ABSTRACT Extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae are increasingly reported worldwide, emphasizing the need for rapid diagnostic tools to limit their spread and prevent outbreaks. Although CTX-M enzymes remain predominant, less common ESBLs such as GES, PER, and VEB are emerging and gaining clinical relevance. We developed simplex and multiplex lateral flow immunoassays (LFIAs) for the rapid detection of these ESBLs. Monoclonal antibodies were produced and incorporated into dedicated LFIA strips. The assays were evaluated using 116 WGS-characterized gram-negative bacteria (GNB) isolates expressing diverse β-lactamases and grown on various media. For testing, three colonies were suspended in 150 µL of extraction buffer, applied to the strips, and results were visually interpreted after 15 min. Both simplex and multiplex LFIAs achieved 100% sensitivity and specificity for detecting GES-, PER-, and VEB-producing isolates, irrespective of bacterial species, β-lactamase content, or growth conditions. The panel included prevalent variants of PER (n = 5, including PER-2), VEB (n = 9), and GES (n = 9, including ESBLs and carbapenemases). Detection limits for recombinant PER-1, VEB-1, and GES-1 were 5, 50, and 100 pg/mL, respectively, and 105, 106, and 106 CFU/mL for corresponding Acinetobacter baumannii producers grown in tryptic soy broth. Overall, these simplex and multiplex GES-, PER-, and VEB-targeting LFIAs provide a rapid, accurate, and user-friendly approach suitable for routine use in clinical microbiology laboratories. When combined with the NG-Test CTX-M MULTI assay, which detects the five major CTX-M groups, they may further enhance the overall detection of the most common ESBLs, particularly among non-fermenting GNB. IMPORTANCE Although less prevalent globally than CTX-M-type ESBLs, GES-, PER-, and VEB-type β-lactamases represent an important and often underrecognized mechanism of resistance, particularly in non-fermenting gram-negative pathogens. These enzymes are frequently associated with multidrug-resistant organisms and may significantly compromise available therapeutic options. The detection of the genes encoding these enzymes is often based on homemade PCR. Rapid diagnostic tests based on lateral flow immunoassays (LFIAs) have been successfully used to detect CTX-M-like ESBLs and the five main carbapenemases. Here, we demonstrated that GES, PER, and VEB enzymes can be readily detected by simplex or multiplex LFIAs from Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii colonies. These assays are rapid (≤15 min), highly sensitive (105–106 CFU/mL), requiring no specialized equipment, thus offering a user-friendly alternative to molecular assays in clinical laboratories. These features align well with the World Health Organization ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid, Equipment-free, and Deliverable). Although less prevalent globally than CTX-M-type ESBLs, GES-, PER-, and VEB-type β-lactamases represent an important and often underrecognized mechanism of resistance, particularly in non-fermenting gram-negative pathogens. These enzymes are frequently associated with multidrug-resistant organisms and may significantly compromise available therapeutic options. The detection of the genes encoding these enzymes is often based on homemade PCR. Rapid diagnostic tests based on lateral flow immunoassays (LFIAs) have been successfully used to detect CTX-M-like ESBLs and the five main carbapenemases. Here, we demonstrated that GES, PER, and VEB enzymes can be readily detected by simplex or multiplex LFIAs from Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii colonies. These assays are rapid (≤15 min), highly sensitive (105–106 CFU/mL), requiring no specialized equipment, thus offering a user-friendly alternative to molecular assays in clinical laboratories. These features align well with the World Health Organization ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid, Equipment-free, and Deliverable).

Ovaj rad proučava proces modernizacije i emancipacije Muslimanke u Bosni i Hercegovini nakon Drugoga svjetskog rata diskurzivnom analizom 28 brojeva lista Novo doba (1945. – 1946.), glasila Glavnoga odbora Muslimana. Poseban fokus stavljen je na načine na koje su medijski diskursi oblikovali novu ulogu žene u javnoj i privatnoj sferi, u skladu s ideološkim smjernicama socijalističke vlasti. Rezultati pokazuju da je Novo doba promoviralo oslobađanje žene od društvenih, kulturnih i religijskih ograničenja, ali pritom je ona predstavljana više kao objekt nego subjekt emancipacije. Obrazovanje, rad i uloga u obitelji prikazani su kao ključni instrumenti u stvaranju „novoga čovjeka” i socijalističkoga društva, dok je Muslimanka predstavljena kao saveznik socijalističkih ideja unutar projekta bratstva i jedinstva. Studija nudi novi uvid u ideološke i kulturno-političke prilike poratne Bosne i Hercegovine te otvara prostor za promišljanje o kontradikcijama između deklarativne slobode i stvarne društvene moći žene u socijalističkoj Jugoslaviji.

Introduction: The COVID-19 pandemic significantly influenced mental health worldwide. This study assessed the use of antidepressants, anxiolytics, antipsychotics, and mood stabilizers in Sarajevo Canton across three periods: before, during, and after the pandemic. Methods: The data used in this study were taken from the database of the production system of the Public Institution “Apoteke Sarajevo,” Bosnia and Herzegovina. The database contains a total of 1,764,582 records on the sale of individual drugs, i.e., 2,361,059 original packages of drugs that are the subject of this research. Prescription data for psychotropic medications in the adult population were analyzed and compared between the pre-pandemic, pandemic, and post-pandemic phases. Results: Antidepressant use increased by 2.76% during the pandemic and by an additional 3.82% afterward. Anxiolytics showed a reduction of 5.29% during the pandemic and a further 18.14% decline post-pandemic. Antipsychotic prescriptions increased by 5.26% during the pandemic but decreased by 8.65% in the following period. Mood stabilizers increased by 6.8% during the pandemic and fell by 5.57% afterward. The most commonly used drugs were escitalopram among antidepressants, lamotrigine among mood stabilizers, bromazepam and diazepam among anxiolytics, and sulpiride, haloperidol, and promazine among antipsychotics. Conclusion: The pandemic was associated with a rise in antidepressant and mood stabilizer use, likely reflecting heightened psychological stress and social isolation, whereas anxiolytics declined and antipsychotics fluctuated. These findings underline the lasting impact of the pandemic on community mental health and the need for improved preventive and therapeutic strategies.

Introduction: Work-related musculoskeletal disorders (WRMSDs) are the most common work-related illnesses and describe a wide range of degenerative and inflammatory conditions affecting peripheral nerves, blood vessels, bones,joints, muscles, tendons, and ligaments. WRMSDs are one of the most common causes of absenteeism among nurses. Studies also indicate that as many as 87.8% of nurses in Europe have reported WRMSDs. Methods: The study involved 94 nurses and technicians of both genders who were involved in the daily provision of health services to patients. The study was conducted at the Public Institution of the Sarajevo Canton Health Center,the largest primary health care institution in Bosnia and Herzegovina, which, in addition to primary care, also provides specialist and consultative services. The study was designed as a cross-sectional descriptive-analytical study that included all respondents who met the inclusion criteria. The instruments used in the study were the standardized Dutch musculoskeletal discomfort questionnaire, modified for our research, and the standardized work ability index (WAI) questionnaire. Results: The highest frequency of WRMSDs in nurses was in the neck area (79.8%), followed by the upper back (71.3%), and, in third place, the lower back (64.9%). The average value of the WAI in the sample was 34.83 ± 8.35, with a median of 36.5. Conclusion: The results of our study indicate that the average value of the WAI in the examined sample was in the “good” category. Although the work ability of most respondents was assessed as satisfactory, there is also a group ofrespondents with moderate and poor work ability, which indicates a risk of further impairment of work functionality. The findings point to the need to include the examined group in a structured ergonomic intervention program aimed at preserving and improving work ability and preventing its further decline.

While the GDPR and ePrivacy Directive mandate that consent information must be clear and accessible, most modern cookie banners remain obscured by technical jargon, vague phrasing, and frequent content overload or underload. This feasibility study investigates the impact of applying plain language (Einfache Sprache) to cookie banners within the IAB Transparency&Consent Framework (TCF). In our study, we analysed cookie banner texts from 200 websites, using AI-based mapping to categorise extracted content into standardised processing purposes. By substituting complex legal terms with simplified descriptions, we successfully demonstrated that the comprehension barrier can be lowered from a college-graduate level to a 7th-grade level. However, the effectiveness of plain language is inherently constrained by the informativeness of the original content; it cannot compensate for banners that omit legally required details. We conclude that while plain language is a vital tool for digital accessibility, it must be paired with standardised implementation guidelines to ensure that cookie banners are both readable and informative.

Physics-informed anomaly detection for industrial control systems (ICSs) is usually validated on synthetic data. How much of that performance survives on real hardware-in-the-loop (HIL) data is an open question. We re-implement PhySec-Edge, a hybrid BiLSTM/β-VAE/GAT/Kalman pipeline, as a full architecture and evaluate it on HAI 23.05. The gap is consistent across five seeds. AI-only F1 falls from 0.774 (synthetic) to 0.525 (HAI); the full hybrid falls from 0.798 to 0.512. The components diverge. PVE residual sharing transfers but weakens on real data (ΔF1 = +0.010 ± 0.008 across five seeds against +0.017 synthetic; ΔMCC = +0.013, positive in every seed). The PVE NIS decision score fails outright (raw FPR = 0.482; a correction brings it to 0.013, but detection does not recover). Under the retrospective fixed-FPR comparison, the preferred configuration is GNN+BiLSTM (F1 = 0.530 ± 0.002), outperforming full-EADE fusion (0.512 ± 0.003). From this split we derive the information-vs-decision pathway distinction, a way to reason about component-level generalization in hybrid architectures. We then set out what it means for physics-informed ICS detector design and edge deployment.

This study examined the association between students' classroom use of general ICT applications and computer and information literacy (CIL) across 35 education systems participating in ICILS 2023. Jurisdiction-specific weighted regression models were estimated using all five CIL plausible values and 75 jackknife replicate weights while adjusting for socioeconomic background, sex, and age. Jurisdiction-specific coefficients were synthesized using random-effects meta-analysis. Linear models indicated a modest positive association between classroom ICT use and CIL, but quadratic models consistently provided a better fit. The quadratic coefficient was negative in 34 of the 35 jurisdictions, yielding a pooled estimate of −5.66 (95% CI −6.58 to −4.74). Predicted CIL increased with classroom ICT use to approximately the midpoint of the international scale, after which gains diminished and CIL gradually declined. This nonlinear pattern was remarkably consistent across education systems and did not vary meaningfully by socioeconomic background. These findings suggest that the educational value of classroom ICT use depends not only on how often digital technologies are used, but also on how they are integrated into teaching and learning.

Solid-phase microextraction (SPME) has emerged as a powerful tool used for extracting analytes from complex matrices, offering high sensitivity, automation potential, and high-throughput efficiency. Despite its advantages, its performance is often limited by the selectivity and physicochemical properties of conventional coatings. This study addresses the limitations of conventional SPME coatings for monitoring environmental contaminants by introducing a nitrogen-rich covalent organic framework (COF) as an SPME extraction phase/coating. The COF-based SPME coating was implemented in a coated-blade spray (CBS) device format and was used to develop a robust analytical workflow that combined ambient mass spectrometry ionization via CBS for rapid screening, followed by LC-MS/MS for confirmation and quantification. Statistical validation using a t test showed that only 3 of 43 analytes exhibited statistically significant differences in loss during the microdesorption step of the CBS-MS/MS screening. The results demonstrate that the COF-based SPME coating facilitates simultaneous extraction of analytes with varying polarities from river samples without altering the sample matrix. The final protocol achieved limits of quantification ranging from 0.025 to 5 ng mL–1. The method was successfully applied to river samples, quantifying the target analytes and demonstrating that LC separation remains essential for confirmation. This work highlighted the effectiveness of a COF-based SPME coating as a next-generation extraction phase that simplifies rapid ambient MS screening, followed by traditional LC-MS/MS for confirmation and quantification, all within a single coated SPME device.

We study the phenomenology of the topological portal between QCD and pseudo-Nambu--Goldstone dark matter proposed in~\cite{Davighi:2024zip}. We construct a weakly coupled ultraviolet completion in which a vector mediator gauges a baryonic current of QCD for the light quark flavours, linking it to dark pions parametrizing the coset $SU(2)_D/U(1)_D$. As a concrete anomaly-free realisation, we gauge the leptophobic combination $B_1+B_2-2B_3$. This completion allows us to calculate thermal coannihilation beyond the regime of validity of the pion effective theory, including resonant mediator effects, and to test the resulting thermal target with boosted-dijet and monojet searches at the LHC, with LEP and bottomonium data, and with flavour observables. These collider signatures follow directly from the symmetry structure required by matching onto the topological portal. At low momentum transfer, the topological operator instead controls the decay of the heavier dark pion, which is long-lived. A future high-statistics $Z$ factory, such as FCC-ee, can probe the low-mass region where hadron-collider sensitivity deteriorates, providing complementary tests of the topological portal.

This paper presents a dual-layered AI framework for real-time landslide risk assessment developed under the GeoNetSee project within the Interreg Danube Region Programme. The first layer employs a fuzzy logic model, inspired by the Slovenian MASPREM system, which integrates Landslide Susceptibility Maps (LSS) with high-resolution precipitation forecasts from the Open-Meteo API to generate a Predicted Landslide Hazard (PLSH) score on a 0–5 scale, updated every 6–12 hours. The second layer focuses on real-time ground displacement detection by fusing low-cost dual-frequency GNSS receivers with MEMS accelerometers and applying machine learning regression algorithms, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM). Validation at the Curine Njive pilot site in Bosnia and Herzegovina demonstrated that SVR achieved a Mean Absolute Error (MAE) as low as 0.0018 m across four GNSS nodes, while the CNN-LSTM achieved the highest overall predictive accuracy across all evaluated nodes with a MAPE of only 3.99% for Node 41. The fuzzy model successfully identified critical hazard levels during intense rainfall events. Results confirm the viability of integrating low-cost sensor networks with AI-driven analytics for transboundary geohazard early warning systems.

Fuzzy inference systems have demonstrated considerable promise for EEG-based cognitive state monitoring in neurodegenerative conditions. However, two design decisions significantly influence system performance and clinical applicability: the choice of inference architecture (Takagi-Sugeno vs Mamdani) and the method of rule and membership function generation (manual expert-driven vs data-driven automated). This paper presents a comparative analysis of both dimensions in the context of an EEG-based Alzheimer’s disease monitoring system operating on the ds004504 OpenNeuro dataset (88 subjects: 36 AD, 23 FTD, 29 HC). A Takagi-Sugeno system, implemented as a hybrid FSM-Fuzzy architecture, is compared against a Mamdani equivalent across four axes: inference transparency, output interpretability, computational complexity, and suitability for FPGA deployment. The limitations of manual membership function tuning are then examined through failure case analysis, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) proof-of-concept is presented, demonstrating automated rule and MF generation from the delta/alpha ratio (DAR) biomarker. ANFIS training on 88-subject DAR data (70 training, 18 test) achieved minimal training RMSE of 0.392 and validation RMSE of 0.391. Data-driven MF centers differ substantially from manually designed centers — shifts of 2.754σ, 0.455σ, and 2.055σ for LOW, MEDIUM, and HIGH MFs respectively — quantifying the representational gap that expert tuning introduces. These findings motivate a hybrid design strategy: ANFIS for offline MF calibration, TS for online FPGA inference, and Mamdani for clinical output presentation.

This paper evaluates the technical, economic, and environmental feasibility of residential prosumer implementation in Sarajevo, Bosnia and Herzegovina, through the integration of photovoltaic (PV) generation and battery energy storage. A simulation‑based assessment was performed in HOMER Pro using a measured 12‑month household load profile with an average daily consumption of 11.25 kWh and a peak demand of 2.85 kW. Six system configurations were investigated, consisting of 3 kW, 4 kW, and 5 kW PV installations, each analyzed with and without a 5.04 kWh lithium‑ion battery, over a 25‑year project lifetime under a zero‑credit export scheme. Among the analyzed configurations, the 3 kW PV system without battery storage achieved the lowest Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) of 0.137 BAM/kWh, representing a 28% reduction compared to the grid tariff, with a simple payback period of 17 years and the highest Internal Rate of Return (IRR) of 3.8%. The 4 kW PV system provided the most balanced operational performance, generating 4,954 kWh annually with imports and exports nearly equal, yielding an LCOE of 0.139 BAM/kWh and a 20‑year payback. The 5 kW PV system generates highest total renewable energy and full annual carbon neutrality, but its surplus generation provided limited additional financial benefit under the current net‑metering framework, which only offsets future bills rather than generating revenue. Battery storage integration substantially enhanced energy autonomy by increasing self‑consumption and renewable fraction (from 52% to 61% for the 3 kW system, and from 57% to 68% for the 4 kW system), but it raised LCOE to 0.198–0.202 BAM/kWh due to higher capital investment. The environmental assessment demonstrated that PV adoption significantly reduces greenhouse gas emissions, with the 3 kW system lowering annual CO₂ emissions by ~76%, the 4 kW system by ~92%, and the 5 kW system achieving net‑negative emissions. The results demonstrate that under zero‑sellback regulatory frameworks, PV sizing must prioritize direct self‑consumption matching. Moderate‑capacity PV installations therefore represent the most economically viable investments, while battery storage serves primarily as a driver of energy independence rather than immediate financial gain.

The primary objective of this research is to examine the direction and strength of the impact that capital structure indicators have on the performance of banks in Bosnia and Herzegovina. Specifically, the study aims to assess the degree and nature of the interdependence between capital structure and profitability indicators within the banking sector of Bosnia and Herzegovina. The empirical analysis is based on audited annual data from commercial banks operating in the Bosnian market from 2008 to 2024. To explore these relationships, econometric panel models with fixed and random effects were employed. The findings reveal a statistically significant influence of the debt-to- equity and debt-to-asset ratios on bank profitability. Additionally, internal factors such as bank size and the cost-to-income ratio demonstrated notable significance. Beyond capital structure variables, the analysis also incorporated selected macroeconomic factors, which were found to play an important role in shaping bank profitability. The results highlight the relevance of the regulatory framework and prevailing market conditions in Bosnia and Herzegovina, as they considerably affect bank operations and their ability to generate profit.