Spiking neural networks can exhibit complex emergent dynamics, but the credibility of spatially explicit agent-based implementations depends on systematic verification and validation (V&V). This study introduces LANA (Local Adaptive Neural Agents), an agent-based spiking neural network in which neurons, propagating signals, directed synapses, and a diffusive environmental field are represented as distinct interacting components. We present a five-level V&V framework spanning operator-level tests, single-neuron mechanisms, propagation behavior, network-level dynamics, and sensitivity/robustness analysis. Across 13 predefined tests and approximately 2000 simulation runs, the model satisfied all prespecified pass criteria: synaptic delays reproduced the expected propagation law exactly, environmental decay and diffusion matched analytical expectations, threshold and refractory mechanisms behaved as predicted, inhibition suppressed firing monotonically, and environmental coupling induced a transition toward higher variability and oscillatory-like activity. Matched-seed comparisons further showed that explicit signal transport and environmental feedback substantially amplify activity relative to a neuron-only baseline while leaving synaptic delay propagation unchanged. Additional regime and lesion experiments demonstrated distinct resting, hyperexcitable, and focal-lesion states, with the lesion condition producing an acute decline followed by only partial recovery. Together, these results provide a transparent V&V baseline for LANA and illustrate how agent-based spiking models can be tested and interpreted across multiple scales.

Outcome improvement alone does not reveal whether users actually inspected disclosed evidence or simply followed a highlighted recommendation. This companion human-study paper analyzes model-selection deliberation under a staged multi-criteria disclosure interface for educational quality assurance (QA). In the final filtered analytic sample of 38 participants and 228 completed scenarios, we examine interface telemetry, participant-level self-report, acceptance, task-level heterogeneity, and a heuristic low-engagement robustness check. Nonparametric comparisons and a clustering-adjusted GEE model were used. Disclosure uptake was selective: ranking was used in 60.5% of scenarios, weights in 47.4%, heatmap in 46.9%, and textual interpretations in 41.7%. Self-reports aligned with telemetry for four of the five major components. Improved scenarios showed longer Step 2 deliberation and greater engagement with multiple evidence surfaces, while the GEE model indicated that ranking use was significantly associated with improvement. Acceptance was favorable (34/38 preferred the agent-assisted workflow), whereas a low-engagement subgroup showed sharply reduced benefit. The observed pattern is more consistent with structured multi-surface deliberation than with shallow recommendation following.

Implementing a cognitive Sense-Think-Act-Learn (STAL) architecture for automated cultural heritage visualization, this paper illustrates a multi-agent system. The system consists of three specialized agents: an Exhibition Curator Agent that applies Large Language Models (LLMs) to classify and generate exhibitions, a Conversational Guide Agent that provides interactive visitor engagement, and an Image Acquisition Agent that performs automated visual content enrichment. Evaluated on a dataset including 6,398 historical events across multiple civilizations and epochs, the system effectively automates the curation of the data by adopting a hybrid approach via deterministic classification algorithms as well as LLM-based analysis. The architecture allows adaptive learning based on administrative feedback, improving classification accuracy over time. This work provides a concrete framework to leverage generative $A I$ in cultural heritage digitization while also addressing issues related to scale, multilingual content, and domain-specific curation needs.

This paper presents the LANA Adaptive Labeling Framework (ALF) as an advanced framework for dynamic method labeling and selecting optimal data processing methods in multiple multicriteria intelligent software systems, focusing on business processes in higher education institutions (HEIs). Earlier approaches to method labeling relied on static hierarchical structures. In contrast, LANA ALF introduces adaptability through continuous learning from user feedback, automatic balancing of criteria based on historical data and current task requirements, and multidimensional labels for comprehensive method evaluation. Each query is represented with a set of labels, while neural networks evaluate the optimal method by balancing criteria such as performance, cost, reliability, and accuracy. User feedback is stored in dynamic tables (e.g., user satisfaction), automatically adapting their structure to new tasks and data types. The results demonstrate that LANA ALF enables intelligent agents to autonomously make decisions without the need for direct involvement of data science experts, thereby increasing accuracy, reliability, and user satisfaction. This framework provides a foundation for further application of ALF in various domains

Background: Deep Acute pancreatitis (AP) is an urging cause of hospitalization in the gastroenterology due to different causes and an unpredictable outcome. Known causes are grouped into four main groups: metabolic, mechanical, vascular and infectious. Objective: To determine the role of certain biochemical or radiological parameters as predictors of an involvement of other organs in AP different pathological staging and the surgical outcome in the treatment of AP. Methods: Ninety-seven AP patients hospitalized in General Hospital “Prim.dr Abdulah Nakaš” Sarajevo, in a period between 2016 and 2021 for both sexes, were divided according to the etiological factors of AP into four groups: nutritional factors, biliary concernments, alcohol and morphological changes of the pancreas. Beside laboratory tests, the imaging methods of abdomen (transabdominal ultrasound, abdominal computed tomography) used in determining morphological changes in the pancreas and other organs were analyzed in relation to parameters that predict the need for surgical outcomes. Results: AP etiological factors of patients differ significantly by gender and showed the dominance of dietary factors in female subjects (51%), followed by the presence of concernments in the biliary tract in 36% of cases, and alcohol consumption in male subjects in 28% of cases. The only variable correlated with the indicator of necessity for surgery is the existence of pleural effusion (coefficient of correlation was 0.38; risk ratio was 5.5) resulting that patients with pleural effusion have a 5.5 times higher chance of surgery indication than other patients. Conclusion: The application of simple parameters such as creatinine value with the values of amylases in serum and urine and the presence of pleural effusion confirmed by radiological imaging of the lungs opens the possibility of a simple and effective selection of patients for surgical treatment with a more severe form of AP.

Today's extensive requirements for the storage, management, and analysis of complex, dynamic, evolving, distributed, and heterogeneous data from different sources and platforms, e.g., Big data, generate enormous challenges for IT, especially database applications. That is why the demand for data reduction is increasingly coming from the world of databases, intending to reduce the costs of storing, processing, and querying Big data. There is a large number of different techniques for Big data reduction that can cause confusion and complicate this process. Because of that, the authors proposed a Big data reduction framework to structure and present both data reduction techniques and necessary components essential for a better understanding of the process. The importance and the components of the proposed framework are explained in this paper.