Search Results (3,144)

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

The Kuroshio Current’s flow velocity imposes exacting requirements on underwater vehicle propulsive systems. Ecological preservation necessitates low-noise propeller designs to mitigate operational disturbances. As technological evolution advances toward greater intelligence and system integration, intelligent unmanned systems are positioning themselves as a critical frontier in marine innovation. In recent years, the global research community has increased its efforts towards the development of high-maneuverability underwater vehicles. However, propeller design optimization ignores the key balance between acoustic performance and hydrodynamic efficiency, as well as the appropriate speed threshold for blade rotation. In order to solve this problem, the propeller design of the NACA 65A010 airfoil is optimized by using OpenProp v3.3.4 and XFlow 2022 software, aiming at innovating the propulsion system of shallow water agile submersibles. The study presents an integrated design framework combining lattice Boltzmann method (LBM) simulations synergized with fully Lagrangian-LES modeling, implementing rotational speed thresholds to detect cavitation inception, followed by advanced acoustic propagation analysis. Through rigorous comparative assessment of hydrodynamic metrics, we establish an optimization protocol for propeller selection tailored to littoral zone operational demands. Studies have shown that increasing the number of propeller blades can reduce the single-blade load and delay cavitation, but too many blades will aggravate the complexity of the flow field, resulting in reduced efficiency and noise rebound. It is concluded that the propeller with five blades, a diameter of 234 mm, and a speed of 500 RPM exhibits the best performance. Under these conditions, the water efficiency is 69.01%, and the noise is the lowest, which basically realizes the balance between hydrodynamic efficiency and acoustic performance. This paradigm-shifting research carries substantial implications for next-generation marine vehicles, particularly in optimizing operational stealth and energy efficiency through intelligent propulsion architecture. Full article

During the period from 2020 to 2024, 900 springs were mapped on the southern slopes of Medvednica Mountain Nature Park. Physicochemical parameters (temperature, pH, and electrical conductivity) were measured at 701 of these springs using a portable multimeter, and results were analyzed in relation to local lithology and human activities. This research provides the first results of this kind in this study area, aiming to expand the knowledge on local springs and to support the future protection and management of spring ecosystems. Springs on the Medvednica mountain showed substantial variation in measured parameters. The temperature ranged from 3.4 to 18.9 °C, reflecting local hydrological conditions, aquifer characteristics, and seasonal variability. Electrical conductivity (EC) ranged between 41 μS/cm and 2062 μS/cm, determined by both hydrogeological settings and anthropogenic impacts such as winter road salting. The pH values showed moderate variability, remaining mostly within neutral levels. These results emphasize the importance of continued monitoring and further research of Medvednica springs, in order to highlight their importance and to preserve their ecological and hydrological roles. Full article

A metal-ion-free method was developed to prepare κ-carrageenan/cellulose hydrogel beads for efficient cationic dye removal. The beads were fabricated using a mixture of 1-ethyl-3-methylimidazolium acetate and N,N-dimethylformamide as the solvent system, followed by aqueous ethanol-induced phase separation. This process eliminated the need for metal-ion crosslinkers, which typically neutralize anionic sulfate groups in κ-carrageenan, thereby preserving a high density of accessible binding sites. The resulting beads formed robust interpenetrating polymer networks. The initial swelling ratio reached up to 28.3 g/g, and even after drying, the adsorption capacity remained over 50% of the original. The maximum adsorption capacity for crystal violet was 241 mg/g, increasing proportionally with κ-carrageenan content due to the higher surface concentration of anionic sulfate groups. Kinetic and isotherm analyses revealed pseudo-second-order and Langmuir-type monolayer adsorption, respectively, while thermodynamic parameters indicated that the process was spontaneous and exothermic. The beads retained structural integrity and adsorption performance across pH 3–9 and maintained over 90% of their capacity after five reuse cycles. These findings demonstrate that κ-carrageenan/cellulose hydrogel beads prepared via a metal-ion-free strategy offer a sustainable and effective platform for cationic dye removal from wastewater, with potential for heavy metal ion adsorption. Full article

In order to address vibration and noise challenges in modern industry while satisfying the lightweighting requirements for aerospace and transportation applications, the development of polymer elastomers integrating both lightweight and high-damping properties holds substantial significance. This study developed polyurethane (PU) with optimized damping and mechanical properties at room temperature through monomer composition optimization. Hollow glass microspheres (HGMs) were introduced into the PU matrix to increase stiffness and reduce density, though this resulted in decreased tensile strength (Rm) and loss factor (tanδ). To further improve mechanical and damping properties, we applied a carbon coating to the surface of the HGMs to optimize the interface between the HGMs and the PU matrix, and systematically investigated the energy dissipation and load-bearing behavior of PU composites. The effect of enhanced interface damping of HGM@C/PU resulted in broadening of the effective damping temperature range (tanδ ≥ 0.3) and higher maximum loss factor (tanδ_max) compared to HGM/PU at equivalent filler loading. The tensile and dynamic properties significantly improved due to optimized interfacial adhesion. In PU composites reinforced with 10 wt% HGM and HGM@C, a 46.8% improvement in Rm and 11.0% improvement in tanδ_max occurred after carbon coating. According to acoustic testing, average transmission loss of HGM/PU and HGM@C/PU with the same filler content showed a difference of 0.3–0.5 dB in 500–6300 Hz, confirming that the hollow structure of the HGMs was preserved during carbon coating. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

This article examines the concept of ming 命 (mandate/command or fate/destiny) in the Analects, Mencius, and Zhuangzi, exploring its relationship to tian 天 (Heaven). Across these works, ming retains an intrinsic connection to tian—an inviolable cosmic force beyond human control. All three texts exhibit profound reverence and submission to tian, acknowledging the boundary between human control and cosmic inevitability, yet, at the same time, advocating active alignment with tian’s ordained patterns. In the Analects, a central tension emerges between tian’s teleological purpose—centered on preserving human culture and ethical cultivation—and the seemingly arbitrary fluctuations of individual fate, particularly regarding lifespan and personal fulfillment. This tension persists in the Mencius, articulated as a conflict between the political disorder of Mencius’ contemporary era and tian’s normative moral order. The Zhuangzi, by contrast, resolves this tension through advocating for withdrawal from the political life, as well as a radical reinterpretation of tian. Stripping tian off the Confucian moral–cultural imperatives, the text deconstructs dichotomies like life and death, championing inner equanimity via flowing with the cosmic transformation. Full article

Many hospitals still lack digital traceability in hygiene and cleaning management, leading to operational inefficiencies and inconsistent quality control. This study aims to establish cleaning and hygiene processes in healthcare services that are planned in accordance with standards, as well as to enhance the traceability and sustainability of these processes through digitalization. This study proposes a Hyperledger Fabric-based blockchain architecture to establish a reliable and transparent quality assurance system in process management. The proposed Quality Assurance Model utilizes digital technologies and IoT-based RFID devices to ensure the transparent and reliable monitoring of cleaning processes. Operational data related to cleaning processes are automatically recorded and secured using a decentralized blockchain infrastructure. The permissioned nature of Hyperledger Fabric provides a more secure solution compared to traditional data management systems in the healthcare sector while preserving data privacy. Additionally, the execute–order–validate mechanism supports effective data sharing among stakeholders, and consensus algorithms along with chaincode rules enhance the reliability of processes. A working prototype was implemented and validated using Hyperledger Caliper under resource-constrained cloud environments, confirming the system’s feasibility through over 100 TPS throughput and zero transaction failures. Through the proposed system, cleaning/hygiene processes in patient rooms are conducted securely, contributing to the improvement of quality standards in healthcare services. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

Our temptation to personify machine intelligence is not unexpected. As a child we named our dolls and took our Teddy Bear to bed with us. Today we ask death bots to comfort us with post-mortem conversation. All the while we know this to be pretend. Yet we must ask: if Artificial General Intelligence (AGI) or even Artificial Super-Intelligence (ASI) become available, will our game of pretend continue? Or will intelligent robots actually become selves deserving of dignity that hitherto could be ascribed only to human persons? If government-imposed guardrails shut the door on development of AGI and ASI in order to preserve human safety and even dignity, we might never learn whether AGI or ASI could develop selfhood, personhood, virtue, or religious sensibilities. As we approach the future, can we live without knowing whether AGI or ASI would be capable of developing selfhood and commanding dignity? Full article

This study examines the geostrategic role of Hungarian-language higher education institutions beyond Hungary’s border. These institutions not only fulfil an educational function but also play a role in preserving identity and geopolitics in the national policy of the Hungarian state. This research is based on a narrative review of the literature, which analyses the demographic situation of Hungarians living beyond the borders and the tools used to support higher education by synthesising domestic and international literature, statistical data, and forecasts. The results highlight that Hungarian-language higher education plays a key role in preserving ethnocultural identity and increasing the chances of success in the homeland, but also faces constraints such as labour market disadvantages resulting from a lack of state language skills. This study concludes that, in order to ensure the sustainability of Hungarian higher education beyond the border, it is necessary to strike a balance between identity preservation and integration, thereby promoting geopolitical stability and cultural cohesion with the majority society. Full article

This paper is focused on developing a Galerkin finite element framework for the fractional Allen–Cahn equation with regularized logarithmic potential over the ${\mathbb{R}}^d$ ($d=1,2,3$) domain, where the regularization of the singular potential extends beyond the classical double-well formulation. A fully discrete finite element scheme is developed using a k-th-order finite element space for spatial approximation and a backward Euler scheme for the temporal discretization of a regularized system. The existence and uniqueness of numerical solutions are rigorously established by applying Brouwer’s fixed-point theorem. Moreover, the proposed numerical framework is shown to preserve the discrete energy dissipation law analytically, while a priori error estimates are derived. Finally, numerical experiments are conducted to verify the theoretical results and the inherent physical property, such as phase separation phenomenon and coarsening processes. The results show that the fractional Allen–Cahn model provides enhanced capability in capturing phase transition characteristics compared to its classical equation. Full article

Background/Objectives: The rapid emergence of antibiotic-resistant Escherichia coli in food and clinical environments necessitates new, clean-label antimicrobials. This study assessed eight Greek native essential oils—oregano, thyme, dittany, rosemary, peppermint, lavender, cistus and helichrysum—for activity against six genetically and phenotypically diverse E. coli strains (reference, pNorm, mecA, mcr-1, blaOXA and O157:H7). We aimed to identify oils with broad-spectrum efficacy and clarify the chemical constituents responsible. Methods: Disk-diffusion assays measured inhibition zones at dilutions from 50% to 1.56% (v/v). MIC and MBC values were determined by broth microdilution. GC–MS profiling identified dominant components, and Spearman rank-order correlations (ρ) linked composition to activity. Shapiro–Wilk tests (W = 0.706–0.913, p ≤ 0.002) indicated non-normal data, so strain comparisons used Kruskal–Wallis one-way ANOVA with Dunn’s post hoc and Bonferroni correction. Results: Oregano, thyme and dittany oils—rich in carvacrol and thymol—exhibited the strongest activity, with MIC/MBC ≤ 0.0625% (v/v) against all strains and inhibition zones > 25 mm at 50%. No strain-specific differences were detected (H = 0.30–3.85; p = 0.998–0.571; p_adj = 1.000). Spearman correlations confirmed that carvacrol and thymol content strongly predicted efficacy (ρ = 0.527–0.881, p < 0.001). Oils dominated by non-phenolic terpenes (rosemary, peppermint, lavender, cistus, helichrysum) showed minimal or no activity. Conclusions: Phenolic-rich EOs maintain potent, strain-independent antimicrobial effects—including against multidrug-resistant and O157:H7 strains—via a multi-target mode that overcomes classical resistance. Their low-dose efficacy and GRAS status support their use as clean-label food preservatives or adjuncts to antibiotics or bacteriophages to combat antimicrobial resistance. Full article

Coprolites from the Oligocene Menilite Formation of the Outer Carpathians in southeastern Poland were investigated to reveal the diversity of prey fishes consumed by coprolite producers. The material comprises 186 coprolites from seven localities. The coprolites are either sub-spherical, or elongate, and although classified into eight shape categories, display a morphological continuum. The phosphatic matrix is preserved in 28% of the specimens. Fish remains, including bones and scales, are preserved in 94% of the coprolite specimens. In 31% of specimens, these remains belong to the orders Perciformes, Gadiformes, Clupeiformes, and Aulopiformes. Prey sizes were estimated and compared to the sizes of fishes preserved as articulated skeletons from the same formation, that inhabited the Carpathian Basin of the Paratethys. The results demonstrate that coprolite analysis provides a significant paleontological data, which can be applied to infer fish diversity in other regions of the Paratethys, as well as in other sedimentary basins. Full article