajevo Canton, Bosnia and Herzegovina. Based on the assessed cognitive status, patients with T2DM were divided into two groups: with cognitive impairment (n= 76) and without cognitive impairment (n=40). A validated assessment tool, the Montreal Cognitive Assessment (MoCA), a quick test designed to screen for milder forms of cognitive impairment, was used for cognitive screening. Venous blood samples were analyzed for standard complete blood count parameters, from which 11 CBCIIs were calculated: neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), aggregate index of systemic inflammation (AISI), systemic inflammation response index (SIRI), lymphocyte-to-monocyte ratio (LMR), and monocyte-to-neutrophil ratio (MNR).. Results: The results of our study showed that NLR, dNLR, NPR, NLPR, PLR, MLR, SII, AISI, and SIRI were significantly higher in the group of T2DM patients with cognitive impairment compared to the group without cognitive impairment. On the other hand, LMR and MNR were significantly lower in the group of T2DM patients with cognitive impairment compared to the group without cognitive impairment (p<0.05). The MoCA score was significantly negatively correlated with NLR, dNLR, NPR, NLPR, and SII, and positively with MNR (p<0.05) Conclusion: Elevated CBCIIs are significantly associated with cognitive impairment in patients with T2DM. These inexpensive and widely available indices may serve as adjunctive markers for early cognitive screening in this population.

Parabens, often used as preservatives in consumer products, have raised concerns due to their endocrine-disrupting properties. The aim of this study was to quantify the levels of methyl and propyl paraben in adult urine samples and to assess potential health risks. Using high-performance liquid chromatography (HPLC), methyl and propyl parabens were detected in 20 participants at different concentrations. Methylparaben was more prevalent than propylparaben. Risk assessment was performed by calculating the estimated daily intake (EDI) and the hazard quotient (HQ), with HQ values indicating no significant health risk for the participants. Although current exposure levels appear to be safe, the long-term effects of chronic exposure remain uncertain, highlighting the need for further research. This preliminary study provides insight into paraben exposure in adults and contributes to the growing literature on the safety and prevalence of parabens.

Occlusive cervical artery dissection (CeAD) is associated with worse patient outcome. The net clinical benefit of acute revascularization measures has to be weighed against the likelihood of spontaneous recanalization. Our aim was to assess the hitherto un-addressed impact of spontaneous recanalization on stroke risk in patients with occlusive CeAD. MRI verified CeAD patients with initially occlusive CeAD within cohort study that did not undergo acute revascularization measures were assessed. Follow-up data derived from clinical routine and study specific assessments. Outcomes of interest were occurrence of (i) recanalization and (ii) ischemic stroke upstream of CeAD-related occlusion. Adjusted logistic regression analysis addressed the impact of recanalization on said outcomes. 97/328 (29.6%) patients had occlusive CeAD and did not undergo acute revascularization treatment. Upon follow-up, 56/97 (57.7%) showed spontaneous recanalization of initially occlusive CeAD. Female sex (OR 0.41[0.18, 0.97]; P = 0.043) and internal carotid artery dissection (OR 0.33[0.14, 0.78]; P = 0.012) were the only factors independently associated with recanalization. Within a median follow-up of 8.2 (1.58, 12.8) years, a total of 18/97 (18.6%) patients suffered ischemic stroke upstream of the initially CeAD-affected vessel. After adjusting for confounders, spontaneous recanalization was independently associated with lower rates of cerebral ischemia upon follow-up (OR 0.28[0.09, 0.90]; P = 0.032), most notably also independent of type of antithrombotic treatment. Spontaneous recanalization in occlusive CeAD is associated with lower rates of stroke upon follow-up. These results indicate that persistent CeAD-related occlusion remains a risk-factor for recurrent ischemic events, thus calling for future trials addressing optimal medical treatment. N/A. Lukas Mayer-Suess.

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.