Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms.

Background/Objectives: Accurate post-mortem interval (PMI) estimation becomes increasingly difficult when bodies decompose under extreme heat. Hyperthermal Mediterranean environments accelerate soft-tissue degradation, induce early mummification, and distort classical thanatological indicators, often resulting in substantial PMI overestimation. This study analyzes three forensic cases affected by climate-driven decomposition anomalies and presents a climate-adaptive, AI-assisted diagnostic framework applied uniformly across all cases to improve PMI interpretation. Methods: A retrospective case series analysis was conducted on three individuals recovered during summer heatwaves. Crime scene investigation, post-mortem computed tomography (PMCT), autopsy, and genetic identification were integrated with 5–15-year meteorological datasets. Classical PMI estimations were compared with circumstantial data. A multimodal AI model, incorporating environmental features, decomposition morphology, and microenvironmental modifiers, was operationalized for each case using a hybrid Random Forest–LSTM architecture. Engineered indices included Accumulated Degree Days (ADD), a Decomposition Index, and climate-stress metrics (Thermal Load Index, Desiccation Pressure Factor, Microenvironmental Distortion Coefficient). Quantile regression provided calibrated prediction intervals. Results: Morphological assessments overestimated PMI in every case, suggesting intervals of 1–6 months despite true PMIs of approximately 20 days (Cases 1–2) or 36–48 h (Case 3). The AI model yielded conceptual outputs more consistent with verified PMIs, ~21 days (Case 1), ~23 days (Case 2), and ~42 h (Case 3), each accompanied by 50% and 90% prediction intervals. Explainability analyses identified thermal load, desiccation pressure, and microenvironmental distortion, particularly insulation in Case 3, as dominant drivers. Conclusions: Extreme heat fundamentally alters decomposition trajectories, rendering classical PMI methods unreliable. Applying a climate-aware, AI-assisted diagnostic framework across all three cases improved interpretability, providing uncertainty-aware estimates aligned with true PMIs. The AI framework is presented as a conceptual, non-trained, proof-of-concept system, and reported outputs represent operational demonstrations rather than validated predictions, offering a promising foundation for next-generation PMI diagnostics in hyperthermal forensic settings.

ABSTRACT Everyday communication is dynamic and multisensory, often involving shifting attention, overlapping speech, and visual cues. Yet, most neural attention tracking studies are still limited to highly controlled lab settings, using clean, often audio‐only stimuli and requiring sustained attention to a single talker. This work addresses that gap by introducing a novel dataset from 24 normal‐hearing participants. We used a wearable electroencephalography (EEG) system (44 scalp electrodes and 20 cEEGrid electrodes) in an audiovisual (AV) paradigm with three conditions: sustained attention to a single talker in a two‐talker environment, attention switching between two talkers, and unscripted two‐talker conversations with a competing single talker. Analysis included temporal response functions (TRFs) modeling, optimal lag analysis, selective attention classification with decision windows ranging from 1.1 to 35 s, and comparisons of TRFs for attention to AV conversations versus side audio‐only talkers. Key findings show significant differences in the attention‐related P2 peak between attended and ignored speech across conditions for scalp EEG. Interestingly, our results revealed strong cross‐condition generalization, with models trained in one condition maintaining good performance when evaluated on the other two. No significant change in performance between switching and sustained attention suggests robustness for attention switches. Optimal lag analysis revealed a narrower peak for conversation compared to single‐talker AV stimuli, reflecting the additional complexity of multi‐talker processing. Classification of selective attention was consistently above chance (55%–70% accuracy) for scalp EEG, whereas cEEGrid data yielded lower correlations, highlighting the need for further methodological improvements. These results demonstrate that wearable EEG can reliably track selective attention in dynamic, multisensory listening scenarios and provide guidance for designing future AV paradigms and real‐world attention tracking applications.

This study examines the key competencies required of teachers in contemporary education and their role in preparing future generations. These competencies are widely recognized as foundational to societal development across multiple domains. In this context, the 4Cs model in initial teacher education was developed to establish conceptual and pedagogical preconditions for integrating critical thinking, creativity, collaboration, and communication competencies into teacher education programs. The study investigated the perceptions of students enrolled in teacher education programs at the University of Sarajevo regarding instructional activities through which university teachers foster and develop 4Cs competencies in higher education teaching. The findings indicate that, from the students’ perspective, instructional activities most strongly support the development of communication competency, followed by critical thinking and collaboration, whereas creativity is the least consistently fostered. This finding suggests that the integration of 4Cs competencies in higher education remains predominantly declarative rather than transformative, revealing a gap between curricular expectations and pedagogical practice. Furthermore, the results indicate a highly significant correlation between instructional activities fostering critical thinking and creativity. Significant correlations were also observed among the other competency scales, indicating that these competencies are interrelated and mutually reinforcing rather than developed in isolation. These findings highlight practical implications for teacher education programs.

This paper presents two offline, on-premise NLP proof-of-concept assistants built on a shared architecture for internal knowledge access in the Central Bank of Bosnia and Herzegovina: (i) a semantic document search tool for internal Word/PDF repositories and (ii) an HR chatbot that applies retrieval-augmented generation (RAG) over indexed HR policies and procedures. Rather than proposing a novel NLP method, the paper contributes by documenting a reusable offline architecture for institutional AI assistants in a security-constrained central banking environment and by providing pilot evidence on how established semantic retrieval and RAG techniques can be adapted to strict requirements of confidentiality, data sovereignty, and governance. The semantic search assistant combines exact phrase matching with embedding-based retrieval and hybrid re-ranking, while the HR chatbot generates source-grounded answers using locally hosted language models under explicit governance constraints, including B/H/S-only output, strict fallback behaviour, and transparent display of retrieved passages. Pilot results indicate that hybrid retrieval offers the most reliable performance across representative internal queries, while the HR chatbot demonstrates the feasibility of document-grounded employee support under offline institutional constraints. The findings provide preliminary evidence that offline NLP assistants can improve access to internal institutional knowledge while remaining compatible with the security and operational risk requirements typical of central banking environments.

Background: Osteoarthritis (OA) is a heterogeneous joint disorder traditionally considered mechanically driven; however, evidence indicates that inflammatory mechanisms contribute to symptom expression. Exploratory analyses of peripheral biomarkers may provide insights into systemic inflammation in symptomatic knee OA, but formal phenotypic validation requires dedicated clustering or longitudinal studies. Objective: To examine associations between clinical pain, functional impairment, and circulating inflammatory biomarkers in patients with knee OA compared with healthy controls. Methods: In this prospective, single-center study, patients aged 40–80 years with radiographically confirmed knee OA and chronic knee pain were compared with age- and sex-matched healthy controls. Pain intensity and functional status were assessed using the Visual Analogue Scale (VAS) and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Circulating inflammatory biomarkers, including cytokines and matrix metalloproteinases, were quantified using multiplex immunoassays. Statistical analyses included adjusted linear regression models, with age and BMI as covariates, and multiple testing correction using the Benjamini–Hochberg procedure (FDR alpha error 5%). Results: OA patients exhibited higher circulating levels of TNF-α, IL-6, IL-8, MMP-1, MMP-3, TNFSF13, TNFSF13B, and pentraxin-3 compared with controls (p < 0.01). No significant sex differences were observed. KOOSs correlated with IL-6 and IL-10 levels, suggesting an association between systemic inflammatory activity and functional limitation. All findings are presented as exploratory and associative. Conclusions: Patients with knee OA display systemic inflammatory biomarker differences associated with pain and functional impairment. These results support the role of inflammation in OA symptoms within an exploratory framework. Larger, longitudinal studies are warranted to validate these observations.

Hydatidiform mole, the most common form of gestational trophoblastic disease, presents as complete or partial form. Complete hydatidiform mole (CHM) usually presents with prominent histological criteria, whereas partial hydatidiform mole (PHM) shows a wide spectrum of presentations depending on gestational age. Molecular markers enhance the understanding of the variation and heterogeneous presentation of molar pregnancies, as well as their biological potential and behavior. This retrospective study included 50 CHM and 50 PHM specimens, terminated in first trimester via suction curettage. A second histopathological review of slides stained with hematoxylin and eosin was conducted, as well as a selection of representative tissue slides for p53 immunostaining. This study aimed to determine the precise correlation between specific morphological criteria and the patterns of p53 immunoexpression. Semi-quantitative analysis of samples for both pathological criteria and p53 immunostaining was performed. p53 positivity was defined as follow: the percentage of positive cells/nuclei: + (10-40%); ++ (40-70%); +++ (>70%); and staining intensity was scored as: 1 – weak, 2 – moderate, and 3 – strong intensity. P53 expression was estimated on at least 200 nuclei of cytotrophoblasts per slide. Significant difference in p53 expression exist between CHM and PHM in staining intensity. CHM shows significant correlation of p53 positivity with hydrops, central cisterns and atypia. In PHM trophoblast pseudoinclusions demonstrate strong significant correlation with p53 positivity. The irregular pseudoinclusions demonstrate lower expression compared with round or oval, being consistent with benign behavior of PHM. Prominent morphological criteria strongly correlate with p53 immunoexpression for both CHM and PHM.

Objective: To evaluate the level of insight into illness in patients with schizophrenia and its associations with demographic factors, clinical symptoms, executive functions, and selected metabolic parameters. Subjects and Methods: This cross-sectional study included 60 outpatients diagnosed with schizophrenia according to DSM-IV criteria. Participants were divided into two groups based on the median score of the Self-Appraisal of Illness Questionnaire (SAIQ): preserved insight (n=30) and impaired insight (n=30). Positive symptoms were assessed with the Positive Symptoms Rating Scale (PSRS), negative symptoms with the Brief Negative Symptom Assessment (BNSA), executive functions with the Wisconsin Card Sorting Test (WCST) and Wechsler-Bellevue Intelligence Scale-II (WB-II) subscales. Metabolic parameters included body mass index (BMI), systolic and diastolic blood pressure, and waist circumference. Statistical analysis was performed using t-tests, ANOVA, Pearson correlation, and multiple linear regression (p<0.05). Results: Patients with impaired insight exhibited significantly higher positive (PSRS: 28.5±4.2 vs 18.3±3.1; p<0.001) and negative symptoms (BNSA: 35.2±5.6 vs 22.1±4.0; p<0.001), poorer executive performance (WCST total score: 45.6±8.9 vs 68.4±7.2; p<0.001), higher BMI (28.7±3.4 vs 24.5±2.8; p<0.01), and elevated blood pressure values. SAIQ total score negatively correlated with positive (r=–0.62; p<0.001) and negative symptoms (r=–0.58; p<0.001), illness duration (r=–0.45; p<0.01), and positively with years of education (r=0.48; p<0.01) and WCST score (r=0.52; p<0.001). Regression analysis showed that negative symptoms (β=–0.41; p<0.001) and executive dysfunction (β=–0.35; p<0.01) were the strongest independent predictors of poor insight (R²=0.62). Conclusion: Impaired insight in schizophrenia is strongly associated with greater psychopathological burden, neurocognitive deficits (especially executive dysfunction), and metabolic disturbances. These findings support the implementation of integrated therapeutic strategies targeting insight, cognition, and cardiometabolic health to improve long-term outcomes.

Lavandula angustifolia essential oil (LEO) was obtained by hydrodistillation of air-dried flowers collected in the Mostar region (Bosnia and Herzegovina). Its chemical composition was analyzed by gas chromatography-mass spectrometry, revealing a camphor content of 16.96%, substantially higher than the maximum value specified in the European Pharmacopoeia. Antimicrobial activity was evaluated using quantitative suspension tests according to EN 1276 and EN 1650 under simulated “dirty” conditions with organic load (bovine albumin, 3 g/L) and a 5 min contact time. High-concentration LEO (80% w/v) exhibited strong bactericidal activity against Escherichia coli ATCC 10536 and Staphylococcus aureus ATCC 6538, and yeasticidal activity against Candida albicans ATCC 10231 (>5 log10 CFU/mL reduction for bacteria, >4 log10 CFU/mL reduction for yeast), but was ineffective against Pseudomonas aeruginosa ATCC 15442 and Enterococcus hirae ATCC 10541. Lower concentrations (1.0% and 0.1% w/v) showed no bactericidal and yeasticidal activity, highlighting LEO’s efficacy limits. Antioxidant activity, assessed by DPPH radical scavenging, was dose- and time-dependent. Molecular docking provided insight into the interaction of major constituents with selected microbial and antioxidant-related targets. These findings highlight both the potential and limitations of LEO as a renewable bio-based resource for sustainable disinfectant formulations while emphasizing the importance of chemical composition and regulatory compliance.

We identify a novel next-to-leading order renormalization effect in the dimension-six SMEFT with direct phenomenological impact. The Higgs-Yukawa operator that modifies the top-Higgs coupling $\kappa_t$ induces a shift in the $ W $ mass at two-loop order through a large anomalous dimension, rendering electroweak precision observables a powerful indirect probe of $\kappa_t$. We show that this effect is essential for the consistent interpretation of data from future Tera-$Z$ and Giga-$W$ factories such as FCC-ee. The effect is realized in a simple renormalizable two-Higgs doublet model.