Partial differential equations (PDEs) are fundamental to modeling complex and nonlinear physical phenomena, but their numerical solution often requires significant computational resources, particularly when a large number of forward full solution evaluations are necessary, such as in design, optimization, sensitivity analysis, and uncertainty quantification. Recent progress in operator learning has enabled surrogate models that efficiently predict full PDE solution fields; however, these models often struggle with accuracy and robustness when faced with highly nonlinear responses driven by sequential input functions. To address these challenges, we propose the Sequential Neural Operator Transformer (S-NOT), a architecture that combines gated recurrent units (GRUs) with the self-attention mechanism of transformers to address time-dependent,nonlinear PDEs. Unlike S-DeepONet (S-DON), which uses a dot product to merge encoded outputs from the branch and trunk sub-networks, S-NOT leverages attention to better capture intricate dependencies between sequential inputs and spatial query points. We benchmark S-NOT on three challenging datasets from real-world applications with plastic and thermo-viscoplastic highly nonlinear material responses: multiphysics steel solidification, a 3D lug specimen, and a dogbone specimen under temporal and path-dependent loadings. The results show that S-NOT consistently achieves a higher prediction accuracy than S-DON even for data outliers, demonstrating its accuracy and robustness for drastically accelerating computational frameworks in scientific and engineering applications.

We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed curvature and rigid embeddings, ManifoldMind represents users, items, and tags as adaptive-curvature probabilistic spheres, enabling personalised uncertainty modeling and geometry-aware semantic exploration. A curvature-aware semantic kernel supports soft, multi-hop inference, allowing the model to explore diverse conceptual paths instead of overfitting to shallow or direct interactions. Experiments on four public benchmarks show superior NDCG, calibration, and diversity compared to strong baselines. ManifoldMind produces explicit reasoning traces, enabling transparent, trustworthy, and exploration-driven recommendations in sparse or abstract domains.

Brewer’s spent grain (BSG), the most abundant by-product from breweries, is mainly discarded or used as animal feed. However, to increase the brewing sustainability, biotechnological utilization of BSG is a much preferred solution. This study examined the fermentation of BSG, composed of old wheat bread and barley malt, by metabolic activity of Saccharomyces cerevisiae on both hydrolyzed and non-hydrolyzed media. Enzymatic hydrolysis with Viscozyme® W FG for 6 h was selected as the most effective and was used in the further research step to prepare the hydrolyzed BSG-based medium. Both media supported almost uniform yeast growth (numbers of S. cerevisiae cells was about 8 log10 CFU/g) in an acidic environment (pH value was about 5), but fermentation of hydrolyzed BSG resulted in 20% higher sugar consumption and 10% higher total titratable acidity. These findings underscore the potential of enzymatic pretreatment to improve fermentation performance. The adaptability of S. cerevisiae and the fermentability of both substrates suggest promising potential for scalable BSG valorization strategies in circular food systems.

U radu su razmatrana trenutna i konsolidacijska slijeganja kvadratnog temelja za dvije različite geomehaničke sredine. Prvi slučaj je pjeskovito tlo ispod kojeg se nalazi sloj gline, a druga geomehanička sredina je pjeskovito tlo u mješavini sa šljunkom te ojačano cementnim prahom (30%) i staklenim vlaknima (1%) ispod kojeg se nalazi glina. Dobiveni rezultati slijeganja pokazuju dobru stabilizaciju tla i povoljne geomehaničke karakteristike ojačanog tla koji se ogledaju u manjoj vrijednosti ukupnog slijeganja temelja koja zadovoljava maksimalnu dopuštenu vrijednost definiranu Eurocode-om. U radu su prikazana slijeganja kroz vremensko razdoblje od 120 dana, prikazano je konačno, dugoročno slijeganje ispod temelja kao i diferencijalni omjer (δ/L) slijeganja temelja. Proračun trenutnog slijeganja vršen je prema teoriji elastičnosti odnosno konsolidacijsko slijeganje prema Terzaghi-ju.

Evaluation of the performance of teleoperation systems plays an important role in assessing the efficacy and reliability of such systems. The evaluation is usually performed based on factors such as stability, transparency, and user satisfaction. However, very few studies have addressed the numerical evaluation of transparency in teleoperation systems so far. This letter presents a novel method to numerically assess the transparency of teleoperation systems based on representing recorded experimental data algebraically by fitting parametric curves using Elliptic Fourier Descriptors (EFD). The EFD coefficients are used to compute the Hybrid Matrix of the teleoperation system, which provides a metric for judging how transparent a teleoperation system is. This letter validates the proposed method using real experimental position and force data for teleoperation systems with and without time delay, as well as providing an analysis of the effect of the number of harmonics on the calculation of the Hybrid Matrix.

This study investigated the potential of high-voltage electrical discharge (HVED), as a green, non-thermal extraction technology, for recovering polyphenols from winter savory (Satureja montana L.). Key process parameters, including frequency (40, 70, 100 Hz) and extraction time (1, 5, 15, 30, 45 min), were optimized, using water as a solvent and maintaining a constant solid-to-liquid ratio of 1:100 g/mL. The extracts were characterized for total polyphenol content (TPC), total flavonoid content (TFC), and antioxidant activity (DPPH, ABTS, FRAP), while individual phenolics were quantified via HPLC-DAD. Multivariate chemometric analyses, including Pearson correlation, heatmap clustering, and principal component analysis (PCA), were employed to reveal relationships between extraction conditions, polyphenolic profiles, and antioxidant activities. The results showed strong correlations between TPC, TFC, and antioxidant activity, with compounds such as quercetin-3-D-galactoside, procyanidin A2, and rutin identified as key contributors. Among the tested conditions, extraction at 70 Hz for 45 min provided the highest polyphenol yield and bioactivity. The application of HVED demonstrated its potential as an efficient and environmentally friendly technique for obtaining phenolic-rich extracts. In addition, the use of chemometric tools provided useful insights for optimizing extraction conditions and understanding the contributions of specific compounds to bioactivity. These results support future applications in clean-label product development and contribute to broader efforts in sustainable ingredient production for the food, cosmetic, and nutraceutical sectors.

Narrative review synthesizes the most current literature on the SARS-CoV-2 XEC variant, focusing on its genomic evolution, immune evasion characteristics, epidemiological dynamics, and public health implications. To achieve this, we conducted a structured search of the literature of peer-reviewed articles, preprints, and official surveillance data from 2023 to early 2025, prioritizing virological, clinical, and immunological reports related to XEC and its parent lineages. Defined by the distinctive spike protein mutations, T22N and Q493E, XEC exhibits modest reductions in neutralization in vitro, although current evidence suggests that mRNA booster vaccines, including those targeting JN.1 and KP.2, retain cross-protective efficacy against symptomatic and severe disease. The XEC strain of SARS-CoV-2 has drawn particular attention due to its increasing prevalence in multiple regions and its potential to displace other Omicron subvariants, although direct evidence of enhanced replicative fitness is currently lacking. Preliminary analyses also indicated that glycosylation changes at the N-terminal domain enhance infectivity and immunological evasion, which is expected to underpin the increasing prevalence of XEC. The XEC variant, while still emerging, is marked by a unique recombination pattern and a set of spike protein mutations (T22N and Q493E) that collectively demonstrate increased immune evasion potential and epidemiological expansion across Europe and North America. Current evidence does not conclusively associate XEC with greater disease severity, although additional research is required to determine its clinical relevance. Key knowledge gaps include the precise role of recombination events in XEC evolution and the duration of cross-protective T-cell responses. New research priorities include genomic surveillance in undersampled regions, updated vaccine formulations against novel spike epitopes, and long-term longitudinal studies to monitor post-acute sequelae. These efforts can be augmented by computational modeling and the One Health approach, which combines human and veterinary sciences. Recent computational findings (GISAID, 2024) point to the potential of XEC for further mutations in under-surveilled reservoirs, enhancing containment challenges and risks. Addressing the potential risks associated with the XEC variant is expected to benefit from interdisciplinary coordination, particularly in regions where genomic surveillance indicates a measurable increase in prevalence.

Introduction. Sentinel lymph node biopsy (SLNB) has significantly advanced axillary staging in clinically node-negative breast cancer, offering lower morbidity compared to traditional axillary lymph node dissection (ALND). Nonetheless, precise prediction of non-sentinel lymph node (non-SLN) involvement remains essential for optimizing surgical decisions and preventing unnecessary ALND. Methods. A retrospective cohort analysis was performed on 176 patients with clinically node-negative breast cancer who underwent SLNB. Clinicopathological data were reviewed to evaluate associations between various predictive factors and non-SLN involvement. Variables analyzed included tumor size, histological grade, lymphovascular invasion (LVI), Ki-67 proliferation index, and sentinel lymph node characteristics. Results. Multivariable logistic regression identified the type of SLN metastasis (OR=21.4; 95% CI 1.7–43.6; p=0.01), the number of positive SLNs (OR=5.66; 95% CI 1.18–36.6; p=0.03), and the number of negative SLNs (OR=0.04; 95% CI 0.006–0.27; p=0.001) as independent predictors of non-SLN metastases. The predictive model demonstrated excellent discriminatory power, with an area under the receiver operating characteristic curve (AUC) of 0.91. Conclusion. Specific clinical and histopathological variables reliably predict non-SLN involvement in SLN-positive breast cancer patients. Incorporation of these predictors into clinical practice may enhance individualized axillary management and reduce unnecessary ALND procedures. Further validation through larger prospective studies is warranted. Key words: Breast Neoplasms, Sentinel Lymph Node Biopsy, Axillary Lymph Nodes, Lymph Node Dissection, Neoplasm Staging.

Background Non-ST-elevation myocardial infarction (NSTEMI) is frequently associated with systemic inflammation and metabolic dysregulation. Indices derived from routine laboratory tests that reflect systemic inflammatory and lipid-inflammatory status may offer better prognostic insight. This study aimed to evaluate the association between selected indices and short-term major adverse cardiovascular events (MACE) and all-cause mortality in patients with NSTEMI treated with dual antiplatelet therapy (DAPT) and statin. The selected indices reflect key mechanisms involved in NSTEMI pathophysiology, including insulin resistance, atherogenic dyslipidemia, and inflammation. Materials and methods This prospective observational study included 171 patients with NSTEMI admitted to the Intensive Care Unit of the Clinic for Internal Medicine at the University Clinical Centre Tuzla between February 1, 2022, and January 31, 2023. Blood samples were collected upon admission and 24 hours subsequently. The following indices were calculated: triglyceride-glucose index (TyG), triglyceride-to-high-density lipoprotein ratio (TG/HDL), atherogenic index of plasma (AIP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and pan-immune-inflammation value (PIV). Outcomes were tracked during hospitalization and up to three months post-discharge. MACE was defined as cardiovascular death, reinfarction, stroke, or unplanned revascularization. All patients underwent coronary angiography; revascularization was performed when clinically indicated. Exclusion criteria included active malignancy, infection, or inflammatory disease. Logistic regression was adjusted for age, diabetes, and other clinical variables. Missing data were handled using the pairwise deletion method. Results High levels of TyG at admission were independently associated with MACE (odds ratio (OR) 1.7; 95% confidence interval (CI) 1.0-2.8; p = 0.037). All-cause mortality occurred in 14.6% of patients (n = 25), while MACE occurred in 60 patients. Independent predictors of mortality included elevated TyG at admission (OR 2.2; 95% CI 1.1-4.4; p = 0.034), TG/HDL at 24 hours (OR 1.4; 95% CI 1.1-1.7; p = 0.007), AIP at 24 hours (OR 5.7; 95% CI 1.1-28.9; p = 0.035), and NLR at 24 hours (OR 1.1; 95% CI 1.0-1.2; p = 0.002). PLR and PIV at 24 hours were also significantly associated with mortality. Optimal cut-off values were TyG ≥ 8.9, AIP ≥ 0.35, and NLR ≥ 4.5. NLR had the highest estimated area under the curve (AUC ≈ 0.78). Conclusion In NSTEMI patients treated with DAPT and statin, several inflammatory and lipid-inflammatory indices were independently associated with short-term mortality. Indices measured at 24 hours had a stronger prognostic value than baseline values. Serial monitoring may aid early risk stratification. Outcomes were assessed during hospitalization and via structured follow-up up to three months post-discharge.

This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space ($\mathcal{C}$-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free $\mathcal{C}$-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.

Summary The rapid rise of 3D printing, both in industrial and home settings, presents emerging health and environmental risks. While 3D printing enhances sustainability by reducing waste and optimizing resource use, its impact on human health remains poorly understood. The use of metals and polymers linked to health risks, coupled with the release of inhalable particles and volatile organic compounds, raises concerns about respiratory and systemic effects. The absence of clear guidelines creates high public demand for information and limits safe implementation, particularly in schools and homes where millions of 3D printers are expected by 2030. Additionally, improper disposal of 3D printing polymer materials may exacerbate plastic pollution. This article proposes the perspective of a structured risk assessment framework set on particle emissions from industrial 3D printing. It will offer a practical tool to bridge current knowledge gaps and to inform safe practice and policy development, because immediate action is necessary to balance innovation with safety.

Procedural modeling methods are used to automatically generate virtual scenes. There is a large number of available top‐down methods for generating partial content for specific purposes. However, little research was done on enabling the generation of content in the presence of manually modeled elements, from the bottom‐up direction, or without significant assistance from the user. No existing approach provides a platform that can combine the results of different methods, which leaves them isolated. This paper presents an integration approach that generates complete virtual space organizations by combining the usage of top‐down and bottom‐up procedural generation of content, with support for the placement of manually modeled content. The integration is made possible by using shape conversion to match the input and output shape types of different methods. The evaluation of the proposed approach was performed on a 2D polygon dataset by using four different scenarios, validating that it works as intended. Additional testing was performed by using a case study of organizing 3D virtual space around the manually modeled element of virtual heritage Tašlihan to demonstrate all capabilities of the integration approach and the different outputs depending on the level of user interaction and the desired results.

The ideological underpinnings of the Great Replacement Theory, which frames Muslims as a threat to Europe, originated in Serbia and emboldened a wider narrative of anti-Muslim hate across Western milieus. The othering of Bosnian Muslims (Bosniaks), an autochthonous ethnic group in Southeastern Europe, has contributed to the normalization of the alienation of Muslims throughout Europe, engendering Educational Displacement—an internalized sense of invisibility and devaluation within targeted individuals, diminishing their participation and trust in the societal institutions. In this complex socio-political and historical context, Bosniaks have nonetheless chosen to principally champion interfaith coexistence, offering an instructive and community-based model of resilience to hate and violence. The study investigates the Bosniaks’ affinity for coexistence by examining the underexplored case of interfaith solidarity and entente between Muslims and Jews in Bosnia and Herzegovina from 1540 to the present.