In this study, we investigate integrating eye tracking with auditory attention decoding (AAD) using portable EEG devices, specifically a mobile EEG cap and cEEGrid, in a preliminary analysis with a single participant. A novel audiovisual dataset was collected using a mobile EEG system designed to simulate real-life listening environments. Our study has two main objectives: (1) to use eye tracking data to automatically infer the labels of attended and unattended speech streams, and (2) to train an AAD model using these estimated labels, evaluating its performance through speech reconstruction accuracy. The results demonstrate the feasibility of using eye tracking data to estimate attended speech labels, which were then used to train speech reconstruction models. We validated our models with varying amounts of training data and a second dataset from the same participant to assess generalization. Additionally, we examined the impact of mislabeling on AAD accuracy. These findings provide preliminary evidence that eye tracking can be used to infer speech labels, offering a potential pathway for brain-controlled hearing aids, where true labels are unknown.

Hearing aid (HA) users often experience increased listening effort, particularly in noisy environments. While noise reduction (NR) algorithms aim to alleviate this, traditional electroencephalography (EEG) methods based on power analysis have limited success in assessing the listening effort in this population. This study proposes a novel method using a whole-head synchronization map analysis that uses local connectivity, a measure of statistical dependencies within localized brain regions. We use EEG electrodes to define a region based on the surrounding electrodes in the first-order neighborhood. This approach was tested using EEG data from 22 HA users with active or inactive NR engaged in a continuous speech-in-noise (SiN) task at low (3dB) and high (8dB) signal-to-noise ratio (SNR) levels. Whole-head synchronization was quantified using circular omega complexity (COC), a multivariate phase synchrony measure. Results showed increased local connectivity in the alpha band (8–12 Hz) within frontal and occipital regions during SiN condition compared to the background noise-only (NO) condition. Furthermore, NR activation impacted the synchronization map differently at the two SNRs of the experiment, with greater effect observed at low SNR, primarily in the left parietal region and alpha band. This behavior is in line with that of existing measures for listening effort, and therefore suggests that EEG local connectivity analysis holds promise as a tool for objectively assessing listening effort in HA users, especially in challenging listening environments.

Purpose – Money laundering is one of the most widespread phenomena in the financial world which is seriously threatening the integrity of system and representing a significant risk to a country’s economic development, as well as its progress in geopolitical and infrastructural terms. In recent years, Bosnia and Herzegovina (B&H) has frequently appeared in various studies, articles, and media publications as one of the countries where this phenomenon is becoming more and more popular, and now we are witnessing that our country is being referred to as a “paradise” for money laundering. This research will focus on the role of Bosnia and Herzegovina’s financial and business sectors, analyzing their role in the money laundering process and attempting to light up on some of the most common methods related to this phenomenon in Bosnia and Herzegovina. Methodology/Research Approach – The research will be conducted using both qualitative and quantitative methods. A detailed analysis of secondary sources of information will be carried out, along with the collection of primary data on the given topic. A review of previously published works and relevant literature will also be conducted. Limitations/Implications – The topic of this research is relatively unexplored and does not receive enough attention in the existing literature/studies, which presents a challenge in gathering needed data. The high unavailability of key information may limit the depth of analysis and accuracy of conclusions. Given the limited data sources, the research has been conducted in accordance with the available information from the approximately last 10 years, which may affect the scope and validity of the findings. Practical Implications – This research contributes to a better understanding of the money laundering phenomenon, with a particular focus on the role of the business and financial sectors in Bosnia and Herzegovina. The research results can help in developing more effective strategies to combat money laundering, thereby reducing the harmful economic and social consequences that this phenomenon brings. Practical recommendations may include improvements in legal provisions and strengthening oversight and control in the business and financial sectors. Originality – This research provides an original perspective on money laundering in the context of Bosnia and Herzegovina’s business and financial sectors and encourages further discussions and deeper investigations. Previous studies can mostly be characterized as reviews, whereas this paper brings together all relevant macroeconomic variables and variables of interest in this case, offering a deeper insight into and addressing a previously unexplored area.

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.

Scientific applications increasingly demand real-time surrogate models that can capture the behavior of strongly coupled multiphysics systems driven by multiple input functions, such as in thermo-mechanical and electro-thermal processes. While neural operator frameworks, such as Deep Operator Networks (DeepONets), have shown considerable success in single-physics settings, their extension to multiphysics problems remains poorly understood. In particular, the challenge of learning nonlinear interactions between tightly coupled physical fields has received little systematic attention. This study addresses a foundational question: should the architectural design of a neural operator reflect the strength of physical coupling it aims to model? To answer this, we present the first comprehensive, architecture-aware evaluation of DeepONet variants across three regimes: single-physics, weakly coupled, and strongly coupled multiphysics systems. We consider a reaction-diffusion equation with dual spatial inputs, a nonlinear thermo-electrical problem with bidirectional coupling through temperature-dependent conductivity, and a viscoplastic thermo-mechanical model of steel solidification governed by transient phase-driven interactions. Two operator-learning frameworks, the classical DeepONet and its sequential GRU-based extension, S-DeepONet, are benchmarked using both single-branch and multi-branch (MIONet-style) architectures. Our results demonstrate that architectural alignment with physical coupling is crucial: single-branch networks significantly outperform multi-branch counterparts in strongly coupled settings, whereas multi-branch encodings offer advantages for decoupled or single-physics problems. Once trained, these surrogates achieve full-field predictions up to 1.8e4 times faster than high-fidelity finite-element solvers, without compromising solution accuracy.

`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.

Access to safe drinking water and adequate sanitation is essential for maintaining public health. This study investigates water collection, treatment practices, perceptions of water safety, and their health impacts within a community. Understanding these aspects is crucial for developing interventions to enhance water safety and health outcomes. A cross-sectional survey was conducted among 360 households in the community and the study subjects were selected randomly from these households. Data was collected through a survey conducted in 360 households, focusing on water collection methods, types of containers, water treatment practices, and sanitation and hygiene behaviors, perceptions of water safety, and the health effects of unsafe water. Descriptive statistics was applied to analyze the frequency and percentage distributions of the variables using SPSS version 20.0. The most common containers for water collection were jerricans (44.2%) and plastic containers (38.6%). While 74.7% of the containers were covered, a notable portion remained uncovered (25.3%). Although 72.8% of respondents believed their water was safe to drink, only 58.1% of the respondents treated their water. Boiling was the most common water treatment method (36.3%), followed by filtration (31.1%) and chemical treatment (30.6%). The primary reasons for not treating water (n  = 151) included the belief that water was already clean (62 respondents, 41.1%) and lack of knowledge about water treatment (60 respondents, 39.7%). There was a major recognition of the health effects of unsafe water, with 38.3% associating it with diarrhea and 30.3% with cholera. However, 47.5% did not link water quality to health, indicating gaps in knowledge. The majority of households used toilets (76.9%), but open defecation (13.9%) and improper waste disposal practices, such as disposing of wastewater into open drains (37.5%), were prevalent. Most households consumed more than 20 L of water daily (63.6%), with monthly expenditures on water varying widely. While 40.6% of households spent $1–5 per month on water, a small but major portion faced higher costs. The study reveals that the community is aware of the importance of water safety and sanitation, but faces challenges in consistent water treatment and wastewater disposal. There is a need for enhanced awareness and education on the health risks of untreated water and support for affordable treatment methods. Improving sanitation infrastructure and providing equitable access to safe drinking water are critical for better health outcomes. Policy interventions and community engagement are essential to address these issues and ensure sustainable water safety practices.

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.

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.

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%.

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.