Search Results (24,876)

Biomimetic restorative treatments in pediatric dentistry increase the longevity of the restoration compared to traditional methods and aim to preserve the natural tooth structure. Prefabricated zirconia crowns have been developed as aesthetic alternatives to stainless steel crowns for full-coronal restorations of primary teeth. This study aimed to compare the fracture resistance and microleakage of two different posterior zirconia crown brands—NuSmile^® (USA) and ProfZrCrown^® (Turkey)—cemented with either conventional glass ionomer cement (GIC) or resin-modified glass ionomer cement (RMGIC). Eighty extracted primary molars were divided into four groups (n = 20). Crowns were cemented with Ketac™ Cem Radiopaque (GIC) or Ketac™ Cem Plus (RMGIC), in accordance with the manufacturers’ instructions, and then subjected to thermocycling. Fracture resistance was tested on 40 samples by applying an increasing compressive load until failure, with values recorded in Newtons (N). The remaining 40 samples were immersed in basic fuchsin dye for microleakage testing and evaluated under a stereomicroscope at 30× magnification. The results revealed that the ProfZrCrown^®/RMGIC group exhibited significantly higher fracture resistance compared to the NuSmile^®/RMGIC group (p < 0.05). No statistically significant differences were found among the other groups. Although no significant differences in microleakage were observed among the groups (p > 0.05), crowns cemented with GIC demonstrated higher microleakage levels. Within the limitations of this in vitro study, ProfZrCrown^® may be considered a promising alternative for aesthetic posterior restorations in pediatric dentistry. Full article

Cycle slip detection in the carrier phase is important for achieving precise relative positioning with global navigation satellite systems (GNSSs) in urban environments. The false alarm rate (FAR) and missing detection rate (MDR) of the detection approach will significantly affect the positioning error. This paper proposes a novel cycle slip detection approach based on inter-frequency cross-validation (IFCV), and designs corresponding test statistics and thresholds. In contrast to conventional cycle slip detection methods, the proposed IFCV approach has the advantages of higher accuracy and efficiency, which is validated by the airborne dynamic test. For cycle slips of specific proportions (such as five-cycle and four-cycle slips), the IFCV method significantly reduces the MDR from 3.89% to 0% and decreases the relative positioning error from 4.69 cm to 3.17 cm. In tests involving randomly injected cycle slips, the IFCV method reduces the MDR from 0.02% to 0%, reduces the relative positioning error from 3.83 cm to 3.02 cm, and decreases the average computational time from 1.02 ms to 0.53 ms. Full article

Background/Objectives: Burning Mouth Syndrome (BMS) is a chronic orofacial pain disorder characterized by persistent intraoral burning sensations without visible mucosal lesions. Although its biopsychosocial complexity is increasingly recognized, cross-cultural comparison data remain limited. Methods: This cross-sectional study assessed 60 patients with BMS (30 Italian, 30 Romanian) who underwent standardized clinical, psychological, and sleep evaluations. Data collected included sociodemographics, clinical characteristics, diagnostic history, comorbidities, and symptomatology. The assessment tools used included the Numeric Rating Scale (NRS), Short Form of the McGill Pain Questionnaire (SF-MPQ), Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS). Statistical comparisons were conducted using Mann–Whitney U and Fisher’s exact tests with Bonferroni correction. Results: No significant differences were observed in age, sex, or body mass index. Italian patients had fewer years of education (p = 0.001), higher pain intensity (NRS, p < 0.001), poorer sleep quality (PSQI, ESS, p = 0.001), and more frequent pre-existing sleep disorders (p < 0.001). Romanian patients showed higher levels of anxiety (HAM-A, p < 0.001), longer diagnostic delays (p = 0.002), and more dysesthetic or perceptual symptoms, including tingling and oral dysmorphism (p < 0.05). Stressful events before onset were more common among Romanians (p < 0.001), while Italians more often received a correct diagnosis at first consultation (p = 0.005). Conclusions: This first cross-national comparison of BMS in Western and Eastern Europe shows that cultural, healthcare, and clinician education differences can shape symptom profiles, comorbidities, and diagnostic delays, underscoring the need for personalized, country-specific management strategies. Full article

High-pressure carbon dioxide (HPCD) has been widely used in the extraction of high-quality bioactive compounds. The flavor profiles of cold brew coffee (CBC) prepared by HPCD, traditional cold brew (TCB), and ice drip (ID) were comprehensively evaluated by chromatographic approaches, and their variations were investigated by multivariate statistical methods. ID produced the lightest coffee color while HPCD produced the darkest. No significant difference was found in pH among the three coffee processes. The concentrations of chlorogenic acids and caffeine were the highest in ID but the lowest in HPCD. Seventeen of the forty-eight volatiles were identified as key aroma compounds, contributing nutty, cocoa, caramel, baked, and other coffee flavors to all CBCs. Among them, linalool (OAV = 100.50) was found only in ID and provided ID with unique floral and fruity notes; 2-methyl-5-propylpyrazine (OAV = 17.70) was found only in TCB and gave a roasted aroma. With significantly lower levels of medicine-like and plastic off-flavors, HPCD had a refined aroma experience featuring nutty, cocoa, and caramel notes, though their contents were not the highest. Orthogonal partial least squares discriminant analysis (OPLS-DA) identified 36 aromas that could differentiate three cold brew methods, with TCB and HPCD being the most similar. Aroma sensory tests showed that no significant difference was perceived between TCB and HPCD. These findings provide a profound understanding of CBC flavor produced by cold brew methods from the aspect of composition, indicating that HPCD has great potential to realize TCB-like flavor characteristics in a shorter time. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

Background/Objectives: The adoption of artificial intelligence (AI) in healthcare, particularly in high-stakes environments such as operating rooms (ORs), is expanding rapidly. While AI has the potential to enhance patient safety and clinical efficiency, it may also trigger anxiety among healthcare professionals due to uncertainties around job displacement, ethical concerns, and system reliability. This study aimed to examine the relationship between AI-related anxiety and patient safety attitudes among OR professionals. Methods: A descriptive, cross-sectional research design was employed. The sample included 155 OR professionals from a university and a city hospital in Turkey. Data were collected using a demographic questionnaire, the Artificial Intelligence Anxiety Scale (AIAS), and the Safety Attitudes Questionnaire–Operating Room version (SAQ-OR). Statistical analyses included t-tests, ANOVA, Pearson correlation, and multiple regression. Results: The mean AIAS score was 3.25 ± 0.8, and the mean SAQ score was 43.2 ± 10.5. Higher AI anxiety was reported by males and those with postgraduate education. Participants who believed AI could improve patient safety scored significantly higher on AIAS subscales related to learning, job change, and AI configuration. No significant correlation was found between AI anxiety and safety attitudes (r = −0.064, p > 0.05). Conclusions: Although no direct association was found between AI anxiety and patient safety attitudes, belief in AI’s potential was linked to greater openness to change. These findings suggest a need for targeted training and policy support to promote safe and confident AI adoption in surgical practice. Full article

This cross-sectional study evaluated the seroprevalence and clinical impact of West Nile virus (WNV) infection in horses from three ecologically high-risk counties in western Romania (Timiș, Arad, and Bihor) between 2023 and 2025. A total of 306 unvaccinated horses were tested using a commercial ELISA, with 8.17% testing positive for WNV antibodies, indicating prior exposure. Passive surveillance for clinical signs during mosquito seasons identified 16 horses with acute neurological symptoms, four of which were confirmed as clinical cases based on WNV-specific IgM positivity, suggesting probable silent WNV circulation in the region. The overall case fatality rate among confirmed clinical cases was 25.0%. WNV seropositivity was highest in Bihor (8.85%), followed by Arad (8.57%) and Timiș (7.32%). Statistical comparisons using χ² tests and binary logistic regression indicated no significant differences in seroprevalence between counties, sexes, or age groups, consistent with the overlapping 95% confidence intervals. These findings suggest the continued silent circulation of WNV in the region and support the integration of equine surveillance into the One Health framework as a potential tool for early detection and risk mitigation. However, in the absence of molecular confirmation (e.g., RT-PCR or virus isolation), these results should be interpreted as indicative of prior exposure rather than direct evidence of ongoing viral activity. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a progressive condition whose heterogeneous endotypes, clinical manifestations, and recovery pathways complicate the identification of reliable predictors of rehabilitation outcomes. Several respiratory and functional assessments are available with no consensus on the most predictive ones. While univariate markers may miss multifactorial interactions essential for prognosis, data-driven unsupervised clustering methods can integrate complex information from different sources. This study aimed to apply unsupervised clustering to identify pre-rehabilitation characteristics predictive of discharge outcomes for COPD patients undergoing pulmonary rehabilitation. Methods: A total of 126 COPD patients undergoing pulmonary rehabilitation were included in the analysis. Three assessments were performed at admission, namely the forced oscillation technique, spirometry, and the six-minute walk test (6MWT). The outcome was the change in 6MWT distance between admission and discharge. Unsupervised clustering methods were applied to admission variables to identify subgroups associated with outcomes. Results: Among the clustering algorithms tested, k-means (with N_cl = 2) provided the optimal solution. The resulting respiratory rehabilitation index (R2I) was significantly associated with the outcome dichotomized via the minimal clinically important difference of 30 m. Patients with R2I = 1, indicating severe functional and respiratory impairments, were associated with higher post-rehabilitation functional improvement (p = 0.032). While few functional parameters of 6MWT were statistically different between the groups identified by outcome, nearly all variables in the analysis exhibited significant distribution differences among the R2I clusters. Conclusions: These findings highlight the heterogeneity of COPD and the potential of unsupervised clustering to identify distinct patient subgroups, enabling more personalized rehabilitation strategies. Full article

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

Research has evaluated the efficacy of various methods for eliminating endophytes from grass seeds, as well as changes in endophyte viability during seed storage under different conditions, indicating significant variation in different procedures and cultivars. Chemical seed treatment (tebuconazole and thiram) completely eliminated viable fungal mycelia, leaving no trace in any tested cultivar. Non-chemical methods, such as drying and microwave treatment, only partially reduced mycelial viability by 30.3% and 33.1%, respectively, with no statistically significant difference between them. A significant positive correlation was observed between the initial mycelial viability and its reduction. Lolium perenne cv. Vigor showed no impact from non-chemical methods, while Festuca rubra cv. Anielka exhibited the greatest reduction (79% after microwave treatment). Seed storage also impacted endophyte survival. Storage at +7 °C, +23 °C, and −20 °C reduced viability by 27.4%, 31.7%, and 37.3%, respectively. Positive correlations existed between initial viability and post-storage reductions. Similarly to elimination methods, cv. Vigor showed resistance to storage conditions. However, −20 °C storage proved least favorable for endophyte survival, particularly for Festuca pratensis cv. Artema, cv. Anielka, and Festuca ovina cv. Jolka. To maintain the viability of beneficial endophytes during seed storage, we must carefully control storage conditions, especially ambient temperature. Full article

Objectives: Fluorescein break-up time (FBUT) is commonly used to assess tear film stability. However, the instillation of fluorescein destabilises the tear film, impacting validity and clinical applicability, while the subjective nature and variation in volume and concentration reduces repeatability. Non-invasive break-up time (NIBUT) offers an alternative method with less potential bias. Normal tear break-up time is conventionally accepted as 10 seconds (s); however, FBUT is expected to be lower than NIBUT. This study was designed to compare FBUT and NIBUT values in a pre-operative refractive surgery population, where diagnosis of dry eye disease may alter the risk–benefits ratio and contraindicate surgical procedure(s). Improved understanding of the relationship between these two methods will aid appropriate pre-operative patient counselling and consent. Methods: Data from consecutive participants presenting to a private ophthalmology clinic, for initial refractive surgery pre-operative assessment, were analysed. NIBUT and FBUT were performed. Paired and unpaired comparisons were made using the Wilcoxon signed-rank and Mann–Whitney U tests, respectively, and relationships with demographics were explored using Spearman’s rank correlation coefficient. Results: Median and interquartile range (IQR) for the first NIBUT was 12.5 s (7.0–18.0 s) and 14.2 s (9.4–18.0 s) for the right and left eyes, respectively. Median and IQR for the average NIBUT was 14.0 s (6.9–18.0 s) and 14.6 s (10.1–18.0 s) for the right and left eyes, respectively. Median and IQR for FBUT was 7 s (5–8 s) and 6 s (5–8 s) for the right and left eyes, respectively. There was a statistically significant difference between NIBUT and FBUT (p < 0.001). Conclusions: The findings suggest that the commonly used diagnostic threshold of 10 s cannot be uniformly applied to both FBUT and NIBUT, as FBUT systematically underestimates tear stability. Full article

Objectives: Anagrelide, an oral phosphodiesterase-3 inhibitor, is widely used to treat thrombocythemia. Evaluating the bioequivalence of low-dose formulations is essential to ensure consistent therapeutic outcomes while minimizing adverse effects, particularly cardiovascular events such as palpitations, tachycardia, and potential arrhythmias, which are known concerns with anagrelide therapy. This study aimed to compare the pharmacokinetics and bioavailability of a newly developed 0.5 mg anagrelide capsule with the reference product under fasting conditions y. Materials and Methods: In a randomized, open-label, two-period crossover design, 42 healthy Turkish male volunteers received a single oral dose (0.5 mg) of either the test or reference anagrelide capsule, with a seven-day washout period between treatments. Serial blood samples were collected over a 10 h post-dose period. Plasma concentrations of anagrelide were analyzed using a validated LC-MS/MS method. Key pharmacokinetic parameters (AUC₀–t, AUC₀–∞, Cmax, tmax, λz, t½, AUC–extrapol) were calculated and subjected to ANOVA-based bioequivalence analysis. Results: A total of 42 healthy male participants (mean age: 34.1 ± 8.9 years; BMI: 25.7 ± 2.9 kg/m²) completed the study without any protocol deviations. Pharmacokinetic analysis demonstrated that the test and reference formulations of anagrelide 0.5 mg were bioequivalent. The mean AUC₀–t values were 4533.3 ± 2379.3 pg·h/mL for the test formulation and 4515.0 ± 2392.3 pg·h/mL for the reference (p > 0.05), while the mean Cmax values were 1997.1 ± 1159.2 pg/mL and 2061.3 ± 1054.0 pg/mL, respectively (p > 0.05). The 90% confidence intervals for the geometric mean ratios of AUC₀–t (94.09–104.75%), Cmax (85.62–104.03%), and AUC₀–∞ (94.50–105.10%) were all within the predefined bioequivalence range of 80–125%, with corresponding point estimates of 99.28%, 94.37%, and 99.66%, respectively. Intra-subject variability was 14.68% for AUC₀–t and 26.98% for Cmax. No statistically significant differences were observed between the formulations for any of the primary or secondary pharmacokinetic parameters (ANOVA, p > 0.05). Regarding safety, 13 treatment-emergent adverse events were reported in 11 participants (26.2%), mostly moderate-intensity headaches, all of which resolved without complications. No serious adverse events occurred, confirming the tolerability of both formulations. Conclusions: This study demonstrates that the test and reference formulations of low-dose 0.5 mg anagrelide are bioequivalent under fasting conditions, with similar safety and tolerability profiles. The findings support the use of the test product as a safe and effective alternative. Full article

Despite the widespread deployment of inclinometers and GPS, an engineering gap remains for a low-cost, seepage-sensitive landslide early-warning technique. To explore the application of self-potential (SP) in landslide monitoring and early warning, a series of physical simulations were conducted, focusing on slope rainfall and slope cracking conditions. The self-potential signals were monitored using a custom-built STM32-based acquisition system, which provided continuous, real-time data with minimal noise. The relationship between self-potential signals and internal changes within the landslide body was analyzed, revealing that SP signals are highly sensitive to seepage, saturation, and structural changes within the slope. During slope rainfall simulations, the self-potential signals responded rapidly to changes in rainfall intensity, capturing the dynamic nature of seepage and saturation changes. A dynamic early-warning model was developed based on statistical methods, including sliding t-tests/Pettitt mutation tests and Mahalanobis distance test, to detect early signs of landslide instability. The model successfully identified significant changes in SP signals that corresponded to the onset of landslide movement, demonstrating the potential of self-potential for real-time landslide monitoring and early warning. This study highlights the effectiveness of self-potential monitoring in detecting early signs of landslide instability and suggests that SP signals can be a valuable addition to existing landslide monitoring systems. Full article

The endangered plant Hopea hainanensis serves as both an indicator and keystone species in tropical rainforests, and its survival status is influenced by the interspecific relationships among coexisting tree species within the community. To explore these relationships, species resource utilization patterns, and community succession dynamics within the endangered plant community, this study utilized survey data from the Hopea hainanensis community in the Bawangling and Jianfengling branches of the National Park of Hainan Tropical Rainforest. Various analytical methods were employed, including the Variance Ratio (VR) method, test statistic (W), χ² test, Spearman’s rank correlation, and M. Godron’s stability analysis, to examine the interspecific associations among dominant tree species at different structural levels in the two regions and their effects on community stability. The results indicate that: (1) Hopea hainanensis is the dominant species in the medium tree layer in both study areas, while it functions as an associated species in other structural layers. (2) In communities where Hopea hainanensis is present in both Bawangling and Jianfengling, the dominant tree species across various structural layers generally show a non-significant positive association. (3) The results of the χ² test and Spearman’s rank correlation test reveal that the interspecific associations across different structural layers of the Hopea hainanensis communities in both regions are predominantly non-significant. This suggests weak interspecific relationships and a high degree of species independence. The communities at different structural levels in both Bawangling and Jianfengling are in an unstable state, with ongoing dynamic adjustments to their internal tree species composition and structure. In terms of stability, the community stability across structural levels in these two regions follows the order: middle shrub layer > middle arbor layer > small arbor layer > large shrub layer. This study reveals the interspecific relationships, community succession status, and stability of dominant tree species at different structural levels in slope barrier communities across regions. These findings provide a theoretical basis for developing scientifically sound and reasonable protection strategies for slope barrier populations, as well as for the restoration and sustainable development of tropical rainforest vegetation. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article