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.

UK summer heatwaves are dictated by the polar jet stream position and sea surface temperature (SST) variability, affecting the Summer North Atlantic Oscillation (SNAO) index. The SNAO can determine and influence the Central England Precipitation (CEP) and Central England Temperature (CET). A strong and significant negative correlation ( r  = −0.63) is found between the SNAO and CEP, but a weaker correlation for the CET. Summers with highly positive SNAO (i.e. 1976 and 2018) are among the driest and warmest on record. In this study, we highlight the roles of large‐scale atmospheric circulations and use of Met Office‐defined weather regimes in understanding UK heatwave characteristics.

This article enables preventive engineering and the risk of negative occurrences in the transport and temporary parking of vehicles with dangerous goods can be reduced, thus decreasing interactions with other traffic participants. An important element in transporting dangerous goods is defining suitable locations where these vehicles can be safely parked. The research, which consists of several phases, has been carried out along a highway network of approximately one thousand kilometres in length. Out of 95 potentially acceptable locations, 28 have been selected (for both directions) as the most suitable. In the first part of the research, a fuzzy Z multi-criteria decision-making (MCDM) model has been created to select the most suitable locations, followed by the formation of a total of 32 scenarios (16 for each direction), integrating the fuzzy PIvot Pair-wise RElative Criteria Importance Assessment (PIPRECIA) method with Z-numbers with a Geographic Information System (GIS) model. Such integration represents an original and unique model. Fuzzy MCDM model based on Z numbers enables more accuracy in defining the assessment of criteria due to considering probabilities of a given assessment. When evaluating the formed scenarios, a minimum distance between two adjacent locations has been taken into account, and it is 50 km. The obtained results show most suitable scenario S3 = S4 = 35.278 for the first direction, and S2 = S10 = 35.521 for the second direction. Sensitivity analysis through 24 sets shown that a few scenarios can be candidate for implementation depending from significance of criteria. The contribution of this article lies in its pioneering research of this kind and provides recommendations to relevant state authorities for implementing the best scenario, but final implementation depends from representative entities.

Although advances such as chain-of-thought, tree-of-thought or reinforcement learning have improved the performance of LLMs in reasoning and planning tasks, they are still brittle and have not achieved human-level performance in many domains, and often suffer from high time and token costs. Inspired by the success of width-based search in planning, we explore how the concept of novelty can be transferred to language domains and how it can improve tree-of-thought reasoning. A tree of thoughts relies on building possible"paths"of consecutive ideas or thoughts. These are generated by repeatedly prompting an LLM. In our paper, a measurable concept of novelty is proposed that describes the uniqueness of a new node (thought) in comparison to nodes previously seen in the search tree. Novelty is estimated by prompting an LLM and making use of embedded general knowledge from pre-training. This metric can then be used to prune branches and reduce the scope of the search. Although this method introduces more prompts per state, the overall token cost can be reduced by pruning and reducing the overall tree size. This procedure is tested and compared using several benchmarks in language-based planning and general reasoning.

Very thin ZnO films were fabricated on p-type Si (100) substrates using two different solvents, methanol (M) and 2-methoxyethanol (2M), via a simple spin-coating method. Five-layer ZnO films were characterized structurally using X-ray diffraction (XRD), with detailed analysis performed using the Williamson–Hall (W-H) method. Surface morphology was examined by scanning electron microscopy (SEM) and atomic force microscopy (AFM), while ultraviolet–visible (UV–Vis) spectroscopy provided optical properties, including the optical band gap determined via the Kubelka–Munk theory. The crystallite sizes calculated from W-H models were larger than those estimated by the Debye–Scherrer method, indicating that peak broadening is influenced by both crystallite size and lattice strain. Strain, stress, and deformation energy density values, derived from the Uniform Deformation Model (UDM), Uniform Stress Deformation Model (USDM), and Uniform Deformation Energy Density Model (UDEDM), respectively, were higher in films prepared with 2-methoxyethanol than methanol. ZnO films from 2-methoxyethanol exhibited greater homogeneity and lower surface roughness, while their optical band gap (~3.3 eV) was slightly higher than that of methanol-derived films (~3.02 eV) but still below bulk ZnO (3.37 eV). These results show that ZnO thin films prepared with 2-methoxyethanol exhibit superior microstructural, mechanical, and optical properties, producing smoother and more uniform films with enhanced optical characteristics, and are therefore more commonly preferred for optoelectronic and photovoltaic applications due to their improved crystallinity, low surface roughness, and suitable band gap.  

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.

Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but lack inference acceleration. This paper explores the middle ground of statistically-lossless compression, examining three complementary aspects of what losslessness means for quantized LLMs. First, task-lossless compression preserves zero-shot benchmark accuracy within natural sampling variance and is achievable at aggressive bitwidths. Second, we formalize the stricter notion of distribution-lossless compression, requiring the quantized model's next-token distribution to be practically indistinguishable from the original, and propose the Expected Acceptance Rate (EAR), the maximum token-agreement probability under optimal coupling, as a directly interpretable fidelity metric. For example, EAR>= 0.99 means 99% agreement. Third, we prove a gamma-squared variance law showing that symmetric quantization inflates noise variance by gamma^2 relative to asymmetric quantization, making asymmetric quantization a prerequisite for distribution-lossless fidelity but not for task-level preservation. Through SLQ, a layer-wise non-uniform method with asymmetric quantization and wide bitwidth search, we obtain task-lossless compression at well below 4 bits per parameter, as low as 3.3 bits depending on the model, distribution-lossless compression at 5-6 bits per parameter on average, and inference speedups of 1.7-3.7x compared to FP16 using optimized kernels. Source code is available at [https://github.com/IST-DASLab/SLQ](https://github.com/IST-DASLab/SLQ).

Objective Description of the clinical presentation and tumor behavior, its general and pathological characteristics, diagnostic and therapeutic approaches, as well as treatment outcomes in children with neuroblastoma (NB) at a single-center. Special focus was placed on the duration of the diagnostic process and the correlation between disease stage, presence of metastases, and treatment outcomes. Given the small cohort, this report is presented as a descriptive case series complemented by a focused review of the literature NB. Material and Methods A retrospective study was conducted for the time period from January 1st, 2010, to December 31st, 2024. The study included 13 patients (7 males, 6 females; median age: 44 months; range, 0-114) diagnosed with NB who underwent surgery and were hospitalized at the Department of Pediatric Surgery, Clinical Center of the University of Sarajevo. Demographic, clinical, biochemical, radiological, and treatment data were extracted from medical records. A narrative review of major published NB cohorts was conducted to compare our observations with existing evidence. Results The median age at diagnosis was 44 months. The largest number of patients were in the age group of 2-4 years, and most presented with stage four disease (according to International Neuroblastoma Staging System classification). The tumor was located in the adrenal gland in 92.3% of cases. The median survival time was 36 months. Patients had elevated serum neuron-specific enolase, urinary vanillylmandelic acid, as well as blood ferritin and lactate dehydrogenase levels. The most common symptoms were loss of appetite, presence of a palpable mass, and abdominal pain. Metastases were present in 76.9% of patients, and stage four was associated with aggressive metastatic dissemination. A multimodal approach was used in treatment. The median time from symptom onset to primary care physician consultation was 12 days, and the median time from physician consultation to diagnosis was 14 days. Relapse occurred in 46.2% of cases, most frequently in patients with stage four disease. A total of 7 patients (46.2%) survived. Conclusion Advanced-stage disease at diagnosis, metastatic burden, and unfavorable outcomes, are dominating factors in our single-center experience. When compared with larger published cohorts, our findings align with global patterns. Due to inherent sample size limitations, these observations are hypothesis-generating and the need for larger, multicenter studies remains.

ABSTRACT To check the occurrence of new natural β-diketones in Helichrysum italicum L. a series of seven β-diketones was synthesized and used in co-injection experiments to detect their presence in several essential oil and hydrolate samples of wild Helichrysum italicum L. from Corsica, Bosnia and Croatia. Two diketones were identified for the first time in this species: 4-methylheptan-3,5-dione (already known as an insect pheromone), as well as 3-methylhexan-2,4-dione, which has never been reported so far as a natural product.

We introduce entropic strict minimum message length (SMML), a risk-sensitive generalization of strict minimum message length coding. The proposed criterion replaces expected two-part codelength under the prior predictive distribution with an exponential certainty equivalent, thereby defining a one-parameter family of coding rules that interpolates between Bayesian average-case coding and worst-case minimax coding. We show that ordinary SMML is recovered in the risk-neutral limit, while the extreme risk-sensitive limit yields a minimax codelength criterion. Applying the same entropic soft maximum to regret relative to the oracle maximum likelihood codelength recovers the normalized maximum likelihood (NML) minimax-regret principle. We further prove that entropic SMML admits a variational characterization as a Kullback--Leibler-regularized worst-case expected codelength, giving it a PAC--Bayes-type interpretation. We establish joint \(n\)--\(\tau\) asymptotics that identify how the risk parameter must scale with sample size in order to recover Bayesian average-case, intermediate robust, and worst-case minimax coding behavior. For regular exponential families, the fixed-codebook partition remains affine in sufficient-statistic space, while the codepoints satisfy a tilted moment-matching condition and admit an interpretation as tilted Bregman centroids. These results position entropic SMML as an information theoretic bridge between MML, PAC--Bayes, and MDL.

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.

Metal additive manufacturing (AM) relies on alloy feedstock powders that may come into contact with the workers’ skin during handling, yet skin-relevant data on metal release and biological reactivity remain limited. Here, we assessed the cutaneous bioactivity of the fine particle fraction of four gas-atomized Fe-based AM powders (316L stainless steel, Fe-powder A, and tooling steels B and C). Powders were sieved to <10 μm and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy before and after incubation in artificial sweat (ASW). Metal biodissolution was quantified in ASW and keratinocyte culture medium using atomic absorption spectrophotometry. Cellular responses were evaluated in HaCaT keratinocytes using Cell Painting-based phenomics and multiplex cytokine/chemokine profiling and in an ex vivo full-thickness human skin explant model, including superficial barrier disruption, IL-8/CXCL8 quantification, and histological assessment. ASW exposure induced marked shifts in the outermost surface composition across powders, indicating sweat-driven surface transformation. Biodissolution was low and medium-dependent, with Fe dominating the release in ASW, and with an overall metal release remaining limited in cell culture medium. In HaCaT cells, MCP-1/CCL2, IL-6, and IL-8/CXCL8 were quantifiable but showed no significant changes following powder exposure. Cell Painting revealed subtle, shared phenotypic signatures, primarily involving mitochondrial-associated features, without evidence of broad cellular stress. In the ex vivo skin model, AM powders did not increase IL-8/CXCL8 secretion, the particles remained localized to the skin surface without detectable penetration, and coexposure with Staphylococcus epidermidis did not enhance bacterial colonization or induce inflammation. To the best of our knowledge, this is the first study that applies a human skin explant model to evaluate dermal responses to metal AM powders. Overall, the tested AM powders showed low short-term cutaneous reactivity under skin-relevant conditions, providing human-relevant evidence to inform occupational risk assessment in AM environments.

Executive function is an essential cognitive domain for typical human behavior which is disrupted in neurodevelopmental and neurodegenerative disorders, but little is known about its underlying molecular basis. To address this, we perform genome-wide association studies (GWAS) using three different measures of executive function in UK Biobank (N = 84,238) and NIHR BioResource’s Genes and Cognition (N = 9932) study participants, followed by a meta-analysis. The trail-making alphanumeric (TMA) measure is the most heritable phenotype (h²=7-26%), associated with 18 independent loci that exhibit a similar direction of effect in both cohorts. Across these loci, in-silico follow-up implicates 178 genes, of which NT5DC2 and RP11-579E24.2 are independently replicated prior to meta-analysis. TMA is linked to pan-cerebral differences in brain structure, with brain-enriched genes showing a biphasic expression profile from early development through to later life. Our data implicate specific cell types, histone modifications and butyrophilin immunoglobulin family proteins as potential targets for promoting cognitive resilience. A genome-wide association meta analysis of Trail Making enriches our understanding of the genetic landscape of executive functioning, identifies cognitive and neural correlates, and reveals a cell-type specific developmental origin.

BACKGROUND AND AIMS The timing of aortic valve replacement (AVR) in severe asymptomatic aortic stenosis (AS) remains debated. Preserved ejection fraction (EF) may mask subclinical dysfunction, while global longitudinal strain (GLS), brain natriuretic peptide (BNP), and diastolic indices (E/E') provide complementary prognostic information. A predictive model for adverse outcomes after AVR integrating GLS, BNP, and E/E' has not been previously investigated. METHODS Ninety-six patients with severe asymptomatic AS and preserved EF (>50%) undergoing AVR were assessed at baseline and 1, 3, and 6 months. Echocardiography (GLS, EF, LVMI, IVSd, LVIDd, E/E'), BNP, and clinical outcomes were analyzed. Primary endpoint was LV remodeling; secondary endpoint was major adverse cardiovascular events (MACE). RESULTS Despite preserved EF, 76% had impaired GLS (<15%), and 64% remained in negative remodeling at 6 months. Baseline GLS ≤15% was the only independent predictor of adverse remodeling in multivariable logistic regression (OR 4.7 at 3 months; OR 3.5 at 6 months). For MACE, baseline E/E' >13 was the strongest independent predictor (OR 3.15, 95% CI 1.58-7.57, p = 0.004). The integrated GLS-BNP-E/E' model demonstrated superior predictive strength compared with individual parameters, with Nagelkerke R2 values of 0.41 for remodeling and 0.31 for MACE. CONCLUSION A multimodal risk model integrating GLS, BNP, and E/E' predicts adverse remodeling and MACE in severe asymptomatic AS. These findings highlight the complementary role of imaging and biomarkers in risk stratification before AVR-a concept that warrants confirmation in future multicenter studies.