In the context of business systems, efficient data analysis through various Online Analytical Processing (OLAP) models represents a key challenge for performance optimization and timely decision-making. This study examines tabular and multidimensional OLAP models within the SQL Server (MSSQL) and Visual Studio environments to determine which model facilitates more effective data processing in a specific business context. An experimental analysis was conducted using the Stats dataset, where the same business question was addressed through both models, comparing their response times and query execution efficiency. Particular attention was paid to execution speed, data processing methodologies, and resource optimization strategies. Results indicated that the tabular model, which relies on in-memory technology and the Data Analysis Expressions (DAX) language, reduces data processing time by approximately 38 % and offers simpler modeling capabilities, making it suitable for analyses where rapid result retrieval is critical. In contrast, the multidimensional model, utilizing Multidimensional Expressions (MDX), provides advanced analytical features and greater scalability, rendering it more appropriate for complex analyses involving large datasets and predefined aggregations. Based on this comparison, it was concluded that the choice between tabular and multidimensional OLAP models depends on specific analytical requirements. If speed and flexibility are prioritized, the tabular model enables faster execution, whereas the multidimensional model offers enhanced control over analytical processes.

Online Analytical Processing (OLAP) technology facilitates efficient multidimensional data analysis, providing users with valuable insights for decision-making processes. Previous studies have explored the implementation of OLAP technology across various domains; however, a limited number of investigations have compared the Multidimensional Analysis Project and Pentaho on the same database or within a single study. This research contributes to existing literature by evaluating the performance and flexibility of these two tools using Microsoft SQL Server as a benchmark dataset, which represents the database of a specific blog application or an application based on user interactions with diverse posts. The manuscript details the modeling and implementation processes for OLAP cubes in both systems, emphasizing fundamental aspects of OLAP technology, key functionalities, performance metrics, and adaptability characteristics. Furthermore, a comparative analysis between Microsoft's solution and Pentaho was conducted, highlighting their respective advantages and limitations within the context of data analytics.

The development of reliable Decision Support Systems (DSS) for talent identification requires a rigorous analytical framework capable of processing high-dimensional educational data. This paper presents the mathematical formulation of the machine learning pipeline utilized for classifying student potential, focusing on the algebraic structure of data representation and the optimization of predictive algorithms. We formally define the mapping of unstructured textual attributes into sparse vector spaces using One-Hot Encoding and analyze the dimensionality reduction effects. The study details the training dynamics of classification models, specifically examining the cost function minimization in Decision Trees via the Gini Impurity index and the stochastic aggregation mechanisms within Random Forest ensembles. Furthermore, to address the challenge of class imbalance, we provide a formal definition of performance metrics, including the harmonic mean of precision and recall and the arithmetic mean of indicator functions for Global Top-K Accuracy. By establishing these mathematical foundations, the paper demonstrates how formal optimization directly correlates with the discriminative power and stability of AI-driven educational assessments.

This study investigates the application of machine learning clustering techniques, specifically Dynamic Time Warping (DTW), to define typical load profiles (TLPs) for industrial facilities. Utilizing $\mathbf{1 5}$-minute smart meter data from a plastics manufacturing plant, the research analyzes total factory consumption alongside individual chiller and compressor loads. Cluster quality is assessed using the Silhouette score, Dunn index, and mean intra-cluster distance. Results indicate that while DTW effectively captures temporal shapes, industrial profiles are highly enterprise-specific and noise-intensive, resulting in fair-to-weak cluster quality. The findings suggest that primary electricity datasets and basic temporal metadata are insufficient for high-quality profiling compared to existing household models. The study concludes that integrating production-related metadata, such as work orders, is essential for improving industrial consumption forecasting and capacity planning.

As web applications have grown to become more dynamic, frontend rendering strategies have also become more central in the architectural discussions. The three main strategies are client-side, server-side, and hybrid rendering, yet empirical comparisons of them under controlled conditions are limited. Each of them has their own set of trade-offs, and this study systematically evaluates them using three functionally and visually identical frontends which are connected to a single backend. The metrics which were collected described the rendering strategies' performance, user experience, search engine optimisation and crawlability, and resource utilisation. Results show that server-side and hybrid rendering significantly improve initial load performance and search engine optimisation compared to client-side rendering, reducing First Contentful Paint by approximately 65% on average, while client-side rendering reduces server resource usage but suffers from delayed rendering and poor crawlability. Hybrid rendering achieves the best balance between performance and search engine optimisation, but with higher resource consumption. These findings highlight that rendering strategy selection should be driven by application requirements, considering crawlability, infrastructure, and performance objectives.

Photovoltaic (PV) power plants have become an important source of clean electricity. To keep them running efficiently, it is essential to detect faults and unusual behavior. However, this is not easy because data from PV power plants are noisy and strongly affected by environment, seasonal changes, and weather conditions. Reliable anomaly detection could help with preventing power losses, reducing maintenance costs, and extending the lifetime of the system. This paper presents a two step approach for detecting anomalies in PV plant data. First, K-means clustering is used to group days with similar conditions based on daily energy metrics. Then, the Local Outlier Factor (LOF) algorithm is applied within each cluster to find whether any anomalies are present. For visualization and interpretability, results are presented in a graphic form. Initial application demonstrates the framework's ability to segment seasonal data and identify extreme deviations from normal operating clusters. Although larger datasets are still needed for full validation, the method provides a foundation for season-aware monitoring.

Three-dimensional geometric morphometric methods have emerged as a pivotal tool in veterinary anatomy, taxonomy, clinical research, and studies of morphological diversity. This article summarizes the key stages, applications, clinical potential, and recommendations for data standardization in 3D morphometrics. Datasets are typically acquired using radiological modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and 3D surface scanning, each offering specific advantages and constraints contingent on the research context. Standardized landmark sets are essential in 3D morphometric studies to ensure reproducibility and comparability of results across independent investigations. Consistent use of reference landmarks enables repeatable analyses, but the number of landmarks directly influences the required sample size and statistical power. Consequently, a minimal yet balanced landmark configuration is critical. This article proposes a standardized, minimal landmark set for the skulls of horses, cattle, and sheep to enhance inter-study reproducibility and comparability. Landmark selection prioritizes anatomically distinct points to avoid excessive landmarking, which may complicate analyses or compromise interpretability. Applications of 3D morphometric methods include orthopedic surgical planning, biomechanical modeling, and assessment of congenital anomalies, providing enhanced precision in diagnostics and research. In conclusion, 3D geometric morphometric methods represent a robust analytical framework in veterinary anatomy, morphology, and clinical research. Their significance is poised to grow through integration with automated landmarking, artificial intelligence-driven analyses, and international data-sharing networks, thereby advancing scientific inquiry in novel dimensions.

Pigmentation is a key functional trait influencing camouflage, predator-prey interactions and energetic efficiency in marine organisms, yet its physiological and ecological consequences remain poorly understood in deep-sea sharks. Here, we describe a deep-sea shark (Heptranchias perlo) exhibiting a mosaic pigmentation disorder characterized by the coexistence of hypermelanotic, hypopigmented and amelanotic regions, indicating disruption of normal melanophore distribution and regulation. Histological examination revealed no structural or inflammatory abnormalities, supporting a non-pathological origin of the pigmentation anomaly. In contrast, condition indices indicated pronounced energetic depletion, with reduced condition factor and hepatosomatic index, while lipid extraction and Fourier-transform infrared and ultraviolet-visible spectroscopy revealed substantial depletion and altered composition of hepatic lipid reserves consistent with chronic negative energy balance relative to phenotypically normal conspecifics. We propose that disruption of countershading in hexanchiform sharks may reduce camouflage efficiency and increase energetic costs, contributing to the observed physiological compromise in sharks. Despite being based on a single individual, this integrative analysis links pigmentation anomalies to functional and energetic consequences, and underscores the need to move beyond descriptive accounts toward mechanistic assessments of coloration in marine predators, particularly in deep-sea elasmobranchs that are inherently rarely encountered.

In the era of Cloud Computing and High Performance Computing as a Service, multiple customers run their software and hardware on the same physical devices. This opens new opportunities for attackers, like Denial of Service (DoS) attacks on shared cloud Field Programmable Gate Arrays (FP-GAs). DoS attacks work by employing a malicious hardware design as a tenant on a shared FP-GA device, draining the FPGA's power budget. This paper aims at identifying potentially malicious circuits before they are deployed to the FPGA, using formal verification. Our methodology, PowerCourt, provides an accurate worst-case power estimation based on switching activity. It computes metadata about the design that enables service providers to classify designs as malicious. The method checks formal properties that verify whether a design can exceed a custom threshold and whether this threshold can be exceeded repeatedly. We demonstrate the effectiveness of our approach using various hardware designs. For example, for a trojan that enables excessive switching, we show that we can detect it offline and disable it during runtime.

The thermal stability and structural evolution of a GaN high-electron mobility transistor (HEMT) heterostructure grown on a Si (111) substrate were investigated using in situ high-temperature x-ray diffraction, reciprocal space mapping (RSM), Raman spectroscopy, and rocking-curve (RC) analysis at varying temperatures. The heterostructure, consisting of a p-GaN cap, an AlGaN barrier, and a GaN channel supported by two AlGaN/AlGaN superlattice buffer layers, maintained clear and periodic satellite peaks up to a temperature of 1000 °C, confirming structural integrity. Symmetric and asymmetric RSM results reveal that both the Si and GaN diffraction peaks shift with increasing temperature, consistent with thermal expansion, and show no significant broadening or relaxation throughout the heating process. The c-lattice constant follows the theoretical expansion predicted by the multi-frequency Einstein model, whereas the a-lattice expansion is slower due to in-plane strain constraints imposed by the underlying Si substrate and buffer layers. Irreversible strain relaxation and thermal mismatch-induced stress redistribution induce a residual compressive strain of roughly 0.3% in the GaN channel after cooling, which has been further confirmed in Raman spectra through a blue shift (∼1 cm−1) of the GaN E2 (high) phonon mode, corresponding to an in-plane strain of −0.15% ± 0.16%. RC analysis revealed an increase in both screw and edge dislocation densities of 28% and 12%, respectively. These results collectively demonstrate that the GaN HEMT heterostructure exhibits robust crystalline stability up to 1000 °C without cracking due to strain relaxation, with only minor strain redistribution and limited dislocation activity, providing experimental evidence for GaN devices' applications under high-temperature conditions.

Background/Objectives: Spontaneous intracerebral hemorrhage (sICH) is a particularly severe subtype of stroke, characterized by high rates of mortality and long-term disability, for which robust prognostic markers are still lacking. The aim of this study was to assess the relationship of the ICH score, the National Institutes of Health Stroke Scale (NIHSS) score, and serum high-sensitivity cardiac troponin I (hs-cTnI) levels with 30-day mortality in patients with sICH. Methods: We conducted a prospective observational cohort study enrolling 100 consecutive patients diagnosed with sICH based on neuroimaging findings. Demographic data, clinical parameters, neuroimaging findings, and serum hs-cTnI levels were collected on admission. Subsequently, the ICH score, its individual components, and the NIHSS score were assessed. Results: Patients who died were older and had significantly higher ICH and NIHSS scores, lower Glasgow Coma Scale (GCS) scores, larger hematoma volumes, more frequent intraventricular hemorrhage (IVH), and elevated hs-cTnI levels compared to survivors. Serum hs-cTnI concentrations were significantly correlated with ICH and NIHSS scores, lower GCS scores, larger hematoma volumes, and the presence of IVH. On univariate logistic regression, higher ICH score, NIHSS score, and hs-cTnI level were associated with mortality, whereas multivariate analysis identified the GCS score, hematoma volume, and IVH score as significant independent factors related to fatal outcome. Conclusions: Individual components of the ICH score may provide useful information on outcomes in patients with sICH. Higher serum hs-cTnI levels were associated with 30-day mortality but were not independent predictors. These markers may assist in patient monitoring and support established clinical procedures in therapeutic decision-making. Nevertheless, larger multicenter studies are needed to further clarify their clinical implications in sICH management.

The rapid growth of the global population has increased the consumption of chicken eggs, leading to the generation of significant quantities of eggshell waste. The sustainable valorization of this biowaste represents an important environmental and resource management challenge. In this study, CaO was synthesized from waste chicken eggshells via calcination at 800 °C and evaluated as a green precipitating agent for the removal of toxic Pb(II) from aqueous solutions. The effects of key precipitation parameters, including initial pH, stirring speed, contact time, and CaO dosage, were systematically investigated. The results showed that the removal efficiency increased with increasing pH, mixing intensity, contact time, and CaO dosage, reaching a maximum Pb(II) removal of 90% under investigated conditions of initial pH 9, stirring speed of 500 rpm, contact time of 15 min, and CaO dosage of 500 mg. In the presence of the competing ion Fe(III), the removal efficiency further increased to 99.99%, indicating a potential synergistic effect in the precipitation process. FT-IR analysis confirmed the successful formation of CaO and revealed significant spectral changes after Pb(II) precipitation, including shifts and disappearance of characteristic absorption bands, indicating the formation of insoluble hydroxide and carbonate phases. These findings demonstrate that eggshell-derived CaO is an effective and environmentally sustainable material for Pb(II) removal from aqueous media and represents a promising approach for the valorization of eggshell waste.

Olaparib, the first clinically approved poly (ADP-ribose) polymerase (PARP) inhibitor, may be repurposed for non-oncological conditions such as acute respiratory distress syndrome (ARDS), where PARP-1 inhibition has shown benefits in preclinical models. We investigated the expression and functional status of PARP-1 and the effects of olaparib in peripheral blood mononuclear cells (PBMCs) from ARDS patients and healthy controls. PBMCs from healthy volunteers (N = 8) and ARDS patients (N = 8) were isolated via Ficoll gradient. PARP-1, cleaved PARP (cPARP), and PAR polymers were assessed by Western blotting. Cytokine production was measured in plasma and in PBMC supernatants after 1 h preincubation with olaparib (10 µM) or vehicle, followed by LPS (100 ng/ml) stimulation for 4 h. Cellular bioenergetics were analyzed using Seahorse XFe24 after H2O2 (100 µM, 2 h) with or without olaparib pretreatment. Control PBMCs showed a lymphocyte-predominant population with mostly full-length PARP-1. In ARDS Day 1 samples, PARylated proteins increased and PARP became downregulated. By Day 8, PARylation decreased and full-length PARP-1, as well as cleaved PARP-1 were detectable. Olaparib treatment of the cells did not alter the LPS-induced cytokine responses. Exposure of healthy PBMCs to oxidative stress suppressed cellular bioenergetics, and this effect was attenuated by olaparib. However, in ARDS PBMCs, which were already bioenergetically suppressed, oxidative stress had no further effect, and olaparib was without protective effect. Thus, in PBMCs isolated from ARDS, olaparib’s cytoprotective effect is no longer detectable, likely due to PARP-1 inactivation and degradation. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-026-03623-4.

PURPOSE OF REVIEW The degree to which computerized methods, such as artificial intelligence (AI), will aid in the assessment of kidney histopathology is undergoing intense study and application; and this is particularly true for interstitial fibrosis, which is often used as a surrogate measure of chronic kidney disease progression, since interobserver variability among human pathologists has been demonstrated in the assessment of interstitial fibrosis and other features. RECENT FINDINGS Computerized assessment of interstitial fibrosis, including with AI, has been assessed alongside pathologists. Computerized methods such as AI have shown direct interstitial fibrosis measurement and indirect assessment through kidney compartment segmentation; however, some studies have shown lack of complete concordance among computerized methods and humans; and studies have still shown the persistent value of human assessment in many circumstances. SUMMARY Computerized methods, including AI, are showing increased application in kidney pathology for a wide variety of clinical and histopathologic parameter assessment, including interstitial fibrosis; however, further studies are needed to characterize the performance of AI and handcrafted methods; and additional work is needed to fully integrate computerized methods into routine pathology practice. Ultimately, humans working with AI ("humans + AI") may provide enhanced analysis for more effective patient care.

Robots increasingly provide explanations to support transparency in Human-Robot Interaction (HRI), yet users differ widely in how much explanation they prefer and when it is appropriate. We present a lightweight simulation framework in which a robot selects among explanation policies ranging from no explanation to norm-based, preference-based, and a Bayesian Adaptive (BA) policy that learns user preferences online while respecting normative expectations. Using synthetic user archetypes, we evaluate how these policies trade off utility, alignment, explanation cost, and regret. Results show that BA consistently achieves low regret across individual users while maintaining strong utility and alignment across diverse user archetypes. These findings motivate the development of preference-aware, uncertainty-driven explanation mechanisms for robust, adaptive robot communication in heterogeneous HRI settings.