Search Results (64,600)

Background: Congenital tooth agenesis is a common dental anomaly in individuals with orofacial clefts and may be related not only to cleft type but also to skeletal growth characteristics. This study aimed to investigate whether the number of congenitally missing permanent teeth is associated with cleft type, sex, and sagittal and vertical skeletal patterns in non-syndromic cleft patients. Materials and Methods: A retrospective cross-sectional analysis was conducted on 60 patients aged ≥17 years (36 males, 24 females; mean age 19.5 ± 1.8 years) with surgically repaired cleft lip and/or palate, based on clinical records collected over a long-term follow-up period. Sagittal (Class I, II, III) and vertical (normal, deep bite, open bite) skeletal patterns were extracted from available orthodontic records based on routine cephalometric assessment. The number of congenitally missing permanent teeth, excluding third molars, was recorded. Statistical analysis included non-parametric tests and Poisson regression. Results: The distribution of missing teeth deviated significantly from normality according to the Shapiro–Wilk test (p < 0.001). In the Poisson regression model, sex (p = 0.011) and cleft type (p < 0.001) were significantly associated with the number of congenitally missing teeth, whereas sagittal skeletal pattern (p = 0.338) and vertical skeletal pattern (p = 0.281) were not significant predictors. Conclusions: In this retrospective record-based analysis, the number of congenitally missing teeth appeared most consistently associated with cleft type, while sex showed a model-dependent association in the adjusted regression analysis. Full article

Background and Objectives: Systemic inflammatory biomarkers have emerged as potential prognostic indicators in metastatic colorectal cancer (mCRC). However, the prognostic robustness of inflammatory indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), C-reactive protein-to-albumin ratio (CAR), and Glasgow Prognostic Score (GPS) remains incompletely characterized. In this study, we aimed to evaluate the prognostic significance of NLR, PLR, CRP, CAR, and GPS for progression-free survival in metastatic colorectal cancer in a cohort of patients from Romania. Materials and Methods: This retrospective observational study included 148 patients diagnosed with mCRC. Inflammatory biomarkers were determined from baseline laboratory parameters. Progression-free survival (PFS) was the primary endpoint. Statistical analyses included correlation testing, Kaplan–Meier survival analysis, Cox proportional hazards regression, Firth penalized Cox regression, restricted cubic spline modeling, time-dependent receiver operating characteristic (ROC) analysis, LASSO penalized regression, multiple imputation, and parsimonious multivariable Cox models adjusted for major clinicopathologic confounders. Results: Median PFS was 21 months (95% CI 19–24). In univariable Cox analyses, elevated NLR (HR 1.98, 95% CI 1.11–3.51, p = 0.020), PLR (HR 1.89, 95% CI 1.25–2.85, p = 0.002), CRP (HR 1.45, 95% CI 1.15–1.83, p = 0.002), and CAR (HR 1.44, 95% CI 1.05–1.98, p = 0.022) were associated with shorter PFS. Restricted cubic spline analysis demonstrated a significant nonlinear association between NLR and PFS (p = 0.0025). After multiple imputation, NLR remained associated with shorter PFS (HR 2.04, 95% CI 1.13–3.68, p = 0.018). However, in a multivariable model adjusted for major clinicopathologic confounders, this association was not retained (HR 1.41, 95% CI 0.81–2.43, p = 0.221) and time-dependent ROC analyses demonstrated its limited discriminatory performance. Conclusions: Although some inflammatory markers were associated with shorter PFS in univariable analyses, the prognostic effect of NLR was attenuated after adjustment and was not consistently confirmed across all analyses. Full article

To address the poor computational efficiency of traditional fixed-stratification methods in very low frequency (VLF) ionospheric propagation modeling, this paper proposes a dynamic stratification algorithm. First, filtering optimization is applied to the electron density, and dynamic adaptive stratification is implemented in the vertical direction. By establishing a nonlinear mapping relationship between the electron density gradient and the stratification thickness, the algorithm integrates dynamic ionospheric stratification with a hybrid regularization algorithm for the transmission matrix. Specifically, Singular Value Decomposition (SVD) and dynamic truncation techniques are employed to process the transmission matrix, effectively resolving the numerical ill-posedness in regions with abrupt ionospheric changes. This enables high-precision calculation of reflection coefficients in the 3–30 kHz frequency band. By tuning parameters such as the reference stratification thickness and adjustment factors, an optimized stratification model and an algorithm quality evaluation coefficient are obtained. The simulation results demonstrate that, compared with fixed stratification, the proposed algorithm achieves an average relative error of 4.7% for the reflection coefficient in the VLF range while improving computational efficiency by more than 50%. This provides a promising approach for efficient and high-precision prediction of VLF wave propagation. Full article

Background: Faricimab is one of the most potent anti-vascular endothelial growth factors used in the management of diabetic macular edema (DME). However, real-world benefits regarding its timing and efficacy are still being explored. Methods: This retrospective non-randomized pilot study aimed to evaluate the efficacy of intravitreal faricimab in the treatment of DME. Eyes initially treatment-naïve for DME with a follow-up of 1 year were grouped as: group 1, where faricimab was introduced within the first six months after the start of treatment; group 2, where it was initiated six or more months after treatment with other drugs. Study parameters included changes in best corrected visual acuity (BCVA) and optical coherence tomography based structural parameters within the 6 × 6 mm optical coherence tomography (OCT) scan regions. Results: Forty-two eyes from 26 patients were analyzed. No statistically significant differences were observed between the groups in cluster-weighted proportions of intra- or sub-retinal fluid, retinal thickness or volume parameters, although group 1 showed modest numerical benefits. SRF showed a trend towards qualitative reduction in group 1, although IRF showed persistence in both groups. Adjusted linear mixed-effects modeling demonstrated no significant impact of early faricimab initiation on functional and anatomical outcomes, which appeared to be influenced by the baseline BCVA, glycemic control, and the number of injections, nullifying the benefits. Conclusions: Faricimab demonstrated modest anatomical improvements with earlier treatment in eyes initially treatment-naïve for DME. Further prospective studies are indicated to assess the treatment strategy and the timing of introduction with faricimab in such eyes. Full article

The Omicron variant of SARS-CoV-2 is associated with milder symptoms and lower hospitalization and mortality rates than Delta variants, although the impact of Omicron on immunocompromised patients, especially solid organ transplant (SOT) recipients, is still unclear. This study compares the hospitalization rate and outcomes between immunocompromised, immunocompetent, and SOT patients during the Delta and Omicron periods. We included adult patients who tested positive for SARS-CoV-2 on PCR or nasopharyngeal antigen test between 26 June 2021 to 8 September 2022, at our institution. A total of 12,401 COVID-19 patients were included, of which 11,055 were immunocompetent, and 1346 were immunocompromised (375 SOT recipients). Throughout the Delta and Omicron outbreaks, immunocompromised patients exhibited higher comorbidities and 30-day hospitalizations, but rates of mechanical ventilation and ICU-level care were like immunocompetent patients. During the Omicron wave, immunocompromised patients had higher unadjusted relative risk estimates (RR = 2.37, 95% CI 1.96–2.87, p < 0.05) than Delta (RR = 1.58, 95% CI 1.24–2.01, p < 0.05), with higher adjusted relative risk for hospitalization in Omicron (RR = 1.50, 95% CI 1.10–2.03, p = 0.01). Analyses show increased hospitalization risk in immunocompromised during the Omicron wave compared to the Delta wave, with no significant difference in hospitalization outcomes. The relative risk of hospitalization for SOT patients was higher in both waves. Full article

Layered cloud cover (LCC) describes the vertical distribution of cloud occurrence and is a key variable for assessing the radiation budget of the Earth-atmosphere system. However, ground-based radars have limited spatial coverage, while existing satellite cloud-cover products rarely provide both spatiotemporal continuity and high accuracy. Because nighttime satellite observations lack visible-channel information, conventional passive satellite remote sensing remains limited in providing day-night continuous LCC retrievals. In this study, we propose an infrared-based framework for retrieving large-scale day-night LCC from geostationary satellite observations. The framework first resolves cloud vertical structure using a hybrid machine learning and physical algorithm for day-night cloud-base height (CBH) retrieval, and then derives cloud cover in different vertical layers. Validation against active measurements from spaceborne and ground-based cloud radar demonstrates that the satellite-retrieved LCC captures cloud vertical distributions and their diurnal variations. The cloud-layer identification accuracies reach 76.3% and 77.9% for daytime and nighttime, respectively, with corresponding Cohen’s kappa coefficients of 0.66 and 0.68. The primary source of algorithmic uncertainty is the low precision of low-cloud identification, which is constrained by objective factors and physical characteristics. The retrieved annual mean LCC fields reproduce major climatological features, including enhanced high and deep convective clouds over the tropical western Pacific and dominant low-cloud occurrence over the mid-latitude oceans. A case study of Typhoon Doksuri further shows that the 10 min LCC retrievals capture the vertical evolution of the typhoon cloud system during intensification, eyewall structural adjustment, landfall, and post-landfall decay. These results indicate that the proposed infrared-based retrieval framework provides a promising basis for constructing large-scale day-night LCC datasets and can support cloud-radiation studies, climate-model evaluation, and weather monitoring. Full article

Growing concerns regarding resource efficiency, economic uncertainty, and energy-market volatility have renewed interest in the relationship between material-use patterns and macroeconomic stability. Recent global disruptions affecting production systems and economic activity have intensified policy attention toward sustainable resource management and resilience-oriented growth strategies. Using an unbalanced panel of 30 OECD economies over the period 1995–2024, this study examines the relationship between resource productivity and economic resilience while accounting for material-use intensity and structural conditions. The empirical framework relies on second-generation panel econometric techniques that account for cross-sectional dependence and heterogeneous country dynamics. The findings indicate that resource productivity is positively associated with economic resilience, with a 1% increase in resource productivity corresponding to an approximately 0.18% increase in resilience. By contrast, domestic material consumption and material footprint display negative associations with resilience, suggesting that resource-intensive production and consumption patterns may be linked to lower adaptive capacity and macroeconomic stability. The short-run estimates additionally indicate the persistence of adjustment dynamics following economic disturbances. These findings highlight the relevance of resource-use efficiency for macroeconomic resilience and sustainable resource-management strategies in OECD economies. Full article

Background/Objectives: People with diabetes are more susceptible to viral respiratory infections and worse clinical outcomes related to COVID-19 and influenza. Vaccination is considered an important prevention strategy. This study aimed to analyze the vaccination status against COVID-19 and influenza among people with diabetes mellitus and associated factors. Methods: An analytical cross-sectional study was conducted between May 2024 and May 2025 in 42 Primary Health Care Units in a municipality in Minas Gerais, Brazil. A total of 316 individuals with type 1 or type 2 diabetes mellitus participated in the study. Data were collected using a structured instrument containing socioeconomic, cultural, behavioral, and clinical variables, in addition to verification of vaccination records through physical vaccination cards and information systems. Descriptive analyses and logistic regression models were performed to estimate crude and adjusted odds ratios, with respective 95% confidence intervals. Analyses were performed using Statistical Package for the Social Sciences and Stata. Results: Adherence to COVID-19 vaccination was 21.5%, whereas influenza vaccination adherence reached 85.4%. In the multivariable analysis of COVID-19 vaccination status, previous influenza vaccination (OR = 7.74; 95% CI: 1.81–33.2) and alcohol consumption (OR = 2.11; 95% CI: 1.13–3.89) were positively associated with vaccination. Conversely, access to social media or other communication channels (OR = 0.47; 95% CI: 0.24–0.92) and insulin use (OR = 0.42; 95% CI: 0.21–0.84) were associated with lower odds of COVID-19 vaccination. Regarding influenza vaccination, positive associations were identified for religious affiliation (OR = 6.46; 95% CI: 1.79–23.30), previous COVID-19 vaccination (OR = 10.2; 95% CI: 2.22–47.06), and longer duration of diabetes diagnosis (OR = 3.47; 95% CI: 1.32–9.20). In contrast, alcohol consumption (OR = 0.42; 95% CI: 0.21–0.86), insulin use (OR = 0.35; 95% CI: 0.16–0.76), and absence of medical follow-up (OR = 0.34; 95% CI: 0.13–0.85) were associated with lower odds of influenza vaccination. Conclusions: The findings revealed a heterogeneous vaccination pattern among individuals with diabetes mellitus, in which higher influenza vaccination coverage contrasted with low adherence to COVID-19 vaccination, reflecting not only differences in the historical consolidation of immunization strategies but also contemporary dynamics related to risk perception, trust, and information circulation. The strong association with previous vaccination history suggests that vaccine adherence is part of a continuum of preventive behaviors mediated by the relationship with healthcare services and by the internalization of healthcare practices over time. Full article

Feature selection (FS) is a pivotal preprocessing task in data mining aimed at identifying optimal feature subsets to improve model generalization and reduce computational overhead. However, its NP-hard nature poses significant challenges for traditional optimizers in terms of search efficiency and solution quality. The Harris Hawks Optimization (HHO) algorithm is a state-of-the-art population-based metaheuristic method that demonstrates powerful capabilities in various optimization challenges. Despite its advantages, HHO encounters problems such as early stagnation and reduced accuracy. To mitigate these problems, we introduce an advanced algorithm called the Hybrid Strategy Harris Hawks Optimization (HSHHO). The HSHHO combines three key enhancements to support global search diversity and local refinement: (1) an exploration mechanism that utilizes the Self-Parameterized Map (SPM) alongside a dynamic logarithmic spiral to expand search breadth; (2) a nonlinear adjustment to the escape energy parameter for improved phase equilibrium; and (3) an elite perturbation approach that uses Cauchy–Gaussian mutation to strengthen local optimization and solution quality. We assessed HSHHO against eight well-known algorithms on 30 benchmark functions, where it exhibited superior results in the majority of cases. Finally, HSHHO is applied to address 18 feature selection tasks. The results demonstrated that HSHHO achieved highly competitive outcomes in terms of objective values, feature subset size, and classification performance in most datasets, reaching an average accuracy of 94.47%. Full article

Soil pH is a critical environmental determinant of seed germination, seedling establishment, and ultimately crop yield. However, the physiological and molecular mechanisms underlying pH stress-mediated inhibition of germination and early seedling development remain poorly understood. Here, tomato was employed as a model system to systematically evaluate the dose-dependent effects of pH stress (ranging from pH 3.5 to 10.5) on germination performance and post-germinative growth. Our results demonstrate that both acidic and alkaline conditions significantly suppressed germination parameters in a pH intensity-dependent manner. Concurrently, seedling growth was markedly inhibited, root and hypocotyl elongation declined progressively, and total seedling biomass decreased substantially. Exposure to acidic (pH 3.5) or alkaline (pH 9.5) stress reduced seed viability and triggered a robust reactive oxygen species (ROS) burst and cell death. Biochemical assays revealed that acidic and alkaline stress disrupted redox homeostasis by compromising the coordinated activity of antioxidant enzymes, elevating membrane lipid peroxidation, and impairing osmotic adjustment capacity. Furthermore, acid and alkaline stress-induced inhibition of root growth coincided with diminished root cell viability and reduced endogenous auxin accumulation. Gene expression analyses showed that acidic and alkaline stress downregulated auxin biosynthesis genes and cell wall-associated genes involved in extension and modification, including EXPs and XTHs. Notably, IAA priming effectively rescued germination and early seedling growth under alkaline stress. Collectively, these findings elucidate a mechanistic framework linking pH-induced oxidative damage, auxin deficiency, and cell wall remodeling to impaired seed germination and seedling establishment and identify IAA priming as a physiologically grounded strategy to enhance crop resilience in alkali-affected marginal soils. Full article

In recent decades, humanoids have become more popular in various applications. However, their applications in human life are more than those in industry. In this paper, a humanoid is used to capture the sets of point clouds of an object for three-dimensional reconstruction. The structured light camera is widely used across diverse 3D scanning applications due to its high resolution, rapid acquisition capability, and adaptability to various material surfaces. Therefore, the humanoid developed by our team holds a structured light camera which captures the point clouds of an object put on a platform for the reconstruction of its 3D digital model. The platform is rotated so that the structured light camera can capture the image of all view angles on the object. Meanwhile, the structured light camera captures point clouds, and the camera of the humanoid recognizes the QR code on the platform so that the sets of point clouds can be distinguished by view angles on the object. Then, the automated registration process of the point cloud sets for a 3D model based on the point-to-plane iterative closest point (ICP) algorithm is proposed. The process incorporates preprocessing techniques, such as downsampling and normal vector estimated from plane, and utilizes the ICP algorithm for registration, ultimately achieving markerless and precision automatic merging of multi-view point cloud data. Experimental results demonstrate that the proposed method with the humanoid can effectively improve the completeness and accuracy of 3D reconstruction models, significantly reduce manual intervention, and enhance the system’s versatility and practical feasibility. Key parameters adjusted for more efficient computation of the ICP algorithm are revealed. In addition, the experimental results of the proposed ICP compared with G-ICP are also included. Full article

Internalized weight bias—the self-directed endorsement of weight-related stereotypes—has emerged as a psychologically potent correlate of health outcomes in individuals with overweight and obesity, yet its relationship with sleep remains largely unexplored. In a cross-sectional manner, 495 Greek adults with a history of overweight/obesity were assessed regarding sleep quality and duration, internalized weight bias (Modified Weight Bias Internalization Scale; WBIS-M), and expressed anti-fat attitudes (Anti-Fat Attitudes Questionnaire, AFA: Dislike, Fear of Fat, Willpower). Insomnia prevalence, assessed with the Athens Insomnia Scale (AIS), was high at 57.6%—nearly doubling across ascending WBIS-M tertiles (39.9% to 73.1%). In hierarchical linear regression models, AIS score remained independently associated with WBIS-M after adjustment for depression, anxiety, BMI, and a comprehensive range of sociodemographic and clinical covariates (B = 0.058; 95% CI: 0.036–0.079; p < 0.001), with the fully adjusted model explaining 58.5% of total variance in WBIS-M. AFA subscales did not remain significant in the model post-full adjustment, and sleep duration failed to show independent association with either bias dimensions. The sleep–weight bias association was therefore specific to the internalized dimension and to sleep quality, rather than quantity. These findings highlight a clinically relevant and dimension-specific link between insomnia symptoms and internalized weight stigma, and suggest that routine sleep assessment may be warranted in individuals with a history of overweight or obesity presenting with elevated internalized weight bias—and vice versa. Full article

Background: The increasing prevalence of diabetes mellitus and metabolic risk factors among young adults has become a major public health concern. University students are particularly vulnerable to unhealthy lifestyle changes, including sedentary behavior, insufficient physical activity, and academic stress, all of which may be associated with an elevated risk of metabolic disorders. Objective: This study aimed to examine the associations of academic stress, physical activity, and sedentary behavior with diabetes risk among university students. Methods: A cross-sectional analytical study was conducted among 264 university students recruited through an online survey. Academic stress was assessed using a six-item Likert-scale instrument, while diabetes risk was evaluated using a composite score derived from indicators adapted from the modified Finnish Diabetes Risk Score (modified FINDRISC). Statistical analyses included descriptive statistics, Cronbach’s alpha reliability testing, exploratory factor analysis (EFA), Spearman’s correlation analysis, and multivariable logistic regression. Results: The academic stress instrument demonstrated good internal consistency (Cronbach’s alpha = 0.85). Exploratory factor analysis supported the construct validity of the instrument, with all six items loading substantially on a common academic stress factor. Correlation analysis revealed that academic stress was positively associated with sedentary behavior and diabetes risk, whereas physical activity was negatively associated with diabetes risk. Multivariable logistic regression showed that academic stress was significantly associated with an increased risk of diabetes (adjusted odds ratio [aOR] = 1.18, 95% confidence interval [CI]: 1.02–1.36; p = 0.028). Moderate-to-vigorous physical activity was associated with a lower risk of diabetes (aOR = 0.74, 95% CI: 0.60–0.92; p = 0.011), while longer sitting duration was associated with an increased risk of diabetes. Conclusions: Academic stress, sedentary behavior, and physical activity were significantly associated with diabetes risk among university students. These findings highlight the importance of developing university-based health promotion programs that integrate stress management, physical activity promotion, and sedentary behavior reduction to support the prevention of metabolic risk factors in young adults. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

Accurate prediction of wear in lubricated metal-to-metal contacts remains a critical challenge, as calibration parameters derived from laboratory tests often lack transferability to finite element method (FEM) simulations. While classical linear Archard models are widely applied, they fail to capture the nonlinear load-dependent wear behavior observed under varying operating conditions. This study addresses this limitation by developing and validating a nonlinear wear formulation based on the Archard–Bayer law within a coupled experimental–numerical framework. A comprehensive Pin-on-Disc test matrix was conducted under lubricated conditions using carbide–steel contacts across varying loads and cycle counts. Wear progression was quantified and analysed using outlier-corrected weighted regression, yielding a force exponent $m_{exp}=1.58\pm 0.34$ and cycle exponent $n_{exp}= 0.41 \pm 0.17$. The calibrated nonlinear model was implemented in a FEM environment and systematically evaluated across multiple loading scenarios. The nonlinear formulation demonstrates improved predictive capability compared to the classical linear Archard model, particularly under higher load conditions (15 N–20 N), where deviations between simulation and experiment remain below 11%. The FEM-calibrated exponent (m = 1.35) lies within the 95% confidence interval of the experimental value, indicating that numerical adjustments required for stability are statistically non-significant. The results show that nonlinear wear models provide a more accurate representation of load-dependent wear behavior but require constrained calibration ranges for reliable application. The proposed methodology enables robust transfer of experimentally derived wear parameters into FEM simulations and provides a practical basis for tool-life prediction, parameter tuning, and model deployment in sheet metal forming processes. Full article