The production of high-quality planting material for the most commonly used woody species in urban greening is a complex challenge influenced by several factors. One of the key factors in seedling production, especially in pots, is the selection of an appropriate substrate. Testing various substrates for different species is an ongoing process. In Bosnia and Herzegovina, four of the most frequently used taxa for urban planting in the region were tested: two varieties of northern white-cedar (Thuja occidentalis L.), namely 'Smaragd' and 'Columna', as well as European boxwood (Buxus sempervirens L.) and Forsythia (Forsythia × intermedia). One-year-old rooted cuttings were transplanted into two-liter pots using five different substrates: plain soil, compost, lumbrico humus, peat, and a combination of all these substrates. At the end of the growing period, morphometric parameters of the seedlings were measured, and based on these measurements, quality indices were determined. The results showed that each species and variety responded differently to the various substrates. Generally, the best results for most of the taxa were obtained using locally produced lumbrico humus and compost. However, some seedlings, such as Thuja occidentalis 'Smaragd', exhibited similar growth characteristics across all substrates tested. Considering the varying costs of the substrates, it is possible to plan the production of these seedlings with significantly better quality while also reducing production costs.

`Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown success in single-physics problems, yet their effectiveness in capturing nonlinear interactions in coupled systems (such as thermo-mechanical or electro-thermal coupling) remains underexplored. Here we pose a practical question: should the architecture of a neural operator reflect the strength of physical coupling it aims to model? We compare single-branch and multi-branch designs, in both feedforward and sequential recurrent forms, across three representative systems: a reaction--diffusion problem with heterogeneous sources, a nonlinear thermo-electrical problem with temperature-dependent conductivity and Joule heating, and a viscoplastic thermo-mechanical model of steel solidification. Single-branch networks consistently outperform multi-branch variants in tightly coupled regimes by encouraging shared latent representations, whereas multi-branch designs remain favorable for decoupled or single-physics tasks. Once trained, these surrogates deliver full-field predictions up to $1.8 \times 10^4$ times faster than physics-based solvers.

Abstract This article focuses on the allocation of subnational aid from Central European donors and Serbia to Bosnia & Hercegovina between 2005 and 2020. Spatial and statistical analyses revealed different patterns of aid distribution among municipalities in Bosnia & Hercegovina. Two of the seven donors studied—Croatia and Serbia—showed a clear bias in favour of their ethnic minorities in Bosnia & Hercegovina. For other Central European donors there was a general tendency to provide less aid to municipalities with more Croats. The relationship between variables approximating recipients’ needs and Central European aid was weak or insignificant.

The impact of resins with varying ligands and pH levels on human IgG4 protein was analysed using anion exchange chromatography. Initially, a resin screening study involving five different positively charged ligands from four different brands was conducted on largely purified monoclonal antibodies, following Protein A capture. Subsequently, the influence of pH levels (6, 7, and 8.1) on an IgG4 protein with an isoelectric point of 6.9 was assessed using a single resin. Throughout the resin screening, all protein quality analyses were performed to identify the resin with the most compatible ligand. The study on pH effects revealed that when the pH exceeded 6.9, various protein fragments were removed, directly affecting the protein charge variant. When the protein pH was at or below the isoelectric point, the anion exchange chromatography flow-through method achieved a maximum protein recovery of 92-98%.

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.

The future circular $e^+ e^-$ collider (FCC-ee) stands out as the next flagship project in particle physics, dedicated to uncovering the microscopic origin of the Higgs boson. In this context, we assess indirect probes of the Minimal Supersymmetric Standard Model (MSSM), a well-established benchmark hypothesis, exploring the complementarity between Higgs measurements and electroweak precision tests at the $Z$-pole. We study three key sectors: the heavy Higgs doublet, scalar top partners, and light gauginos and higgsinos, focusing on the parameter space favored by naturalness. Remarkably, the Tera-$Z$ program consistently offers significantly greater indirect sensitivity than the Mega-$h$ run. While promising, these prospects hinge on reducing SM uncertainties. Accordingly, we highlight key precision observables for targeted theoretical work.

Successful speech communication in multi-talker scenarios requires a skilful combination of sustained attention and rapid attention switching. While the neurophysiology literature offers detailed insights into the neural underpinnings of sustained attention, there remains considerable uncertainty on how attention switching takes place. In this study, using EEG recordings from normal-hearing adults in an immersive multi-talker environment, we measured the neural encoding of two competing speech streams amid background babble. Participants were cued to switch attention between streams every 15–30 seconds. Neural tracking was assessed via Temporal Response Functions (TRF), confirming reliable decoding of attentional focus. Our results indicate asymmetric disengagement and engagement processes during attention switches, where the neural tracking of the new target stream emerges before disengaging from the previous target, revealing a transient simultaneous encoding of two speech streams. That transition was closely mirrored by a reduction in EEG alpha power, informing on the cognitive effort during different phases of the attention switch. We then isolated cortical activity reflecting lexical prediction mechanisms to determine how lexical context is updated after an attention switch, comparing four numerical hypotheses that were constructed using Large Language Models. Our findings elucidate both the temporal and contextual mechanisms underlying auditory attention shifts, pointing to the possibility that listeners carry out a reset in lexical context after switching attention. By focusing on dynamic attentional reallocation, this study offers insights into the brain’s capacity for flexible speech processing in complex listening environments.

Analysis of conversions between compressional and shear waves is a workhorse method for constraining crustal and lithospheric structure on Earth; yet, such converted waves have not been unequivocally identified in seismic data from the largest events on the Moon, due to the highly scattered waveforms of shallow seismic events. We reanalyze the polarization attributes of waveforms recorded by the Apollo seismic network to identify signals with rectilinear particle motion below 1 Hz, arising from conversions across the crust‐mantle boundary. Delay times of these converted waves are inverted to estimate crustal thickness and wavespeeds beneath the seismometers. Combined with gravimetric modeling, these new crustal thickness tie‐points yield an updated lunar crustal model with an average thickness of 29–47 km. Unlike previous models, ours include explicit uncertainty estimates, offering critical context for future lunar missions, geophysical studies, and predicting 15–36 km crust at Schrödinger and 29–52 km at Artemis III sites.

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.