Search Results (7,351)

Join size estimation plays a crucial role in query optimization, correlation computing, and dataset discovery. A recent study, LDPJoinSketch, has explored the application of local differential privacy (LDP) to protect the privacy of two data sources when estimating their join size. However, the utility of LDPJoinSketch remains unsatisfactory due to the significant noise introduced by perturbation under LDP. In contrast, the shuffle model of differential privacy (SDP) can offer higher utility than LDP, as it introduces randomness based on both shuffling and perturbation. Nevertheless, existing research on SDP primarily focuses on basic statistical tasks, such as frequency estimation and binary summation. There is a paucity of studies addressing queries that involve join aggregation of two private data sources. In this paper, we investigate the problem of private join size estimation in the context of the shuffle model. First, drawing inspiration from the success of sketches in summarizing data under LDP, we propose a sketch-based join size estimation algorithm, SDPJoinSketch, under SDP, which demonstrates greater utility than LDPJoinSketch. We present theoretical proofs of the privacy amplification and utility of our method. Second, we consider separating high- and low-frequency items to reduce the hash-collision error of the sketch and propose an enhanced method called SDPJoinSketch+. Unlike LDPJoinSketch, we utilize secure encryption techniques to preserve frequency properties rather than perturbing them, further enhancing utility. Extensive experiments on both real-world and synthetic datasets validate the superior utility of our methods. Full article

Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of remote sensing observations with sector-specific disaster statistical data to construct a knowledge system that supports early warning decision-making remains a significant challenge. This study aims to address the bottleneck in the “data-information-knowledge-service” transformation process by constructing an integrated natural disaster early warning knowledge graph that incorporates multi-source heterogeneous data. We first designed an ontological schema layer comprising six core elements: disaster type, event, anomaly information, impact information, warning information, and decision information. Subsequently, multi-source data were integrated from various sources, including the Emergency Events Database (EM-DAT), sector-specific websites, encyclopedic pages, and remote sensing imagery such as Gaofen-2 (GF-2) and Sentinel-1. A Bidirectional Encoder Representations from Transformers with a Conditional Random Field layer (BERT-CRF) model was employed for entity and relation extraction, and the knowledge was stored and visualized using the Neo4j graph database. The core innovation of this research lies in proposing a quantitative methodology for assessing disaster intensity, impact, and trends based on remote sensing evaluation, establishing a knowledge conversion mechanism with sector-specific warning levels, and designing explicit warning issuance rules. A case study on a specific wildfire event (2017-0417-PRT, Coimbra, Portugal) demonstrates that the knowledge graph not only achieves organic integration and visual querying of multi-source disaster knowledge but also facilitates warning decision-making driven by remote sensing assessment indicators. For this event, quantitative analysis of Gaofen-2 imagery yielded intensity, impact, and trend levels of 4, 3, and 3, respectively, which, when applied to our warning rule (intensity ≥ 1 or impact ≥ 1 or trend ≥ 3), automatically triggered an early warning, thereby validating the rule’s practicality. A preliminary performance evaluation on 50 historical wildfire events demonstrated promising results, with an F1-score of 74.3% and an average query response time of 128 ms, confirming the system’s practical responsiveness and detection capability. In conclusion, this study offers a novel and operational technical pathway for the deep interdisciplinary integration of remote sensing and disaster science, effectively bridging the gap between data silos and actionable warning knowledge. Full article

Escherichia coli O157:H7 (EcO157) is a Gram-negative foodborne pathogen capable of transmitting between soil, food, and humans, posing a threat to human health. The soil pH in Jilin Province decreases gradually from west to east, exhibiting a natural pH gradient zone. Persistence of EcO157 in soils from different places was widely reported, while its survival behavior in soils over a pH gradient is yet to be investigated. In the current study, a total of 24 soil samples were collected along a natural pH gradient. Soils were classified into weak acidic soil (pH < 6.5), neutral soil (6.5 < pH < 7.5), weak basic soil (7.5 < pH < 8.5), and strong basic soil (8.5 < pH < 10). EcO157 cells were inoculated into those soils and the survival profiles were investigated. The influencing factors affecting the survival behavior of EcO157 were analyzed by multivariate statistical analysis. The results showed that the average survival time of EcO157 in weak acidic, neutral, weak basic, and strong basic soils was 61.08, 72.05, 76.85, and 18.54 days, respectively. The survival time in strong basic soils was significantly less than that in the other three soil groups. Soil physicochemical properties such as NO₃⁻-N and NH₄⁺-N were negatively linked to the survival of EcO157, while total phosphorus (TP)was positively correlated to the survival of EcO157 (p < 0.05). The microbial community α diversity index was negatively correlated with the survival of EcO157, while relative abundance of Proteobacteria and Acidobacteria was positively and negatively correlated to the survival of EcO157, respectively. Both co-occurrence network analysis and structural equation model results showed that pH was a key factor that could directly and indirectly influence the survival of EcO157 via the bacterial community. Our data coupled with the findings of other studies might be of great help in the evaluation, control, and reduction of the potential health risk associated with EcO157 in soils along a natural pH gradient. Full article

Background/Objectives: The anti-spike (S) SARS-CoV-2 antibodies confer neutralizing properties and their concentration may be related to COVID-19 protection. Anti-nucleocapsid (N) SARS-CoV-2 antibodies in mRNA COVID-19 vaccine recipients indicate infection. The aim of this study was to analyze the anti-S and anti-N titers 3 years after COVID-19 vaccination. Methods: Ninety-nine vaccinated healthcare workers provided blood samples in 2024 and filled out questionnaires about their COVID-19 history and boosters acceptance. Anti-spike and anti-nucleocapsid IgG were assessed with commercially available immunoassays, DiaSorin’s SARS-CoV-2 TrimericS IgG and Abbott’s SARS-CoV-2 IgG, respectively. Results: Three years after the primary COVID-19 vaccination, the anti-S SARS-CoV-2 antibody concentration was still high. However, it dropped in comparison to the data obtained a year before (3600 vs. 2040 BAU/mL), possibly due to the lack of boosters. In contrast, the percentage of anti-N seropositive individuals grew from 34% two years after vaccination to 40.4% after three years. Subjects with SARS-CoV-2 infection within a year prior to the antibody measurements had statistically significantly higher median anti-S concentrations than those with tentatively no contact with SARS-CoV-2 (2940 vs. 1930 BAU/mL). Conclusions: Overall, our data indicates that although the booster vaccinations’ acceptance decreases, the circulating SARS-CoV-2 stimulates humoral immunity, resulting in high anti-S antibody concentrations even three years after the vaccination. Full article

Biochar has been widely recognized for its potential to improve soil quality and mitigate greenhouse gas (GHG) emissions. A field experiment was conducted in a temperate climate zone of Slovakia on Haplic Luvisol and evaluated the long-term impact of biochar on soil properties, nitrous oxide (N₂O) emissions, and winter wheat (Triticum aestivum L.) yield. Biochar was applied in 2014 at rates of 0, 10, and 20 t ha⁻¹ and reapplied in 2018 at the same rates, combined with nitrogen (N) fertilization (0, 140, and 210 kg N ha⁻¹). Measurements, conducted from March to October 2021, showed that biochar improved soil water content, increased soil pH, and enhanced soil organic carbon content. However, the concentrations of NH₄⁺-N and NO₃⁻-N generally decreased across all the treatments compared to their respective controls. Biochar reapplication rate at 20 t ha⁻¹, especially combined with second level of N-fertilization, led to a significant reduction in cumulative N₂O emissions by 38.40%. Winter wheat yield was positively correlated with both biochar application (10 and 20 t ha⁻¹) and N levels (140 and 210 kg N ha⁻¹), but these differences were not statistically significant (p > 0.05). The positive effects of biochar on soil properties and yield declined over time, with no significant yield differences observed 7 years after the initial application and 3 years after reapplication. These findings suggest that while biochar can enhance soil conditions and reduce GHG emissions in the short term, its long-term effectiveness remains uncertain. Further research is needed to explore alternative biochar feedstocks, application methods, and strategies to sustain its benefits in agricultural systems. Full article

Background/Objectives: Depression is a prevalent mental health problem among undergraduate students, and dietary patterns may play a role in its prevention. Phytochemical-rich diets have been proposed to be potential protective factors against depression due to their antioxidant, anti-inflammatory, and neuroprotective properties. This study aimed to investigate the association between the Dietary Phytochemical Index (DPI) and depressive symptoms among undergraduate students in Türkiye. Methods: A descriptive, cross-sectional study was conducted among 789 undergraduate students at Erciyes University between May 2024–May 2025. Dietary data were collected using a 101-item Food Frequency Questionnaire, and the DPI was calculated as the percentage of total daily energy derived from phytochemical-rich foods. Depressive symptoms were assessed via the Burns Depression Checklist (BDC). Statistical analyses included correlation and logistic regression models adjusted for gender, income, and academic department. Results: Participants with higher DPI scores exerted significantly lower BDC total and sub-dimension scores, including activities and personal relationships, physical symptoms, and suicidal urges (all p < 0.05). The inverse association between DPI and total depression score remained significant across all adjusted models (p < 0.001), and a significant linear trend was observed across DPI quartiles (p-trend < 0.001). Conclusions: Higher dietary phytochemical intake was associated with lower depressive symptom levels among undergraduate students. These results suggest that phytochemical-rich dietary patterns, characterized by increased consumption of fruits, vegetables, whole grains, legumes, and nuts, may contribute to improved psychological well-being. Promoting the intake of phytochemical-dense foods could be a practical nutritional strategy for supporting mental health in young adults. Full article

Groundwater level predictive monitoring is necessary to address accelerated aquifer depletion and ensure sustainable management under increasing climatic and anthropogenic pressures. This study uses machine learning approaches to model groundwater level (GWL) dynamics in six observation wells in the Ruataniwha Basin, New Zealand. These models are enhanced with seasonal decomposition techniques. This study uses both static properties and dynamic variables to capture hydrogeological heterogeneity. Random Forest (RF) and Support Vector Machine (SVM), with seasonal decomposition preprocessing, were developed for GWL modelling. The models were trained on 80% of the dataset and tested using the remaining 20% of the data. Model accuracy was assessed using five statistical metrics: mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination (R²), mean absolute percent error (MAPE), and percent bias (PBIAS). Model uncertainty was analyzed using Bayesian Model Averaging combined with the p-factor and d-factor at the 95% confidence level. The results demonstrate that both models delivered strong predictive performance across training, testing, and full period evaluations. However, the RF model demonstrated a marginally superior predictive accuracy by achieving lower errors (MAE: 0.013–0.174; RMSE: 0.04–0.283), better bias control (PBIAS ≈ 0%), and slightly tighter error bounds in most wells. Uncertainty quantification revealed that models provided a minimum p-factor of 0.878, capturing more than 87% of the observed GWL data within the uncertainty bounds. Comparing the results of both models, the RF model has higher p-factor values ranging from 0.878 to 0.976 with precise interval widths (d-factor: 0.436–0.769), indicating its reliability for adaptive groundwater management. Full article

Variability in reclaimed asphalt pavement (RAP) properties, such as aggregate gradation, asphalt content, and moisture content, poses a significant challenge to producing consistent and reliable recycled asphalt mixtures. This study systematically evaluated processing techniques for mitigating variability through a comparative analysis of four fractionation strategies, i.e., unfractionated, two-fraction, four-fraction, and six-fraction processing. Corresponding to the four approaches, four distinct reference RAP mixtures were fabricated by proportionally recombining the obtained RAP fractions towards a target gradation. The gray relational analysis (GRA) was employed to quantify geometric similarity between the gradation curve of reclaimed aggregates from each fraction and the target gradation curve, thereby facilitating efficient determination of blending proportions without resorting to complex optimization algorithms. Statistical variability indicators, including range, standard deviation, and coefficient of variation (COV), were used to assess the effectiveness of each fractionation and recombining method. The results demonstrated that refined fractionation processing significantly reduced variability, particularly in gradation properties. Compared with the COV values from the commonly used two-fraction processing, those from the refined four-fraction and six-fraction processing methods decreased by up to 51.5% and 73.5%, respectively. While increasing the number of fractions generally enhanced homogeneity, the four-fraction approach emerged as the most technically reliable and economically viable strategy, achieving a desirable balance between processing effort and variability control. Furthermore, the GRA proved to be a practical and efficient tool for blend proportioning, reducing reliance on complex numerical methods. These findings reveal the importance of refined fractionated RAP processing in enabling the production of high-RAP recycled mixtures with improved uniformity and performance. Full article

This study presents fractal technology as a foundational approach to sustainable growth in the artificial intelligence (AI) era and Industry 5.0. We explore how the principles of fractal geometry, including self-similarity and recursive properties, improve scalability, efficiency, and adaptability in AI-driven systems. Representative applications include neural networks, decentralized control, and intelligent manufacturing, where fractal-based design enables modularity, fault tolerance, and optimized resource use. Case studies and theoretical models demonstrate that a fractal frameworks provide a viable path toward long-term, self-organizing industrial innovation and sustainability-oriented vision of Industry 5.0. Theoretical perspectives are strengthened by connections to nonextensive Tsallis statistics and parallels with complex systems in quantum field theory, suggesting the universality of fractal laws across disciplines. Case studies confirm that fractal frameworks offer a viable path toward long-term, self-organizing industrial innovation, contributing to the emerging field of fractal engineering and providing a systems-level paradigm for sustainable technological evolution. Full article

Background and Objectives: The treatment of Chronic Myeloid Leukemia (CML) faces challenges such as resistance to Tyrosine Kinase Inhibitors (TKIs), necessitating new adjuvant therapies. This study aimed to evaluate the cytotoxic effect of direct, photosensitizer-free irradiation with LASER and LED light on the CML cell line K-562, hypothesizing that LASER light at a specific wavelength would be selectively effective. This work serves as a foundational in vitro study to establish the basis for a potential ex vivo therapeutic strategy. Methods: The human CML cell line K-562 was irradiated with LASER (405, 532, 629 nm) and LED (457, 517, 630 nm) sources at energy doses from 1 to 10 J/cm². Cell viability was assessed 24 h post-irradiation using Trypan Blue exclusion, the MTT assay, and biophysical changes in the cell absorbance spectrum. Results: Irradiation with a 532 nm LASER was the only condition that induced massive, statistically significant, and dose-dependent cytotoxicity, reaching up to 67.8% cell death at 10 J/cm² (p < 0.05). In contrast, other LASER wavelengths and all tested LED wavelengths failed to produce a significant cytotoxic effect. The superiority of the LASER over the LED of a similar wavelength highlights the critical role of the physical properties of light. Conclusions: Direct, photosensitizer-free irradiation with 532 nm LASER light is a potent and selective method for inducing cytotoxicity in K-562 cells in vitro. This effect is critically dependent on both the specific wavelength and the optical properties of the light source. These findings establish a solid foundation for the development of new ex vivo adjuvant therapies, such as extracorporeal photopheresis, for CML, pending further validation of its mechanism and selectivity. Full article

Accurate estimation of population distribution characteristics is a fundamental task in survey sampling and statistical inference. This paper introduces a new family of estimators for the cumulative distribution function (CDF) under probability proportional to size (PPS) sampling, incorporating auxiliary information to enhance efficiency. The proposed approach employs dual auxiliary variables in the estimation phase, while the sampling design relies on a single auxiliary variable. Theoretical properties, including bias and mean squared error (MSE), are rigorously derived to establish the efficiency of the new class. An extensive empirical evaluation using three distinct populations—fisheries data, wine chemistry data, and demographic records—demonstrates the superiority of the proposed estimators. In terms of accuracy, the best-performing proposed estimator achieves an MSE of 0.0012, compared to 0.0127 for the widely used GK estimator. Percentage relative efficiency (PRE) values further underscore these improvements, with gains ranging from 123% to over 328% across the three populations. Graphical comparisons confirm these trends, illustrating that the proposed estimators consistently dominate conventional approaches. Overall, the findings highlight both the theoretical soundness and practical utility of the proposed family, offering robust and computationally efficient improvements for CDF estimation in complex survey designs. Full article

Successful root canal treatment depends on adequate obturation with biocompatible and non-cytotoxic materials. This study evaluated the in vitro and in vivo biological characteristics of an experimental polydimethylsiloxane (PDMS)-based endodontic sealer and compared it with Silco^® and Sealapex^® cement. Human dermal fibroblasts (HDFa) were exposed to polydimethylsiloxane-based sealer eluates, Silco^® and Sealapex^®, at concentrations of 1:200, 1:100, 1:50, 1:1, and undiluted eluate (1×) for 24, 48, and 72 h, and they were subcutaneously implanted in Wistar rats for 15, 30, and 45 days. Cell viability exceeded 90% at 24–48 h and remained at 85% at the highest concentration after 72 h. Sealapex^® showed approximately 85% viability at 24 h, over 70% at 48 h, and remained below the cytotoxicity threshold at 72 h. Silco^® showed a marked reduction, with values approaching 50% at 24 h. At 48 and 73 h, Silco^® showed a significant reduction in cell viability. Histological analysis revealed only mild acute and chronic inflammation, with no statistically significant differences over time. These results indicate that the experimental sealant demonstrates favorable biological properties suitable for further clinical evaluation. Full article

Introduction: Patient comfort is a central concept in nursing practice, and is particularly important in emergency contexts, where clinical complexity and care overload challenge the provision of humanized care. Katharine Kolcaba’s Theory of Comfort offers a robust theoretical framework for assessing and promoting comfort in multiple domains. The main objective is to psychometrically validate the adapted version of Kolcaba’s Comfort Scale—COLCABA-32—in critically ill patients treated in a Portuguese hospital emergency department. Method: A quantitative, descriptive, cross-sectional study was conducted using a sample of 165 adult patients triaged with urgent clinical priority. Data collection was performed through individual interviews. The COLCABA-32 Scale and the Mini-Mental State Examination (MMSE) were used. Statistical analysis included descriptive statistics, principal component analysis (PCA), internal consistency (Cronbach’s alpha), and correlation with clinical priority according to the Manchester Triage. Results: PCA revealed six factors with eigenvalues greater than 1, explaining 59.01% of the total variance of the scale. The dimensions identified were psycho-emotional comfort and autonomy, physical and symptomatic comfort, relational comfort and information, spiritual comfort, environmental comfort and motivational comfort and hope. The overall Cronbach’s alpha was 0.897, indicating excellent internal consistency. Correlations with clinical priority confirmed partial convergent validity. Conclusions: The COLCABA-32 Scale demonstrated adequate psychometric properties for assessing the comfort of critically ill patients in an emergency setting and is a valid, reliable, and sensitive instrument for the multiple dimensions of comfort, as proposed by Kolcaba. Its application can contribute to more person-centered and evidence-based nursing practices. Full article

The global sparkling wine market continues to grow steadily, reaching approximately 24 million hectoliters in 2023, with an annual increase of around 4% despite a general decline in overall alcoholic beverage consumption. This growth highlights the importance of employing diverse yeast strains to improve product variety and quality. Flor yeasts are specialized strains of Saccharomyces cerevisiae that develop a biofilm on the surface of certain wines during biological ageing. They possess unique physiological properties, including high ethanol tolerance and the capacity to adhere, which supports wine clarification. They also have the ability to contribute unique volatile compounds and aroma profiles, making them promising candidates for sparkling wine production. This study evaluated two flor yeast strains (G1 and N62), which were isolated from the Pérez Barquero winery during the second fermentation process using the traditional method. Sparkling wines were produced by inoculating base wine (BW) with each strain, and the wines were monitored at 3 bar CO₂ pressure and after 9 months of ageing on lees. Comprehensive metabolomic analysis was performed using GC-MS for volatile compounds and HPLC for nitrogen compounds, with statistical analysis including PCA, ANOVA, Fisher’s LSD, and correction FDR tests. Strain N62 demonstrated faster fermentation kinetics and higher cellular concentration, reaching 3 bar pressure in 27 days compared to 52 days for strain G1. Both strains achieved similar final pressures, 5.1–5.4 bars. Metabolomic profiling revealed significant differences in the profiles of volatile and nitrogen compounds between the two strains. G1 produced higher concentrations of 3-methyl-1-butanol, 2-methyl-1-butanol, and acetaldehyde, while N62 generated elevated levels of glycerol, ethyl esters, and amino acids, including glutamic acid, aspartic acid, and alanine. These findings demonstrate that both flor yeast strains successfully complete sparkling wine fermentation while producing distinct metabolic signatures that could contribute to unique sensory characteristics. This supports their potential as alternatives to conventional sparkling wine yeasts for enhanced product diversification. Full article

The hardening soil model with small-strain stiffness (HSS model), capturing nonlinear stiffness of soils at small strains, offers advantages for deformation analysis of tunnels or deep excavations in soft clay areas such as Hangzhou City. However, its complex parameters are rarely determinable via conventional tests, and regional geological differences render parameter determination methods of other areas inapplicable to Hangzhou. To address this issue, this paper summarizes the geological genesis, spatial distribution, and physical–mechanical properties of Hangzhou soft clays, and clarifies significance and acquisition of HSS model parameters. Via statistical analysis of existing literature data, the relationships between key HSS model parameters and physical indices (e.g., void ratio) were established. A 3D finite element (FE) simulation of a Hangzhou excavation validated the proposed parameter determination method: simulated lateral retaining structure displacement and surface settlement closely matched field measurements. The simulation results employing the model parameters proposed herein are closer to the measurements than those based on the method of Shanghai, providing guidance for excavation design and geotechnical parameter selection in Hangzhou soft soil region. Full article