This paper proposes an integrated Expert Criteria Selection and Weighting (ECSW) model for expert-based criteria evaluation, selection, and weighting in multi-criteria decision-making (MCDM). The model is designed to preserve the expert-driven nature of the decision process while reducing the cognitive burden and potential inconsistency associated with pairwise comparisons and criteria ranking. It integrates four components: statistical criteria selection, assessment of the informativeness of expert ratings, determination of criteria weights based on their relative importance and rating stability, and sensitivity analysis. Candidate criteria with below-average ratings are evaluated by testing whether their expert-specific deviations from the experts’ average rating patterns are significantly below zero, using a one-sided one-sample t-test followed by the Holm–Bonferroni correction for multiple comparisons. The final weights are obtained by adjusting equal base weights according to the relative importance of each criterion and the dispersion of expert ratings. Model stability is examined using a leave-one-expert-out procedure and Pearson and Spearman correlation coefficients. The weighting procedure was preliminarily verified against results obtained using the Ordinal Priority Approach. A strong agreement was observed for both criteria weights (r = 0.956) and rankings (ρ = 0.929), with p < 0.001 in both cases. The two models also identified the same ten highest-ranked criteria, although some differences occurred in their ordering. The ECSW model is particularly suitable for decision problems involving a large number of criteria for which conventional pairwise-comparison or ranking-based procedures may be difficult to apply. A supplementary ECSW implementation tool is provided to facilitate practical application and reproducibility.

Natural zeolites may exhibit certain limitations in the adsorption of metal ions from aqueous solutions due to their heterogeneous mineral composition, variable content of exchangeable extra-framework cations, and the presence of impurities. In this study, natural clinoptilolite zeolite was modified using an ethanolic extract derived from lemon peel waste to evaluate the effect of this sustainable modification approach on Cu(II) adsorption from synthetic aqueous solutions. Batch adsorption experiments were conducted using 50 mL of Cu(II) solution at 300 rpm for 60 min. A one-factor-at-a-time (OFAT) approach was applied to investigate the effects of initial solution pH (2–6), initial Cu(II) concentration (10–100 mg/L), and adsorbent dosage (500-2500 mg) for both unmodified natural zeolite (UN-zeolite) and fruit-waste-extract-modified zeolite (FWEM-zeolite). All experiments were performed in triplicate. At pH 6, complete Cu(II) removal (100%) was achieved using UN-zeolite, whereas FWEM-zeolite reached a removal efficiency of 84.37%. At initial Cu(II) concentrations of 10 and 50 mg/L, complete removal was achieved using UN-zeolite, whereas FWEM-zeolite achieved removal efficiencies of 87.97% and 80.91%, respectively. Increasing the UN-zeolite dosage resulted in complete Cu(II) removal at 2000-2500 mg, while FWEM-zeolite reached its maximum removal efficiency of 91.9% at a dosage of 1000. Overall, modification with lemon peel waste extract did not improve Cu(II) removal compared with the unmodified natural zeolite under the investigated conditions. Nevertheless, FWEM-zeolite maintained high Cu(II) removal efficiency, particularly at elevated initial concentrations, demonstrating the potential of fruit-waste-derived extracts as sustainable modifying agents for natural mineral adsorbents

Multi-criteria decision-making methods conventionally rank alternatives by aggregating normalized criterion values into a composite score, an approach that depends on the normalization scheme, requires externally determined criterion weights, and evaluates performance in absolute terms without accounting for inter-criteria dependencies. This paper proposes machine learning-based Peer-Prediction Trees for multi-criteria decision-making, a novel method that ranks alternatives by their excess performance over data-driven peer expectations. For each criterion, a leave-one-out cross-validated decision tree predicts each alternative’s value from its performance on all other criteria; the residual between observed and predicted values extracts how much the alternative outperforms the empirical trade-off structure constraining its peers. Residuals are standardized, aligned with the preferred criterion direction, and aggregated into an Excess Performance Index using uniqueness-based weights derived endogenously from the cross-validated predictive fit. The method is validated on three real-world datasets: a numeric vehicle selection problem (22 alternatives, five criteria), a fully linguistic financial performance evaluation of Turkish listed firms (32 alternatives, four criteria), and a mixed numeric-linguistic exchange-traded fund selection problem (12 alternatives, nine criteria). The contributions of this work are fourfold: (1) the introduction of peer prediction via leave-one-out cross-validated decision trees as a multi-criteria decision-making ranking paradigm that evaluates excess performance rather than absolute scores; (2) a uniqueness-based criterion weighting scheme embedded into the method, eliminating the need for separate weighting procedures; (3) native handling of numeric, linguistic, and mixed decision matrices within a single unified framework; and (4) empirical validation across three heterogeneous real-world applications demonstrating the method’s versatility and interpretability.

Tracking the progression of the bone resorption process is important for clinical research and development of pharmaceuticals for bone-related diseases. Although fluorescence imaging is found to be one of the powerful tools for studying bone, conventional fluorescence microscopy relies on extremely high excitation powers that induce photobleaching and phototoxic effects on biological samples, preventing long-term observations. To overcome these limitations, we propose a quantum light-based approach using entangled two-photon fluorescence microscopy (ETPFM). We demonstrate ETPFM for imaging fixed demineralized unstained and Hoechst- and DAPI-stained bone sections at photon fluxes nearly 6 orders of magnitude lower than classical two-photon excitation. This quantum-correlated light preserves tissue integrity, eliminates photobleaching, and reveals microstructures with high contrast. Endogenous fluorescence is detected without exogenous dyes, and osteoclast cells are clearly resolved in the stained bone sections. ETPFM thus provides a minimally invasive, quantum-enhanced approach for long-term imaging of bone microenvironments.

We present AgentiGrid, an agentic artificial intelligence (AI) framework that integrates large language models (LLMs) intelligence and high-performance computing (HPC) to streamline and accelerate the multi-scenario power flow studies. AgentiGrid is an autonomous decision-making agent that proposes parameter modifications, invokes analyses through HPC analysis toolkit ExaGO, interprets results, and determines subsequent actions. ExaGO provides multiple power flow applications that can perform deterministic, stochastic and security constrained optimal power flow analyses. AgentiGrid provides backends to multiple LLMs (OpenAI, Anthropic, Ollama, and Ollama cloud) augmented with context specific and task specific prompts. Key features include interactive mid-search steering, goal-type-aware post-search analysis, and concurrent variant exploration for power flow optimization. A Streamlit-based graphical launcher provides real-time visualization of iteration progress and generates reports in natural language. AgentiGrid is capable of autonomously converging transmission constrained alternating current optimal power flow in under 20 iterations, with near-perfect reliability

Radio communication, as a traditional form of communication, has been somewhat overshadowed in today’s landscape of technological advancements and digital communication. However, in certain shapes and forms, radio communication plays a key role in civil protection services and protection and rescue units during emergencies. This paper will outline the structure of goals and objectives, as well as the organizational framework of radio communication within the local community, specifically focusing on the municipal civil protection headquarters, according to the legal regulations of the Federation of Bosnia and Herzegovina entity. The paper will also examine the structure of the headquarters and the role of the emergency radio network (RMZO) and amateur radio clubs within these units. The purpose of this paper is to determine the significance of amateur radio associations during emergency situations. This research aims to emphasize the importance of effectively organizing and mobilizing both human and technical resources in response to an emergency event that requires the activation of radio communication. This subject gives rise to the central issue examined in this article methods and means for establishing an emergency communication network in the case that traditional communication systems become inoperable. This article will emphasize the organization set-up, necessary technical resources, and the various methods, types, and objectives for achieving effective communication in specific situations, highlighting the importance of this topic in contemporary practice. This topic is particularly pertinent in today’s fast paced environment, where the demands of modern life can often leads to the neglect of various responsibilities, which can contribute to increased level of insecurity that may ultimately manifest in natural disasters, the occurrence of missing persons, and general emergency events that require the use of these resources.

Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances orders of magnitude faster than conventional numerical solvers. Attention mechanisms have recently been introduced into neural operators, but most studies change several architectural components at once, making it difficult to identify what actually improves accuracy. This work presents a controlled and systematic study of five deep operator network (DeepONet) variants with distinct attention mechanisms, trained under both data-driven and physics-informed regimes, to isolate the effects of cross-attention, self-attention, tokenization, and attention depth. We evaluate them on a source-driven transient one-dimensional nonlinear diffusion-reaction equation, a transient one-dimensional viscous Burgers equation with variable initial conditions, and a two-dimensional Poisson heat-conduction problem with heterogeneous source fields. Per-sensor tokenization with cross-attention reduces the mean relative L_2 error of the classical DeepONet in all benchmark-training combinations by factors of 2.4-28.0, while the best attention configurations reach 3.5-32.3. Branch self-attention paired only with dot-product fusion is inconsistent, degrading the one-dimensional problems while helping the more complex two-dimensional source field; added on top of cross-attention it improves all six cases, though by less than cross-attention fusion alone. Global pre-mixing provides no consistent benefit. Increasing cross-attention depth further improves accuracy, but with diminishing returns and a substantially higher cost under physics-informed training. Overall, query-dependent cross-attention is the most reliable mechanism, whereas branch self-attention is most useful for large, spatially complex functional inputs.

Oral epithelial dysplasia (OED) is the principal histopathological marker for malignant risk stratification in oral potentially malignant disorders (OPMDs). Current World Health Organisation (WHO) three-tier and other binary grading systems are limited by modest inter-observer reproducibility and suboptimal prognostic discrimination, particularly for architecture-predominant lesions where cytological atypia is inconspicuous. The most clinically consequential failure of current practice is not mis-grading but non-recognition of dysplasia altogether, with direct implications for patient surveillance and management. This position paper, arising from Working Group 2 of COST Action CA21140 INTERCEPTOR, synthesises evidence from inter-observer reproducibility studies, feature-based prognostic models, under-recognition studies, outcome data and computational pathology analyses to propose and contextualise a pattern-anchored, grade-retaining diagnostic framework for OED. Five recognisable histological patterns are proposed: conventional/basaloid, keratinising/differentiated, verrucous, papillomatous, and HPV-associated, functioning as diagnostic anchors that capture the full morphological spectrum of OED, including architecture-predominant phenotypes that are disproportionately under-recognised in routine practice. Feature-based prognostic models demonstrate that architectural traits characterising these patterns (bulbous rete ridges, AUROC 0.74; cohesion loss, AUROC 0.73) predict malignant transformation more effectively than grade alone. Independent computational pathology analyses identify corresponding immune microenvironment and architectural signals as the strongest prognostic variables, providing convergent support. A two-step approach i.e. first identifying the dominant morphological pattern and then assigning grade within that pattern using existing WHO or binary criteria, offers a pragmatic, grade-retaining refinement compatible with current diagnostic frameworks and clinical pathways. The proposal is hypothesis-driven and requires prospective multicentre validation before wider adoption.

Abstract DNA methylation profiling is an integral diagnostic tool in the classification of central nervous system (CNS) tumors. While the Heidelberg CNS Tumor Methylation Classifier is widely used to support CNS tumor diagnostics, new classifiers such as CrossNN are emerging. However, their clinical performance and added value within routine diagnostic workflows remain insufficiently explored. In this study, we evaluated the diagnostic performance of the CrossNN classifier in a real‐world CNS tumor cohort and compared it with the established Heidelberg classifier to assess its potential as both a non‐inferior alternative and a complementary tool to improve diagnostic accuracy. A retrospective cohort of CNS tumors profiled using Illumina Human Methylation 930k EPIC v2 BeadChip arrays was analyzed. Classifier outputs were compared with integrated WHO CNS5 (2021) diagnoses. In addition, CrossNN and Heidelberg outputs were harmonized to WHO CNS5 (2021) tumor type levels and evaluated both individually and within sequential and parallel diagnostic workflows. The proposed workflows were subsequently assessed in an independent prospective validation cohort. Among 205 samples, CrossNN correctly classified 88.8% of cases and demonstrated 86.8% concordance with the Heidelberg classifier. CrossNN demonstrated non‐inferior classification performance compared with the Heidelberg classifier. Combining both classifiers increased the number of clinically informative and correct classifications by nearly 10%. This finding was confirmed in an independent validation cohort of 41 samples. In conclusion, these results demonstrate the complementary strength of the CrossNN and Heidelberg classifiers as a dual‐classifier strategy to improve diagnostic confidence and accuracy in routine CNS tumor diagnostics.

Water pollution by heavy metal ions poses a significant environmental problem due to their toxicity, persistence, and potential for accumulation in living organisms. Nickel requires particular attention, as it can occur at elevated concentrations in galvanic industry wastewater. Therefore, the aim of this study was to investigate the efficiency of peanut shells as a natural and readily available biosorbent for the removal of Ni(II) ions from galvanic wastewater within the context of circular economy principles. As an agricultural residue, peanut shells represent a potentially economically and environmentally acceptable alternative to conventional adsorbents. The experimental study examined the influence of key process parameters on adsorption efficiency, primarily wastewater pH, initial Ni(II) concentration, and biosorbent mass. Based on the obtained results, optimal process conditions were determined and adsorption efficiency was evaluated. The adsorption equilibrium was analysed using the Freundlich and Langmuir isotherm models. The results demonstrated that peanut shells can serve as low-cost biosorbents for the efficient removal of Ni(II) ions from galvanic wastewater. For both samples, the highest removal efficiency was achieved at pH 6 and an initial Ni(II) concentration of 1000 mg L-1. The Freundlich adsorption isotherm model showed good agreement with the experimental data, with R2>0.9 for Sample 1 and R2>0.8 for Sample 2. The comparable adsorption performance of the two samples indicates that peanut-shell residue previously used can be further valorised as a biosorbent for Ni(II) removal. The multiple use of the same biomass residue contributes to waste reduction and improved resource utilisation, supporting the potential application of peanut shells within a circular economy approach.