Abstract: We present AortaSeg-60, an open dataset of 60 real-world thoraco-abdominal CT-angiography scans of the aorta encompassing normal anatomy and pathological variations, designed for AI research, benchmarking, and educational purposes. The dataset is organized into six balanced categories: young normal, elderly normal, aortic aneurysms, aortic dissections, venous acquisition, and non-contrast acquisition, capturing realistic anatomical and pathological diversity. All scans are provided in NIfTI format with fully automated aortic segmentation masks generated using TotalSegmentator, without manual correction, enabling evaluation of typical algorithmic errors and testing of refinement strategies. Two radiologists performed a technical validation to ensure dataset curation and correct category assignment. AortaSeg-60 is publicly available on Zenodo under a CC0 license. By providing paired imaging and automated labels, the dataset facilitates reproducible research, algorithm development, and method comparison for vascular segmentation, while noting limitations of sample size, single-centre acquisition, and reliance on automated annotations.

Abstract: Soccer performance depends on multiple interacting factors, including physical, technical, tactical, and psychological components. Among the psychological factors associated with optimal performance are athletes’ emotional states, their regulation, and executive functions. These processes support attention to relevant external stimuli and enable players to plan, adapt, and regulate their behavior during gameplay. Although executive functions and emotional states have been widely studied in sport settings, research examining the relationship between these variables in athletes is limited, particularly in female soccer players. The aim of this study was to explore the relationship between emotional states, emotional regulation, and performance on cognitive tasks in female players from the Mexican soccer league. Twenty-eight players participated in two individual assessment sessions in which anxiety and depression levels, emotional regulation, and executive functions—planning, inhibitory control, working memory, and cognitive flexibility—were evaluated using psychological and neuropsychological tests. Results indicated a relationship between aspects of decision-making and players’ emotional regulation abilities, as well as between depression levels and onset latency in a working memory task. These findings support the existence of an association between emotional processes and cognitive functioning in female soccer players.

Abstract: Objectives: The Satisfaction With Life Scale (SWLS) generally operates as unidimensional but demonstrates invariance issues. This study aimed to examine the construct validity and stability of various SWLS across age and gender groups. Methods: Employing a convenience sample of community-dwelling European adults (N = 7531, median age = 26 (22-28) years, 51.1% females), this instrumental study investigated the structure and stability of SWLS through exploratory/confirmatory factor analysis (EFA/CFA) and multigroup CFA in SPSS and JASP. Results: EFA in 30% of the sample (n = 2246, KMO (0.86), Bartlett’s test of sphericity χ² (10) = 4561.84, p = 0.001) revealed a single factor with an eigenvalue of 3.12, which explained 62.35% of the variance. The unidimensional and two bidimensional structures (present/past life satisfaction; achievement/acceptance) expressed excellent fit (χ² (4-5) = 92.60-106.14, ps = 0.001; all CFIs = 0.994, ; TLI = 0.985-0.987, ; RMSEA = 0.052-0.056, ; SRMR = 0.013-0.014). Bifactor and second-order structures based on both two factor-structures did not converge. The three structures were invariant at the configural metric, scalar, and strict levels across age (<26, ≥26 years) while only the unidimensional SWLS was invariant at all levels across genders. Achievement/acceptance SWLS converged only in males while present/past life satisfaction converged only in females—the fit of both models was excellent, and the fit of the latter slightly improved when the errors of items 2 and 4 correlated. Conclusions: The findings support the use of the SWLS as a single-factor instrument for comparative purposes. SWLS components (cognitive or experiential) are interpreted uniformly among different age groups while gender-specific convergence patterns suggest meaningful gender-related nuances in its dimensional expression—males and females differently conceptualize SWLS components. Research should explore theoretical mechanisms underlying differential structuring of life satisfaction and examine whether these gender-specific dimensional patterns replicate across cultures and longitudinal designs.

Abstract: The Aral Sea crisis has severely impacted water resources in the Republic of Karakalpakstan, making groundwater a critical alternative source for drinking and irrigation. This study presents a hydroecological assessment of brackish groundwater in the Karauzyak district based on field investigations conducted in 2025. Results showed that groundwater mineralization ranges from 2.1 to 4.8 g/L (predominantly 2.2–3.8 g/L), classifying the water as brackish to highly brackish. The dominant hydrochemical type is sodium-chloride and mixed sodium-sulfate-chloride. Most samples exhibited pH values of 7.1–8.3, moderate to high hardness (6.5–26.5 mg-eq/L), and elevated sulfate and chloride levels. Concentrations of toxic microelements (Pb, Cd, As, Hg, etc.) remained below maximum permissible limits. However, the overall salinity significantly restricts direct use for drinking water supply and limits agricultural application without additional management. Piper diagram analysis revealed distinct hydrochemical facies, reflecting the influence of natural salinization processes, irrigation seepage, and evaporative concentration under arid conditions. The findings highlight both the potential and limitations of local groundwater resources and underscore the need for desalination technologies, improved drainage, and continuous monitoring to ensure sustainable use in the Aral Sea region.

Abstract: Introduction: Obesity is a chronic, multifactorial disease associated with significant metabolic and cardiovascular complications. Glucagon-like peptide-1 receptor ago-nists (GLP-1RAs) have emerged as effective pharmacological options for weight man-agement, demonstrating clinically relevant weight loss in controlled trials. However, real-world evidence is essential to assess their effectiveness and safety under routine clinical conditions and to verify if trial results are reproducible in diverse populations. Objective: To evaluate the effectiveness and safety of GLP-1RAs in terms of weight loss in real-world clinical practice and to compare outcomes among different available agents, focusing on their impact on obesity management. Method: A cross-sectional, observational pilot study was conducted in Spain. Adult patients receiving GLP-1RAs for at least four weeks were included. Data collected included sociodemographic vari-ables, treatment characteristics, anthropometric measurements, and adverse effects. Weight loss outcomes were analyzed using descriptive statistics, ANOVA for in-ter-drug comparisons, and multivariate ANCOVA to adjust for confounders. This pilot study also validated the protocol for a subsequent nationwide multicenter study. Re-sults: A total of 32 patients (62.5% women; mean age 58.2 years) were analyzed. Mean weight loss was 2.97 kg (3.17%). Significant differences between drugs were observed (p = 0.005), with semaglutide 2.4 mg (Wegovy) showing the greatest reduction (11.0 kg). Patients without diabetes achieved significantly greater weight loss than those with diabetes (5.0 vs. 0.8 kg; p = 0.021). Treatments were well tolerated, with 53.1% re-porting no adverse effects; most side effects were mild gastrointestinal symptoms. Conclusions: GLP-1RAs are effective and well-tolerated for obesity treatment in re-al-world clinical practice, although weight loss is more modest than in pivotal clinical trials. Differences between agents persist after adjustment, with specific formulations like semaglutide 2.4 mg showing superior effectiveness. These findings support the need for individualized treatment strategies in obesity care. This pilot study success-fully validated the methodology for an ongoing nationwide investigation

Abstract: Hard flaccid syndrome (HFS) is an emerging condition of male sexual dysfunction characterized by a persistent semi-rigid penis in the flaccid state, altered penile sensation, erectile dysfunction, and pelvic or perineal pain. Single-modality treatments have shown limited success, and multimodal protocols have been reported only in single-patient case studies. Our objective was to conduct a retrospective analysis of clinical outcomes from an integrative multimodal rehabilitation protocol in men with HFS. Thirty-two men with HFS completed a comprehensive protocol combining class IV laser therapy, dry needling, radial pressure wave shockwave therapy, therapeutic ultrasound, biofeedback training, manual therapy, therapeutic exercise, behavioral coaching, and oral tadalafil. Patient-reported outcomes were collected at treatment initiation and completion. The main outcome measures were Erection Hardness Scale (EHS), penile satisfaction, PROMIS Sexual Interest, and PROMIS Global Health Physical and Mental Component scores. In this case series, median EHS increased from 2 to 4 and median penile satisfaction increased from 2 to 5 (both P<0.01). All 32 patients achieved EHS ≥3 by treatment end, compared with 8 of 32 (25%) at baseline. PROMIS Sexual Interest, Physical Component, and Mental Component scores all improved significantly (P<0.01). Common comorbid features included low back pain (53%), hip or groin pain (38%), pelvic floor pain (31%), and urinary symptoms (28%). In this retrospective case series, multimodal treatment produced substantial improvements in erectile function and sexual quality of life in men with HFS, supporting an integrative model in which musculoskeletal and end organ pathologies initiate the syndrome and are amplified by central and peripheral nervous system contributions.

Abstract: Background: Risk-reducing mastectomy (RRM) and risk-reducing salpingo-oophorectomy (RRSO) are established strategies for women carrying pathogenic or likely pathogenic (P/LP) variants in high-risk cancer susceptibility genes. However, real-world uptake remains variable and is influenced by clinical, demographic, and psychosocial factors. This study evaluated adherence to guideline-recommended surgical risk-reduction strategies following positive genetic testing. Materials and Methods: We conducted a retrospective observational study of 13,069 women who underwent hereditary cancer testing using a multigene next-generation sequencing panel between 2020 and 2025. Among them, 1,255 carriers of P/LP variants in genes with established recommendations for RRM and/or RRSO were identified. Physician-reported questionnaires were available for 203 individuals and captured data on counseling, acceptance, and implementation of risk-reducing surgery. Genes were grouped according to NCCN guideline recommendations: (i) BRCA1/2; (ii) PALB2, PTEN, TP53; and (iii) BRIP1, RAD51C, RAD51D. Results: Of the 203 women, 83% had a personal history of cancer. Overall, 83.7% were offered at least one risk-reducing intervention, with 64.9% accepting. RRM was more frequently discussed in higher breast cancer–risk genes, while RRSO discussion varied by gene group; these differences were statistically significant. Acceptance rates were moderate for RRM but consistently high for RRSO across groups. Younger age and advanced-stage disease were key factors limiting uptake or discussion, and 22 patients postponed surgery despite initial agreement. Conclusions: In this real-world cohort, physician counseling largely aligned with gene-specific guidelines, and acceptance of risk-reducing surgery was high once discussed, particularly for RRSO. However, uptake was influenced by age, disease stage, and clinical context. These findings highlight the importance of early genetic testing and multidisciplinary counseling to optimize timely decision-making and improve adherence to preventive strategies.

Abstract: Urban flooding in rapidly urbanizing coastal megacities is increasingly intensified by climate variability, declining permeability, ecological degradation, and infrastructure pressures. In Mumbai, India, flood management continues to rely predominantly on conventional grey stormwater infrastructure despite growing international advocacy for Blue-Green Infrastructure (BGI). However, limited research has examined institutional readiness and governance conditions shaping BGI design, planning and implementation within Indian municipal systems. This study investigates institutional knowledge, perception, and implementation readiness regarding BGI within the Municipal Corporation of Greater Mumbai (MCGM) through a mixed-methods approach combining informal interviews with senior municipal officials and a structured survey administered across the Storm Water Drains (SWD), Planning, and Gardens departments. The findings indicate that Mumbai’s stormwater governance framework remains largely engineering and drainage-capacity oriented, with flooding increasingly recognized as a multi-causal challenge associated with high-intensity rainfall, reduced permeability, drainage limitations, tidal interactions, and rapid urbanization. While institutional responses continue to prioritize grey infrastructure interventions, the interviews and survey findings reveal growing openness toward ecological and hybrid grey–green approaches within future flood-management planning. The survey findings demonstrate widespread institutional awareness regarding flooding occurrence and strong willingness toward BGI implementation across departments. However, technical understanding related to BGI multifunctionality, hydrological performance, implementation mechanisms, and limitations under extreme rainfall conditions remained comparatively uneven across institutional groups. The Planning Department demonstrated comparatively stronger conceptual understanding of BGI and ecological planning approaches, while the SWD and Gardens departments demonstrated comparatively stronger implementation willingness despite lower technical familiarity. The study identifies an important institutional gap between conceptual understanding and implementation readiness within the MCGM and highlights the need for integrated governance, technically grounded hydrological capacity building, and context-specific ecological planning approaches. The findings contribute empirical insights into governance transitions shaping hybrid grey–green stormwater management within dense tropical coastal megacities and support the development of integrated ecological and climate-resilient urban flood-management frameworks.

Abstract: I propose that both quantum theory and gravitation are emergent low-energy phenomena arising from a deeper pre-quantum, pre-spacetime dynamics formulated on split bioctonionic geometry with symmetry E8 × ωE8. In this framework, Connes time replaces external classical time, trace dynamics replaces quantum kinematics, and spacetime itself emerges when many atoms of spacetime-matter become sufficiently entangled and undergo a quantum-to-classical transition. The program naturally accommodates three fermion generations, left-right structure, gravi-weak unification, and explicit charged-fermion mass relations. I explain these achievements, identify the principal open problems, and compare the program fairly with string theory. The central claim is that unification may require not quantizing classical spacetime, but deriving both spacetime geometry and quantum mechanics from a more primitive octonionic dynamics.

Abstract: Polymers enable countless modern technologies, yet vast regions of their chemical space remain unexplored. Traditional polymer discovery relies on chemical intuition, ingenuity, and experience (with a healthy dose of serendipity), yet it fails to leverage millions of potentially accessible and synthesizable polymer structures. Here, we present RxnChainer, a digital methodology integrating virtual polymer generation, retrosynthetic analysis, and post-polymerization modification to systematically explore synthetically accessible polymer space. Using commercially available monomers from the Toxic Substances Control Act (TSCA) and ChEMBL databases and RxnChainer, we generated over 289 million hypothetical polymers across 44 polymerization pathways spanning 32 polymer classes, including polyamides, polyimides, polyesters, and polyethers. Comparison with the known (i.e., previously synthesized) spectrum of polymers revealed that a significant portion of these new synthesizable structures are novel, i.e., previously unknown and unexplored. We demonstrate the methodology's versatility through automated retrosynthetic planning for 30,000 polyesters and targeted functionalization via four post-polymerization modification pathways incorporating vinyl and nitrile pendant groups. The resulting datasets enable downstream tasks such as property-driven screening, application-specific design, and training of generative models.

Abstract: Non–small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of NSCLC patients who underwent surgery between 2022 and 2024 was analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n=55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n=74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p=0.001) and higher TMB (7.8 vs. 5.8, p=0.021). In multivariate logistic regression, both PD-L1 and TMB independently predicted cluster assignment (p&lt;0.05). Mutation-based SNA clustering successfully delineated biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed increased immunogenicity, reflected in elevated PD-L1 and TMB levels. This method offers a novel integrative approach that requires prospective validation before clinical implementation.

Abstract: Particle pollution has been recognized as a major part of environmental pollution. More specifically, the inhalation of very small (ultrafine) airborne particulate matter (PM) that is emitted from the burning of fossil fuels poses the most serious threat to human health. High-efficiency retention of these particles is one of the most challenging environmental problems, since conventional techniques like electrostatic precipitators, bag filters or cyclones have low collection efficiency in the respirable range (0.1 μm–1.0 μm). Acoustically induced agglomeration of ultrafine particles is a promising technique to increase the size of small particles before they enter a conventional filter. During this process, high-intensity acoustic fields are applied to the flue gas stream, inducing interaction effects among suspended particles that give rise to collisions and agglomeration. The preconditioned aerosol can then be filtered within conventional filters with higher collection efficiency. The present work reports the results of a numerical investigation of the effect of ultrasound preconditioning on the particle size distribution as a function of parameters related to the ultrasound system design, such as the acoustic frequency and intensity, and the initial mass loading. Particle agglomeration is modeled via the solution of the population balance equation (PBE) with the Multi-Monte Carlo (MMC) method. Results show that acoustic agglomeration can shift particle size distribution towards larger values of diameters and reduce the total number concentration of particles, thus leading to increased capture efficiency of conventional filters.

Abstract: AlphaEarth Foundations (AEF) unifies multi-modal and multi-temporal observations into analysis-ready low-dimensional representations, introducing a representation-driven paradigm that addresses key limitations of traditional task-centric approaches, including high engineering complexity, limited cross-regional transferability, and strong dependence on labeled data. This paper presents the first systematic review of AEF, synthesizing 23 research articles published up to March 2026 from three perspectives: conceptual positioning, technical framework, and application practices. It first clarifies the distinctions between AEF and related paradigms, including foundation models, remote sensing large models, and existing unified embedding approaches. It then summarizes the organization of its data system and key technical components. Based on a systematic literature survey, the paper further provides a structured synthesis of current studies in terms of thematic and regional distribution, scientific questions, usage patterns, and evaluation methods. The analysis indicates that AEF represents an important step toward a paradigm shift from task-driven to representation-driven Earth observation, offering clear advantages in reducing engineering barriers, enabling cross-regional transfer, and establishing a unified environmental semantic foundation. However, limitations remain in temporal resolution, semantic interpretability, and performance in specific task scenarios. Future work should focus on enhancing dynamic representation capabilities, cross-domain adaptation mechanisms, multi-source integration frameworks, systematic evaluation and annotation systems, and asset-oriented applications. By delineating the capability boundaries and applicable contexts of AEF from both methodological and empirical perspectives, this study provides a systematic reference for the standardized development and rational application of unified surface embeddings as an emerging geospatial information infrastructure.

Abstract: The efficiency of data-driven research relies not only on high-quality data and sufficient computational resources but also depends sensitively on the personal knowledge management of the researcher. The multitude of digital artefacts created during the researcher’s daily workflow might comprise experimental results, simulation results, literate programming notebooks analysing experiments and simulations, statistical models, machine learning models, figures, tables, and conversations with generative artificial intelligence systems. In order to trace and track these interconnected research artefacts over several months of research or even extended research periods and different research projects, these artefacts need to be systematically named so that they can be referenced in note-keeping systems and research outputs. Therefore, the naming and referencing scheme for research artefacts needs to be flexible, consistent, efficient and support the linking of artefacts across different software frameworks and even classical laboratory notebooks. This article introduces a hierarchical naming scheme and the supporting open-source Python package contexere together with best practises for the personal knowledge management for postgraduate students and early career researchers, which provides a clear and linkable structure for data artefacts and thus supports effective personalised research workflows.

Abstract: Plant-derived compounds exhibit well-documented osteogenic and anti-resorptive activities; however, their translation into consistent skeletal benefits remains limited. This review proposes a transformation-state–dependent framework in which the efficacy of plant-based interventions is interpreted through the exposure architectures they generate rather than solely through intrinsic molecular activity. By integrating plant matrix organization, gastrointestinal processing, microbial biotransformation, and formulation-driven pharmacokinetics with the temporal dynamics of bone remodeling, the review addresses a critical gap in current literature, which largely evaluates phytochemicals independent of their delivery context. Across a continuum ranging from intact plant matrices to isolated compounds and advanced delivery systems, distinct pharmacokinetic regimes emerge, characterized by differences in release kinetics, metabolic transformation, systemic persistence, and target-site exposure. Evidence indicates that sustained, metabolite-mediated exposure profiles are more compatible with the prolonged, cumulative nature of bone remodeling, whereas transient exposure often limits efficacy despite mechanistic activity. Formulation strategies, including phospholipid complexes, bioenhancers, and nano- or vesicle-based systems, can partially overcome these limitations by modulating exposure behavior. By reframing plant-based interventions as dynamic exposure systems, this framework provides a unifying basis for interpreting variability across studies and offers a rational foundation for designing strategies that align pharmacokinetic behavior with skeletal biology, thereby improving translational potential.

Abstract: Time series analysis (TSA)–forecasting, anomaly detection, imputation, classification, and unified multi-task analytics– has become a battleground for sequence-modeling backbones, where Transformers, MLPs, convolutions, and state-space models compete on long context benchmarks. Since the 2023 release of Mamba, its linear time recurrence and input-dependent selectivity have triggered a surge of Mamba-based time-series models that report strong numbers yet resist like-for-like comparison. This survey provides the first focused treatment of Mamba for time series analysis, organized around four orthogonal perspectives: Model, Task, Data, and Application. The Model perspective formalizes a five-axis design space– tokenization, channel strategy, directional scan, hybridization, and decomposition– together with a three-pattern architectural toolbox of pure, bidirectional, and hybrid designs. The Task perspective re-indexes the corpus across the five canonical TSA tasks. The Data perspective re-tags it by data shape, spanning univariate, multivariate, spatio-temporal graph, and irregular series. The Application perspective then surveys deployments in healthcare, energy, traffic, climate, finance, activity recognition, and foundation-scale settings. Building on these views, we distill practical guidelines for variant selection, training recipes, and Mamba-specific pitfalls. We also catalog public implementations, datasets, and metrics, and we map out open frontiers in gain attribution, modeling regimes, and cross-task unification. An online repository is maintained at https://github.com/tamlhp/awesome-mamba-ts.

Abstract: A single-server queueing framework with infinite queueing capacity is formulated and analyzed, where customer arrivals are governed by a Markovian arrival process and both service and repair times are characterized through phase-type distributions. The service structure is two-tier: each incoming customer undergoes a mandatory primary service under a first-come, first-served discipline; then a secondary service becomes available on an optional basis, provided only at the customer’s request after the primary service is completed. When the system empties, the server initiates a shutdown process before entering a vacation period. Upon return from vacation, the server resumes service if customers are present; otherwise, successive vacations will be taken until demand arises. Random server failures can occur during either service mode, after which the server undergoes repair before restarting. The steady-state behavior of the system is analyzed using the matrix analytical method, from which the stability conditions, stationary probability vectors, and key performance metrics are derived. A cost analysis framework is also formulated to evaluate the economic implications of system operation. To substantiate these analytical findings and illustrate the practical applicability of the proposed model, a series of numerical experiments are performed.

Abstract: Algorithms based on gradient-boosted trees are known for predictive accuracy in tabularregression, while hybrid boosting designs adding non-tree terms remain underexplored. We in-troduce Ordered Piecewise Additive Learner (OPAL), a stagewise regression model combininghinge, categorical indicator, and regression-tree terms. Stage 1 of OPAL sequentially adds hingeand categorical indicator terms to squared loss residuals; Stage 2 fits regression-tree terms toremaining residual structure. To increase expressiveness, OPAL augments data with determinis-tic numeric transformations, including products, ratios, differences, signed square roots, signedlogarithm of one plus absolute value transforms, and squared terms. OPAL can optionally fita single tree before the two-stage model and train the main component on the resulting residu-als. As regularization, inner cross-validation selects the prediction rule among the full predictor,the two-stage component alone, the optional single-tree component, the training-set mean, or aweighted blend. The rule is stored as fitted state, and the selected rule is reflected in a distilledadditive prediction equation used for term-level summaries. Across 35 regression tasks, OPALhas the best average RMSE rank against XGBoost, LightGBM, and MARS: 1.8571 versus 2.0286,2.4286, and 3.6857. A Friedman test rejects equality of ranks (χ2F = 42.977, p = 2.49 ×10−9).Log-ratio paired tests do not separate OPAL from XGBoost but favor OPAL over LightGBMand MARS. The results support the strong predictive performance of the configured OPALpipeline as a whole rather than any isolated algorithmic component; potential research direc-tions therefore include further study of OPAL’s constituent parts. A unique aspect of OPAL isits partial additive transparency: hinge and categorical terms support effect-curve summarieswhen they have nonzero weight in the distilled prediction equation, although the full predictormay include generated pairwise features and tree terms involving the same raw variables.

Abstract: Background/Objectives: Rhanterium epapposum Oliv. a medicinally valuable plant traditionally used by indigenous communities to treat skin and gastrointestinal ailments and as a natural insecticide, was investigated for its phytochemical composition and diverse biological activities (antioxidant, antimicrobial, anti-inflammatory), and enzyme inhibitory (α-amylase and lipoxygenase), supported by advanced in silico analyses. Methods: Methanolic and aqueous extracts were analyzed for total phenolic, flavonoid, and tannin contents, with their metabolite profiles characterized via LC-ESI-MS/MS. Both extracts were comprehensively assessed for antioxidant, antimicrobial, anti-inflammatory, and enzyme inhibitory (α-amylase and 5-lipoxygenase) activities. Furthermore, in silico framework was applied to elucidate the binding efficiency and inhibitory potential of key bioactive compounds against selected pharmacological targets. Results: Many bioactive compounds, mainly chlorogenic and syringic acids were identified in both extracts. The aqueous extract showed higher TTC (69.61 ± 0.212 mg TAE/g extract) and TFC (23.81 ± 0.163 mg QE/g extract), while the methanolic extract was richer in phenolics and exhibited overall stronger scavenging antioxidant activity with IC₅₀ of 14.9 ± 4.7 µg/mL (DPPH) and 35.0 ± 0.67 µg/mL (ABTS), respectively. both extracts exhibited remarkable antimicrobial activity towards ESKAPE and Candida spp. strains. Furthermore, the aqueous extract demonstrated greater, dose-dependent oedema inhibition, peaking at 100 mg/kg, and stronger α-amylase (IC₅₀ = 188 μg/mL) and lipoxygenase (IC₅₀ = 49 μg/mL) inhibition than the methanolic extract (IC₅₀ = 247 μg/mL and 188 μg/mL, respectively), though both were less potent than standard drugs. Molecular docking, MD simulations, and DFT analyses assessed phytocompounds against multiple targets. Chlorogenic acid exhibited multi-target activity, forming hydrogen bonds with Ser49, Thr121, Leu5, Ala7, Asp27 (3FYV), Asp120, Asp86, Asp218 (3Q70), Ser602, Arg415, Asn382, Arg483, Ile461, Ser508 (7Q6S), Gly249, Asp212, Tyr2 (1B2Y), and Gly249, Leu211, Asp212 (1N8Q). MD confirmed complex stability, while DFT (6-31G**) highlighted favorable electronic reactivity and stability. Conclusions: Overall, the aqueous and methanolic extracts showed complementary bioactivities, emphasizing their potential as natural therapeutic agents and viable candidates for developing new pharmacological formulations.

Abstract: One of the most common ways to improve the properties of aluminum casting alloys is through their modification. This study investigates the influence of titanium modification on the mechanical properties of Al-Si casting alloys. In this research, the Al-Si alloy which is widely used in the foundry industry, was selected as an object. The samples were liquefied in an induction furnace, and the liquid alloy was poured into sand-clay molds. The casting temperature was 750 °C. The titanium element was added to the liquid Al-Si foundry alloy in special packaging in the form of a powder from 0.1% to 0.3% of the charge and in the form of Al-10Ti master alloy from 0.1% to 0.2%. Then, samples were machined to prepare further investigations. During the research, mechanical properties including hardness and wear resistance analysis were conducted. Moreover, X-ray diffraction and microstructural analysis of the Ti modified Al-Si samples were carried out. Experimental results showed that the addition of titanium improved the mechanical properties of the samples. That is, the highest hardness was obtained at 0.1 wt.% Ti modified Al–10Ti master alloy, while titanium powder resulted in a more gradual increase in hardness. According to the wear resistance evaluations, addition of titanium within the range of 0.1–0.2 wt.% content was performed an optimal result. After, microstructural analysis, it is found that titanium promoted grain refinement and improved structural homogeneity, especially it is added in the form of Al-10Ti master alloy. The introduction of titanium into the aluminum alloy led to the formation of the Al₃Ti intermetallic compound, which contributed to the improvement of mechanical properties. These results demonstrate that the modification of Al–Si alloys with titanium can be reliably used to predict and improve mechanical properties based on comprehensive experimental analysis.