Background: Negative public attitudes promote undesirable stereotypes and stigma in stutterers. Method: To mitigate negative attitudes, 403 respondents combined from 16 international samples filled out the Public Opinion Survey of Human Attributes–Stuttering (POSHA–S) before and after interventions to improve attitudes and were compared to 249 respondents from seven control groups. Investigators aimed (a) to replicate an extreme case of regression to the mean (i.e., “crossover” effect) reported earlier in larger combined samples in which respondents with high pre-scores ended with low post-scores, respondents with low pre-scores finished with high post-scores, and intermediate scorers were unchanged; and (b) to identify individual POSHA–S items related to overall attitude change and among the high and low scorers. Results: As in previous studies, stuttering attitudes improved in the intervention group but not in the control group. Intervention and control respondents demonstrated “crossover” but less than the earlier samples due to lower pre–post correlations. Item contributions to pre–post change and differences among the three change groups were inconsistent; however, high agreement items by respondents were less likely to vary than low agreement items. Conclusion: The “crossover” effect was replicated, and future research should explore its presence in other measures or conditions.

Researchers in Bosnia and Herzegovina face systemic barriers to publishing, including scarce funding and weak institutional support. Limited mentorship and low research literacy increase vulnerability to predatory journals and conferences. Fragmented academic and healthcare systems hinder collaboration and international visibility. Financial constraints and APC costs divert high‐quality local research away from mainstream journals. Practical reforms should focus on research support offices, regional collaborations, and improved promotion criteria.

The rapid integration of artificial intelligence into private and public-sector decision-making has outpaced the development of standards governing the interaction between human judgment and machine intelligence. Existing frameworks—the EU AI Act Regulation, the NIST AI Risk Management Framework, and ISO/IEC 42001—regulate AI systems as discrete technical artifacts but do not standardize the hybrid intelligence configurations in which human cognition and algorithmic outputs jointly produce governance decisions. This paper proposes a three-layer standards framework comprising technical interoperability standards governing how AI outputs are communicated to human decision-makers, procedural standards governing human-AI task allocation and escalation protocols, and accountability standards governing responsibility attribution in distributed decision configurations. The framework is grounded in the Quadruple Bottom Line (QBL), which adds governance as a fourth sustainability dimension. To move beyond a purely conceptual contribution, the paper provides operationalization tools—including a role allocation matrix, confidence calibration thresholds, an accountability mapping template, and a domain classification schema—and proposes a three-tier conformity assessment methodology for evaluating framework implementation. By establishing the hybrid human–AI decision configuration as the unit of standardization, the paper introduces a governance architecture that enables operational, auditable, and comparable hybrid intelligence systems.

The open science paradigm, digitalisation, interdisciplinarity and internationalisation have significantly changed the research process, collaboration, dissemination and impact of scholarly work in the 21 st century. Research impact assessment should include new metrics based on Web 2.0 channels suffering from the following issues: data quality (i.e. accuracy, coverage, comprehensiveness), heterogeneity of data sources and APIs and potential manipulation (i.e. metrics gaming). Although the Findable, Accessible, Interoperable and Reusable (FAIR) principles were designed for research data, they can also be applied to research impact metrics to increase their discoverability and reusability. The main aim of this European Cooperation in Science and Technology (COST) Action is to remove barriers for the wider adoption and reusability of metrics, based on Web 2.0 technologies, which are a significant and vital part of research ecosystems. These metrics can serve as the basis for enhanced research impact assessment and, thus, improve recognition of excellence and foster the further development of science and society. Although Scientometrics, based on Web 2.0, is a paradigm that is over 10 years old, it has not yet been widely adopted. Therefore, a plan or roadmap for transition to Scientometrics 2.0 is needed. This should include recommendations for overcoming the challenges associated with new research impact metrics, as well as frameworks for the evaluation of new metrics and data sources. These challenges include the heterogeneity and comprehensiveness of metrics data sources, the varying quality of metrics data, metrics data gaming etc. Due to the multifaceted nature of these challenges, the Action proposes to create synergies between all interested actors: researchers, research software engineers, librarians, representatives of metrics data providers and policy-makers. This article presents an edited version of the original funding proposal submitted to the COST Open Call 2021.

Climate change significantly affects human physiology and contributes to increased morbidity and mortality, with heat stress representing one of the most severe consequences of thermal imbalance. The aim of this study was to analyze morphological changes to leukocytes on the peripheral blood smears of Wistar rats exposed to hyperthermia using the geometric morphometrics method. A total of forty Wistar albino rats were divided into three experimental groups according to water temperature exposure (37 °C, 41 °C, and 44 °C). Peripheral blood smears were prepared, stained, and digitally recorded using Motic Images Plus 2.0 software, after which selected images were analyzed using geometric morphometric programs (tpsDig, tpsUtil, and MorphoJ) to evaluate leukocyte shape variations. Comparative analysis demonstrated statistically significant morphological changes in polymorphonuclear cell shapes between the control group (37 °C) and rats exposed to 41 °C (p = 0.009). Significant differences were also identified in mononuclear cell morphology between the antemortem and postmortem groups (p = 0.00307). The findings indicate that exposure to elevated temperatures induces measurable alterations in white blood cell morphology, confirming that hyperthermia produces significant structural changes in polymorphonuclear cells and mononuclear cells detectable through geometric morphometric analysis.

The present study investigates who was considered authoritative in matters of language in the 16th and 17th centuries as well as how grammar authors position themselves with respect to these authorities. It evaluates whether a shift in referencing norms may already be observed from the 16th to the 17th century in these regards. As it is claimed that in England the 16th century marks the beginning of English grammar writing ( McCarthy 2020 : 19–20) and the 17th century saw a shift in favour of English being recognized as a separate academic discipline ( Beal 2004 : 102), one can ask if and how onomastic — that is, name-based — references, appear in those grammars and how they can be categorized. One of the findings of this study is that the onomastic references found in the 16th- and 17th-century grammars can be categorized along the six semantic categories suggested in previous work for 19th-century grammars ( Busse et al. 2020 : 11–12). These include, for example, quotations, opinions, or mere mentions. To account for a possible shift in reference strategies over time, these semantic categories were reevaluated by means of inter-rater reliability (IRR) in the 16th- and 17th-century context. Our main findings show that while some 16th-century authors put emphasis on Latinate authors, others embrace moving away from the Latinate approach by not referring to the established Latinate authorities at all or only rarely. A significant shift in onomastic referencing can be observed from the 16th to the 17th century, however the Latinate authorities still held significant ground in the 18th century grammar texts.

Biological invasions are a major driver of biodiversity loss, yet inconspicuous or “cryptic” species often escape detection and public awareness, limiting management responses. We investigated the freshwater jellyfish Craspedacusta sowerbii , likely native to China and now present on six continents, through a 22‐month multilingual online survey across 17 European countries ( n  = 1388), to assess public knowledge, perceptions and views on its management. Results revealed low public awareness: over 80% of respondents did not know the scientific name, 49% misidentified its freshwater habitat as marine and only 33% recognized it as non‐native. Aesthetic perceptions were largely positive, but mass occurrences triggered caution and direct contact was rare and usually harmless. Bayesian Network analysis showed that direct observation—rather than demographic factors or scientific knowledge—was the strongest predictor of concern and support for public discussion. While C. sowerbii was not widely perceived as a local threat, many respondents acknowledged its potential ecological implications and supported greater public awareness and management actions. Our findings highlight how visual subtlety and limited media coverage reduce detection and prioritization of cryptic invasive alien species (IAS), hindering early warning systems and policy development. Leveraging citizen science and experiential engagement can improve recognition and reporting, complement formal monitoring and support more inclusive management strategies. We recommend integrating cryptic invasions like that of C. sowerbii into IAS frameworks and policies, emphasizing public participation and awareness to address overlooked, but potentially impactful species. Read the free Plain Language Summary for this article on the Journal blog.

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.

Spinal muscular atrophy (SMA) therapies that restore SMN expression improve survival and motor function but often fail to fully stabilize distal motor units or sustain endurance. We propose a hypothesis-driven adjunctive approach, intended to complement SMN-restoring therapies, in which localized nanotube-enabled interfaces acting at or near the distal motor unit and neuromuscular junction enhance neuromuscular transmission reliability in surviving, remodeled motor units. The model predicts a temporal cascade: improved junctional reliability and reduced activity-dependent failure, followed by consistent motor unit output across repeated activation, and ultimately, enhanced endurance and functional reserve. Phenotype-specific responsiveness identifies patients most likely to benefit, specifically those with preserved-but-limited residual motor unit substrate accompanied by measurable neuromuscular junction instability. Drawing on shared mechanisms from ALS, spinal cord injury, and other neuromuscular disorders, we discuss mechanistic, translational, safety, regulatory, and ethical considerations. This framework links objective physiological constructs to functional outcomes, offering a mechanistically grounded path for adjunctive therapy development in SMA and related conditions.

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.