Search Results (1,223)

Biclustering is a two-dimensional data analysis paradigm that aims to identify subsets of rows and columns in a data matrix whose intersection forms a submatrix satisfying predefined properties. In the case of binary data, a bicluster is typically defined as a submatrix containing exclusively ones or exclusively zeros. Numerous approaches to binary biclustering have been proposed over the last several decades. Among them, a distinctive line of research relies on Boolean reasoning, transforming the biclustering task into the problem of finding prime implicants of a data-dependent Boolean function. From a theoretical perspective, it has been shown that every prime implicant of the data-dependent Boolean function corresponds to an inclusion–maximal bicluster in the original dataset, and vice versa. Here, inclusion maximality means that no additional row or column can be added without violating the bicluster property. For discrete datasets, the corresponding Boolean function can be represented in Conjunctive Normal Form (CNF), where clauses contain up to three variables, reducing the general biclustering problem to an instance of the 3-SAT problem. Furthermore, the computational complexity of discrete-data biclustering can be reduced to the analysis of binary matrices. In this case, the associated Boolean functions consist of clauses containing at most two literals, yielding monotone 2-CNF formulas. This paper focuses on improving the efficiency of computations required to derive a disjunctive normal form (DNF) representation composed exclusively of prime implicants. Since the extraction of prime implicants constitutes the computational core of the Boolean biclustering framework, accelerating this process directly enhances the scalability and practical applicability of biclustering methods based on Boolean reasoning. Full article

This study focuses on accelerating adaptive beamforming in the Multicarrier Wireless Internet Local Loop (McWiLL) professional radio communication system using “Korona” ring smart antennas. The work investigates algorithms for calculating complex weight coefficients in an eight-element uniform circular antenna array. The main objective is to reduce beam pattern adaptation time while maintaining interference suppression depth and robustness under multipath propagation. To achieve this, an ensemble machine learning approach based on the Random Forest algorithm is employed to approximate the optimal Minimum Variance Distortionless Response (MVDR) solution using elements of the sample covariance matrix of received signals. The training dataset is generated through McWiLL channel simulations considering mutual coupling between array elements, signal-to-noise ratio (SNR) variation, and different angles of arrival of the desired and interfering signals. The proposed method is evaluated against the classical MVDR algorithm in terms of radiation pattern null depth, robustness to phase distortions, and inference time on a Field-Programmable Gate Array (FPGA) hardware platform. Results demonstrate that the Random Forest-based approach achieves more than a fourfold reduction in computation time while forming radiation-pattern nulls of about 30–35 dB toward the interferers (versus 44–46 dB for the classical MVDR); the synthesized core uses no hardware multipliers (DSP48), and its functional equivalence to the software model is confirmed by bit-exact RTL co-simulation. The findings show promise for deployment in McWiLL base stations and other professional radio systems requiring fast, adaptive beamforming under dynamic channel conditions. Full article

Cannabidiol (CBD), the principal non-psychoactive phytocannabinoid of Cannabis sativa, exhibits diverse pharmacological activities through interactions with multiple molecular targets. Thus, breast, colorectal, and lung cancers arise from distinct molecular mechanisms. This study investigated the potential multi-target pharmacological mechanisms of CBD using an integrated approach combining network pharmacology and molecular docking. CBD-associated targets from three prediction platforms were intersected with disease-associated genes for each cancer type, yielding 143 overlapping targets that formed a significantly enriched protein–protein interaction network. Maximal Clique Centrality (MCC) analysis identified 10 hub proteins, including SRC, SIRT1, PTGS2 (COX-2), PPARG, NFKB1, MMP2, IGF1R, ESR2, ESR1, and EGFR, which represent key regulators of hormone signaling, inflammation, cell proliferation, and tumor progression. Molecular docking against these targets, benchmarked using each protein’s authentic co-crystallized ligand, predicted predominantly moderate binding affinities for CBD. Compared with the corresponding reference ligands, CBD generally exhibited lower predicted binding affinity, although comparable or slightly stronger scores were observed for PTGS2, ESR2, and EGFR. Independent validation using AutoDock Vina demonstrated overall agreement with the MOE docking results, supporting the robustness of the predicted binding profiles. Collectively, these findings suggest that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target. By integrating pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking, this study provides a computational framework for prioritizing biologically relevant CBD targets for future experimental validation. These findings should be regarded as hypothesis-generating rather than evidence of clinical efficacy. Full article

Rapid prediction of vehicle lift coefficient is an important issue in aerodynamic design. Traditional CFD/DSMC methods have high computational costs. Although commonly used machine-learning surrogate models offer high prediction efficiency, they struggle to provide explicit mathematical expressions. To balance prediction accuracy and model interpretability, this paper introduces the symbolic regression method and establishes an explicit modeling process for the lift coefficient. Using Mach number, angle of attack, and related flow parameters as inputs, validation is conducted on two-dimensional blunt body DSMC data and three-dimensional missile aerodynamic data, with comparisons against linear regression, quadratic polynomial regression, Kriging, random forest, XGBoost, and multilayer perceptron. The results show that symbolic regression can obtain high-precision explicit expressions on the two-dimensional blunt body data and can also build analytical models with certain predictive capability on the three-dimensional missile data with limited samples. Compared with traditional explicit regression methods, symbolic regression does not require a pre-specified fixed functional form; compared with black-box models, its advantage lies in providing interpretable and editable algebraic expressions. The findings indicate that symbolic regression has application potential in rapid explicit modeling of the lift coefficient. Full article

Carried out with 30 students enrolled in the Computer Technologies Department of a vocational school at a public university in Türkiye, this qualitative study examines university students’ perceptions and institutional expectations regarding the integration of sustainable artificial intelligence (AI) in universities. Data were collected through semi-structured interviews. The interview protocol was developed by the researchers and refined based on feedback from three field experts to ensure content validity. Before the main data collection, the interview form was reviewed and refined through an initial exploratory application with a small group of participants to assess the clarity and comprehensibility of the questions and to identify potential issues in the data collection process. The data analysis was conducted using qualitative content analysis supported by MAXQDA software. The findings indicate that students generally associate sustainable AI with energy efficiency, environmental responsibility, and ethical use of technology. Participants demonstrated awareness of large-scale AI systems’ environmental impacts. Although students viewed the development of environmentally friendly AI systems positively, they expressed concerns about ethical boundaries, excessive energy consumption, and the absence of clear regulatory frameworks. Overall, the findings suggest that structured content on the environmental, ethical, and social dimensions of AI should be integrated into vocational higher education curricula. Full article

Foam concrete is a lightweight material characterized by low density and moderate mechanical strength, which can be combined with aluminum alloys to form a novel type of column. Such composite members enable rapid assembly, disassembly, and functional reconfiguration in prefabricated structures. However, research on the axial compressive performance of this new column system remains limited. This study investigates the axial behavior of aluminum alloy-foam concrete short columns through a combination of numerical simulation, theoretical analysis, and machine learning prediction enhanced by the SHAP (SHapley Additive exPlanations) interpretability method. A three-dimensional finite element model was developed in ABAQUS to examine the effects of frame thickness, foam concrete strength, and section dimension on load-bearing capacity. The results indicate that the column sectional dimensions have a significant influence on the axial compressive capacity. The foam concrete strength and frame thickness have relatively smaller effects. In addition, the frame thickness can effectively restrain lateral deformation and delay buckling. Based on the confinement mechanism, polynomial fitting, Mander’s model, and a composite column formulation were proposed for axial capacity prediction. Furthermore, eleven machine learning models were trained on 64 simulation datasets 64 independent computational experiments, among which the Gradient Boosting Decision Tree (GBDT) demonstrated the best performance (R² = 0.9984, MAE = 5.97, RMSE = 7.51). SHAP analysis further revealed the relative contributions of key features, showing that section dimension is the most influential parameter, followed by foam concrete strength, while frame thickness contributes the least. These findings not only enhance the theoretical understanding of the load-transfer mechanism of columns but also provide reliable predictive models and analytical formulations for their application in lightweight prefabricated structures. Full article

As cities accelerate climate adaptation and decarbonisation, municipally owned companies (MOCs) play an important role in delivering sustainable urban infrastructure, accessing transition finance, and supporting Positive Energy Districts (PEDs). However, the expansion of sustainability reporting requirements under the European Union’s Corporate Sustainability Reporting Directive (CSRD) raises questions about the preparedness of public infrastructure providers to meet evolving sustainability-information demands. This study examines ESG reporting readiness among Polish MOCs, focusing on current reporting activity, reporting intentions, regulatory awareness, indirect ESG information pressures, sustainable-finance and investment plans, and potential transition-risk exposure. The analysis is based on a Computer-Assisted Web Interviewing survey of 226 municipal enterprises conducted in August 2025. The results indicate substantial reporting gaps. Only 8% of surveyed MOCs had already prepared or planned to prepare a non-financial report. When companies planning EU Taxonomy reporting only were also included, 14% of the sample had some form of current or planned reporting, while the remaining 86% had neither current nor planned non-financial or EU Taxonomy reporting. Reporting readiness was lower among smaller companies, while indirect ESG information pressures arising from business relationships, stakeholder requests, and financing plans were also evident across the surveyed sample. The findings suggest that limited reporting preparedness, when combined with external sustainability-information demands, may contribute to potential transition-risk exposure relevant to municipal investment and financing processes. More broadly, ESG reporting readiness represents an organisational capability relevant to sustainable finance, municipal climate investment, and the development of PEDs and smart cities. Full article

Background/Objectives: Propolis is a resinous natural product rich in phenolic acids and flavonoids, recognized in ethnopharmacology for its antioxidant and anti-inflammatory properties. This study aimed to identify the most bioactive Saudi propolis extract using a bioassay-guided strategy and investigate its chemical profile and mechanistic anti-inflammatory potential. Methods: Four propolis extracts (P1–P4) collected from different regions of Saudi Arabia were evaluated for their antioxidant (DPPH and ABTS) and anti-inflammatory (COX-1 and COX-2) activities. The most active extract was profiled using liquid chromatography–mass spectrometry (LC–MS), followed by molecular docking and molecular dynamics simulations. Results: Among all samples, P3 demonstrated the strongest antioxidant activity, with IC₅₀ values of 25.84 ± 0.96 µg/mL (DPPH) and 32.30 ± 1.20 µg/mL (ABTS), comparable to ascorbic acid (27.45 ± 1.42 and 21.22 ± 0.79, respectively). P3 also exhibited potent and selective COX-2 inhibition with an IC₅₀ of 6.19 ± 0.21 µg/mL (relative to celecoxib, IC₅₀ 0.681 ± 0.02 as positive control). LC–MS analysis identified 26 secondary metabolites. Computational studies ranked kaempferol as forming the most conformationally stable COX-2 complex among the ligands examined, including the reference inhibitor, on the basis of molecular dynamics descriptors of pose persistence and conformational confinement. Conclusions: Saudi propolis P3 is a potent source of bioactive compounds with strong antioxidant and selective COX-2 inhibitory activity. These findings highlight its anti-inflammatory potential and suggest its value as a natural lead for developing safer therapeutics targeting inflammation-related chronic diseases. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

Proto-AGI describes the intermediate stage of artificial intelligence between narrow task-specific tools and fully autonomous general intelligence. This paper presents the first formal operationalisation of Proto-AGI traits within a cross-domain digital workflow for structural engineering and demonstrates it through a four-domain computational ecosystem applied to seismic vulnerability assessment. Viktor.ai processes cone penetration test data to stratify a three-layer soil profile, identifying a compressible intermediate stratum at 5 to 12 m depth with amplification characteristics in the 0.3 to 0.7 second period range, based on the depth and stiffness contrast of the weak layer rather than a formal site response analysis. The Fayaz RotD script computes orientation-independent RotD50 and RotD100 response spectra for two contrasting ground motion records: the near-fault Northridge record (RSN 1086, Mw 6.69) delivers RotD50 = 2.00 g and RotD100 = 2.79 g at the structural natural period of 0.41 s, a 39.6% directional uplift; the moderate-distance Kobe record (RSN 1107, Mw 6.9) delivers RotD50 = 0.591 g and RotD100 = 0.795 g at the same period. OpenSeesPy nonlinear dynamic analysis of a five-storey reinforced concrete frame produces peak inter-storey drifts of 0.45% and 0.34% under the two records respectively, both within the FEMA 356 Immediate Occupancy threshold of 1.0%. A 3.4-fold spectral demand difference produces only a 1.32-fold drift difference, reflecting the combined effects of frequency content, pulse characteristics, duration and nonlinear structural response under the two contrasting records. The Viktor.ai RC Section Analyzer yields a curvature ductility factor of 2.3 under ACI 318-25, identifying deformation capacity as the governing constraint under more severe future demands. These four findings form a causal chain connecting site conditions, spectral demand, structural response and sectional capacity that no single domain produces independently—the emergent ecosystem intelligence that defines Proto-AGI in structural engineering practice. Full article

Background: Gestational diabetes mellitus (GDM) is a major opportunity for type 2 diabetes mellitus (T2DM) prevention, yet gaps remain in the transition from postpartum care to long-term diabetes prevention despite clear management guidelines. This study evaluated awareness of future T2DM risk among parous women in Riyadh, Saudi Arabia, comparing women with and without a self-reported history of GDM, and identified sociodemographic and clinical determinants of risk awareness and adherence to follow-up and preventive measures. Methods: This observational, questionnaire-based cross-sectional survey included 316 parous women in Riyadh (n = 88 with self-reported prior GDM; n = 228 without), recruited non-probabilistically via a self-administered Google Forms questionnaire distributed through WhatsApp. GDM status, risk awareness, sociodemographic parameters, and postpartum screening and lifestyle-modification outcomes were all self-reported and not verified against medical records. Full awareness was defined a priori as selecting the highest of three ordered response options (“Yes, I am fully aware”) on a single item assessing whether GDM increases future T2DM risk; the remaining two options were combined as low/moderate awareness. Analyses used Pearson’s Chi-square tests and multivariable logistic regression to compute adjusted Odds Ratios (aOR). The primary between-group comparison was adjusted for age and educational attainment, with equivalence assessed by two one-sided tests (TOST) and subgroup differences by likelihood-ratio interaction tests. Results: A prior self-reported history of GDM was not associated with significantly elevated long-term risk awareness when compared with parous women without self-reported prior GDM (71.6% vs. 66.2% full awareness; p = 0.361, phi coefficient, φ = 0.06); this remained the case after adjustment for current age and education (aOR = 1.02, 95% CI 0.57–1.82, p = 0.936), and formal equivalence could not be established at a ±10 percentage-point margin (TOST p = 0.209). Older current age (≥45 years) was associated with higher awareness (78.0% vs. 60.3%; OR = 2.34, 95% CI 1.41–3.88, p = 0.0009). Within the GDM subset, structured postpartum counseling was associated with higher awareness levels (89.5% for scheduled appointments vs. 50.0% for no counseling; p = 0.002). In the whole sample of 316 parous women, multivariable analysis identified high awareness as the strongest independent correlate of self-reported adherence to postpartum glucose screening (aOR = 3.57, 95% CI 2.04–6.23, p < 0.001), with prior GDM diagnosis also independently associated with screening (aOR = 2.18, 95% CI 1.27–3.74, p = 0.005). Because postpartum glucose screening is specifically indicated only after GDM, a corresponding analysis was restricted to the 88 women with self-reported prior GDM, in whom 66.7% of those with full awareness reported having been screened compared with 16.0% of those with low/moderate awareness (OR = 10.50, p < 0.001). Conclusions: A prior history of GDM did not translate into higher self-reported awareness of future T2DM risk. Within the prior-GDM subgroup, structured postpartum counselling was associated with higher risk awareness, which was in turn associated with greater self-reported adherence to postpartum screening. Given the cross-sectional design and self-reported exposures and outcomes, these findings represent associations only and cannot establish causality or direction of effect. Full article

Background: Q&A corpora generated through university student–AI mental health support tools may reveal how physical activity (PA) and psychosocial themes are represented in support-oriented text. However, the absence of individual-level demographic metadata and the pooling of prompt and response fields limit attribution of any expression to a particular speaker, and the corpus describes a specific student population rather than a general or clinical one. Objective: This exploratory study described PA-, sport-, physical education (PE)-, body-, lifestyle-, and emotion-related patterns in a large corpus of university student–AI mental health exchanges collected through an institutional counselling platform. Methods: This study analysed 209,715 paired prompt–response records as combined exchange-level units using a BERTopic-based computational text-mining workflow. The full corpus was used for the main 18-topic model and overlapping dictionary analyses. After secondary data-quality filtering, 178,062 eligible exchanges formed the sampling frame from which a systematic sample of 10,000 exchanges was drawn for a separate complementary BERTopic and scenario-mapping analysis. The workflow used Qdrant/bge-small-zh-v1.5 embeddings, NFKC normalisation, an archived stop-word list, UMAP (n_neighbors = 15, n_components = 5, min_dist = 0.0, cosine metric, seed = 42), HDBSCAN (min_cluster_size = 300, min_samples = 10, Euclidean metric, EOM), c-TF-IDF topic representations, overlapping dictionary screens, and stability testing across seeds 42, 52, and 62. Results: A student/school/family-context lexical screen matched 83,215 exchanges (39.68%), and a broad PA/body/lifestyle screen matched 82,464 exchanges (39.32%). These overlapping indicators describe topical co-occurrence and do not establish PA behaviour or which party to the exchange produced a given term. Eighteen corpus-level themes were retained. In the 10,000-exchange analysis, 13.11% of exchanges matched a narrow movement-related expression screen, with the highest within-topic rate in the sample topic labelled emotional outburst and relaxation regulation (51.09%). Conclusions: The findings describe exchange-level lexical and topic patterns in student–AI interactions rather than actual PA behaviour, intervention delivery, clinical efficacy, or population prevalence, and they do not identify which party introduced the language. The mapping to autonomy, competence, relatedness, and emotional regulation is a post hoc interpretive lens, offered as a hypothesis to inform future, prospectively validated design work in PE and digital mental health support rather than as a demonstrated result. Full article

Ground-motion intensity measures (IMs) are key parameters for seismic hazard and risk assessment. However, seismic hazard characterization and code-based design spectra commonly rely on a limited set of parameters, particularly peak ground acceleration (PGA), spectral acceleration, and control periods defining spectral shape. Such representations may not fully capture seismic input relevant to structural response, soil deformation, slope instability, and indirect environmental impacts. This study analyzes 220 horizontal accelerogram components recorded during the Vrancea earthquakes of 4 March 1977, 30 August 1986, 30 May 1990, and 31 May 1990. Twenty-three IMs were computed, covering peak and effective amplitudes, velocity-related measures, cumulative and energy-based indicators, spectral intensities, duration, cyclicity, and impulsivity, together with a set of frequency content-related parameters. Pearson and Spearman correlations were evaluated using both the geometric mean and the maximum of the two horizontal components. Hierarchical clustering, PGA-centered correlation profiles, event-specific comparisons, and spatial representations were used to assess redundancy, complementarity, and relationship stability. Results show that amplitude-, velocity-, and spectrum-related IMs form strongly correlated groups, whereas duration, cyclicity, and impulsivity remain more distinct. Spatial comparisons also show that different IMs may produce different station rankings and regional patterns for the same event. These findings support selecting complementary IM families for more comprehensive, risk-informed structural and geotechnical applications. Full article

In this paper, a Hermite–Hadamard-type inequality for interval-valued functions is investigated within the framework of conformable fractional calculus. First, new results are established for $cr$-convex interval-valued functions by using the conformable fractional integral operator. Through appropriate choices of parameters, the obtained inequality covers and generalizes many known Hermite–Hadamard-type results. To illustrate the theoretical findings, an explicit numerical example is presented together with graphical representations. In addition, an artificial neural network (ANN) model is implemented as a computational approximation tool to investigate the center-radius forms of the left, middle, and right terms of the inequality across a broad parameter domain. The numerical outputs demonstrate the high predictive accuracy of the trained ANN model in approximating the analytical expressions. Full article

Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer’s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer’s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration. Full article