Search Results (177)

Background: The hemoglobin-to-red-cell distribution width ratio has emerged as a novel prognostic marker in various clinical settings. However, its association with total bone mineral density in adolescents remains inadequately explored. Methods: This cross-sectional study was based on data from the 2011–2018 National Health and Nutrition Examination Survey, including adolescents aged 12–19 years with complete data on hemoglobin, red cell distribution width, and total bone mineral density. Weighted multivariable linear regression models and generalized additive models were used to evaluate the association between hemoglobin-to-red-cell distribution width and total bone mineral density. A two-piecewise linear regression model was applied to assess potential threshold effects, with log-likelihood ratio tests used to determine the significance of inflection points. Subgroup and interaction analyses were further conducted to examine whether age, sex, race, and milk product consumption modified this association. Results: A total of 3789 adolescents were included. Participants in the highest hemoglobin-to-red-blood-cell distribution width ratio quartile had significantly higher hemoglobin levels, lower red blood cell distribution width, greater total bone mineral density, higher total calcium and blood urea nitrogen levels, and lower body mass index, high-density lipoprotein cholesterol, and serum 25OHD levels compared to lower quartiles. The hemoglobin-to-red-blood-cell distribution width ratio was positively associated with total bone mineral density (fully adjusted β = 0.078, 95% CI: 0.053, 0.104, p < 0.0001). A two-piecewise linear regression model identified an inflection point at the hemoglobin-to-red-cell distribution width ratio = 1.055; the positive association became stronger above this threshold (β = 0.143 vs. β = 0.039 below the threshold, p = 0.003 for nonlinearity). Subgroup analysis revealed significant gender interactions (p < 0.0001). A higher HRR was significantly associated with greater total BMD in males (β = 0.130, 95% CI: 0.089–0.171, p < 0.0001), whereas no significant association was observed in females (β = −0.009, 95% CI: −0.043–0.025, p = 0.604). Positive associations were also observed among participants aged 12–15 years, non-Hispanic Whites, non-Hispanic Blacks, other Hispanics, Mexican Americans, and frequent milk consumers. Conclusions: Our results indicate that the hemoglobin-to-red-cell distribution width ratio shows a potential association with bone mineral density in male adolescents, which may offer supportive value for bone health assessment but requires further validation. Full article

Background: Globally, uncorrected refractive errors are recognized as the primary cause of visual impairment and blindness. According to a report by the World Health Organization (WHO), providing spectacle lenses at an affordable cost remains a significant challenge, particularly for underprivileged populations in developing countries. This challenge contributes to the low compliance with spectacle wear worldwide. However, the benefits of wearing spectacles are influenced by the perceptions of the population regarding spectacle use. Methods: A quantitative, cross-sectional survey-based study was conducted at a superior educative center in Oman, the University of Buraimi. Participants were recruited from the four major colleges, namely, the College of Health Sciences (COHS), College of Business (COB), College of Engineering (COE), and College of Law (COL), and the Center for Foundation Studies (CFS). This study was conducted over the period from 18 December 2022 to 18 December 2023. Essential data were collected using an electronic questionnaire facilitated by the Google platform. The initial section of the questionnaire outlines this study’s objectives and its benefits to the community. The digital survey comprises three sections: the first section addresses the sociodemographic profile of the participants; the second section explores perceptions related to spectacles; and the third section examines visual symptoms associated with spectacle wear. In this study, a pre-tested survey was administered following consultation with a panel of three subject matter experts who reviewed the clarity and content validity of the test items. Data analyses were performed using descriptive statistics, and linear regression was applied to assess the effect of socioeconomic profile on perceptions of spectacles. Additionally, data entry, processing, and analysis were conducted using SPSS 25 software. The overall mean score for spectacle-related visual symptoms was 2.51 ± 0.75, indicating a moderate level of symptom occurrence. Results: A total of 415 participants (N = 415) were included in this study, comprising 133 males (32.0%) and 282 females (68.0%). The most prominent symptoms related to spectacle perception were “light sensitivity” and “eye pain”, with mean values of 3.03 ± 1.30 and 3.04 ± 1.25, respectively. Additionally, 249 participants (60%) reported moderate concern regarding spectacle-related visual symptoms. Among female participants, 118 (41.8%) exhibited little concern about visual symptoms associated with spectacle wear, whereas this was observed in 25.6% of male participants. Descriptive statistics indicated the mean perceived spectacle-related disadvantages score measured on a scale of 0 to 4 was 2.88 ± 1.16 (57.69% ± 23.15% in percentages), reflecting a moderate perception of such disadvantages. The linear regression model demonstrated statistical significance, as indicated by the likelihood ratio chi-square = 199.194 (df = 15, p < 0.001). The most significant predictor was study major (χ² = 72.922, p < 0.001). Conclusions: The present study indicates that undergraduate students generally exhibit a low perception of the disadvantages associated with wearing spectacles. Randomized sampling should be preferred in future studies to the convenience sampling technique. The most frequently reported visual symptoms include “light sensitivity and eye pain” among spectacle wearers. Therefore, it is imperative to implement health education programs and foundational studies across colleges to address these issues among undergraduate university students. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

Large Language Models are often fine-tuned on proprietary corpora, motivating reliable provenance signals. A corpus-level watermark method is proposed for fine-tuning datasets that survives training and common text transformations. The method subtly biases synonym choices according to a secret key (PRF) and encodes a multi-bit payload with an error-correcting code, enabling keyed detection via a generalized likelihood ratio test with permutation-calibrated p-values. For short offline passages (~100 words), the channel is valid but statistically underpowered: the average density is ~0.0165, and the median p-value is close to 1.0. In generative tests with Mistral 7B across 12 configurations and 12,720 texts, 0.00% detection was observed at very high quality (~99.8%). As limited base cases, positive detection was reported for other setups: 8.9% (offline), 5.0% (Mistral 7B), and 3.0% (Llama2-13B). A permutation test (R = 5000), confidence intervals, and power analysis were added. Quality impact statements were refined, with “minimal impact” used instead of “imperceptible.” In this study, limitations and ethical use are discussed, and directions for stronger semantic channels and model-based detectors are outlined. Full article

Methane emissions (CH₄-em) from dairy cows are a major environmental concern, contributing to greenhouse gases and energy loss in dairy cows. This study implemented advanced data analysis techniques to understand how different diet ingredients and production traits in dairy production systems can affect methane emissions. We analyzed a comprehensive meta dataset compiled from 225 peer-reviewed studies including 303 observations across multiple traits, using Bayesian networks and various machine learning models to explore the relationships between MEs, diet chemical ingredients, and production traits in dairy cattle. Eight models were applied, including linear models (OLS, LASSO, ridge, elastic net) and non-linear models (PLSR, spline regression, support vector machine, Gaussian process), to assess predictive performance. CH₄-em showed correlations ranged from −0.43 (with diet starch; STR) to 0.50 (with neutral detergent fiber; NDF) for diet-related factors, and 0.18 (with body weight; BW) to 0.29 (with milk yield; MY) for production traits. Also, Bayesian network analysis indicated that CH₄-em was a downstream variable for diet-related factors and an upstream variable for production traits. Additionally, the likelihood ratio test identified NDF as significant variable among the diet-related factors, while MY and milk fat (FAT) were crucial for production traits. non-linear models, particularly spline regression (SPL) and Gaussian process (GP), outperformed linear models in predicting CH₄-em. For production traits, support vector machine (SVM) and GP models showed superior predictive capabilities. Model performance was evaluated using R² and mean squared error (MSE) metrics. We found that while larger cows emitted more methane overall, they were generally more efficient, as methane intensity decreased with increasing MY regardless of body size. These findings offer valuable insights for developing sustainable methane mitigation strategies in dairy cattle production. Full article

The rapid evolution of communication systems, exemplified by the Internet of Things (IoT), demands increasingly stringent reliability in both communication and sensing. While Orthogonal Frequency Division Multiplexing (OFDM) struggles to meet the challenges posed by complex scenarios, Orthogonal Chirp Division Multiplexing (OCDM) has gained attention for its robustness and spectral efficiency in Integrated Sensing and Communication (ISAC) systems. However, its sensing mechanism remains insufficiently explored. This paper presents a theoretical analysis of the communication and sensing performance of OCDM waveforms within the ISAC framework. Specifically, a closed-form BER expression under equalization is derived, alongside the ambiguity function and detection performance evaluation under matched filter (MF) and Generalized Likelihood Ratio Test (GLRT) detectors with a constant false alarm rate (CFAR) criterion. Simulation results demonstrate that OCDM offers comparable sensing performance to OFDM while achieving superior communication robustness in complex environments. Full article

Background: Chinese Red Steppe cattle (CRS) combine indigenous environmental resilience with moderate dairy performance, whereas Holstein cattle (HOL), despite their high milk yield, suffer reduced genetic diversity and compromised adaptation. A comparative analysis of their population genetic architecture and selection signatures can reveal valuable targets for CRS dairy improvement. Methods: We genotyped 61 CRS and 392 HOL individuals using the Illumina GGP Bovine 100K SNP array and performed stringent quality control. Population structure was assessed via principal component analysis, neighbor-joining trees, and sparse nonnegative matrix factorization. Historical effective population size (Ne) and divergence time were inferred with SMC++. Genome-wide selection scans combined Fixation Index (FST) and Cross-Population Composite Likelihood Ratio test (XP-CLR); overlapping high-confidence regions were annotated and subjected to GO and KEGG enrichment analyses. Results: CRS and HOL were clearly separated along PC1 (explaining 57.48% of variance), with CRS exhibiting high internal homogeneity and weak substructure, versus greater diversity and complex substructure in HOL. SMC++ indicated a split approximately 3500 years ago (700 generations) and a pronounced recent decline in Ne for both breeds. Joint selection mapping identified 767 candidate genes; notably, the ACSM1/2B/3/4 cluster on chromosome 25—key to butanoate metabolism—showed the strongest signal. Enrichment analyses highlighted roles for proteasome function, endoplasmic reticulum stress response, ion homeostasis, and RNA processing in regulating milk fat synthesis and protein secretion. Conclusion: This study delineates the genetic divergence and demographic history of CRS and HOL, and pinpoints core genes and pathways—particularly those governing butanoate metabolism and protein quality control—underlying dairy traits. These findings furnish molecular markers and theoretical guidance for precision breeding and sustainable utilization of Chinese Red Steppe cattle. Full article

Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited fault data. The presence of random interfering sounds and variability in operating conditions can lead to lower performance and high false-positive rates. To overcome these limitations, we propose a fault detection method that leverages the underlying physical characteristics of the sound signals. By investigating the components of the acoustic signal, we found that fault-related sounds can be modeled as exponentially decaying oscillations. This insight allows for the development of a physically based signal model, setting our approach apart from purely data-driven methods. Using this model, we developed a robust detection method based on a Generalized Likelihood Ratio Test (GLRT). The effectiveness of this approach was validated using both synthetic and real-world data from a steel industry facility. Our results demonstrate that the proposed model-based approach provides superior performance compared to standard audio features, particularly in high-noise conditions. On real-world data, the GLRT-based approach outperformed all audio features, as clearly shown by the Receiver Operating Characteristic (ROC) analysis. Specifically, the Partial Area Under the Curve (pAUC) of the GLRT is more than twice that of the best-performing audio feature, demonstrating good detection at significantly lower-false-positive rates compared to audio features. Furthermore, simulations showed that our method maintains robust detection down to a Signal-to-Noise Ratio (SNR) of −13 $\mathrm{dB}$, significantly outperforming audio feature-based detection, which was limited to approximately −10 $\mathrm{dB}$. The physically informed nature of our model not only provides a more reliable and robust solution but also enables the method to be generalized to other industrial scenarios with similar fault properties, offering broader applicability for reliable acoustic condition monitoring. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

Background/Objectives: The optimal surgical management of perforated gastric cancer (PGC) in emergency settings remains controversial. Urgent upfront one-stage gastrectomy (1SG) and two-stage gastrectomy (2SG) with damage-control surgery followed by elective gastrectomy have been proposed. The aim of the present systematic review is to compare short- and long-term outcomes between 1SG and 2SG in the treatment of PGC. Methods: A systematic review and individual patient data (IPD) meta-analysis of studies reporting data of patients undergoing 1SG vs. 2SG for PGC was conducted. The time-dependent effects of surgical interventions were assessed using a likelihood ratio test. Hazard function plots were generated via marginal prediction. Results: Ten retrospective series (579 patients) were included. Overall, 482 patients (83%) underwent 1SG, while 97 patients (17%) were treated with 2SG. A trend toward better short-term oncological outcomes and safety profiles for 2SG compared to 1SG was observed. Long-term outcomes were comparable between 1SG and 2SG, and the IPD meta-analysis showed no statistically significant difference between the two approaches in terms of OS or hazard for mortality at all time points. A trend towards a higher hazard for mortality was observed for 1SG in the first 20 months postoperatively. Conclusions: Our analysis suggests that 1SG and 2SG yield comparable short-term outcomes, although 2SG may be associated with a lower medium-term mortality risk. Further research is needed to identify key factors to improve clinical judgments and decision-making in PGC. Full article

Paired data often arise in medical studies, with a correlation between responses of paired organs or parts. Under an intra-correlated model, this paper proposes a generalized linear model to investigate probable confounding factors of the individual response rates in paired data. The main link functions include logistic, log–log, complementary log–log, probit, and double exponential. The estimators of model parameters are calculated through the Newton–Raphson, quadratic lower bound, and Fisher bounded algorithms. Then, three tests (i.e., likelihood ratio test, Wald-type test, and score test) are constructed to analyze whether covariates significantly affect the response rate. Finally, the proposed methods are illustrated by numerical simulation and visual impairment data from Iran. Full article

This study aimed to identify the best model and method for the genomic selection of early growth traits in Inner Mongolian cashmere goats (IMCGs). Using data from 50,728 SNPs, the phenotypes (birth weight, BW; weaning weight, WW; daily weight gain, DWG; and yearling weight, YW) of 2256 individuals, and pedigree information from 14,165 individuals, fixed effects were analyzed using a generalized linear model. Four single-trait animal models with varying combinations of individual and maternal effects were evaluated using the ABLUP, GBLUP, and ssGBLUP methods. The best model was selected based on a likelihood ratio test. Five-fold cross-validation was used to assess the accuracy and reliability of the genomic estimated breeding values (GEBVs). Birth year and herd significantly affected BW (p < 0.05) and WW, DWG, and YW (p < 0.01), while sex, birth type, and dam age had highly significant effects on all traits (p < 0.01). Model 4, incorporating direct and maternal additive genetic effects, maternal environmental effects, and their covariance, was optimal. Additionally, ssGBLUP achieved the highest GEBV accuracy (0.61–0.70), outperforming the GBLUP and ABLUP methods. Thus, ssGBLUP is recommended for enhancing the genetic progress in IMCGs. Under the best method, the heritability estimates for BW, WW, DGW, and YW were 0.11, 0.25, 0.15, and 0.23, respectively. Full article

This paper develops tests for hypotheses concerning subvectors of parameters in models defined by moment conditions. It is well known that conventional tests such as Wald, Likelihood-ratio and Score tests tend to over-reject when the identification is weak. To prevent uncontrolled size distortion and introduce refined finite-sample performance, we extend the projection-based test to a modified version of the score test using implied probabilities obtained by information theoretic criteria. Our test is performed in two steps, where the first step reduces the space of parameter candidates, while the second one involves the modified score test mentioned earlier. We derive the asymptotic properties of this procedure for the entire class of Generalized Empirical Likelihood implied probabilities. Simulations show that the test has very good finite-sample size and power. Finally, we apply our approach to the veteran earnings and find a negative impact of the veteran status. Full article

Factorial invariance is critical for ensuring consistent relationships between measured variables and latent constructs across groups or time, enabling valid comparisons in social science research. Detecting factorial invariance becomes challenging when varying degrees of heterogeneity are present in the distribution of latent factors. This simulation study examined how changes in latent means and variances between groups influence the detection of noninvariance, comparing Bayesian and maximum likelihood fit measures. The design factors included sample size, noninvariance levels, and latent factor distributions. Results indicated that differences in factor variance have a stronger impact on measurement invariance than differences in factor means, with heterogeneity in latent variances more strongly affecting scalar invariance testing than metric invariance testing. Among model selection methods, goodness-of-fit indices generally exhibited lower power compared to likelihood ratio tests (LRTs), information criteria (ICs; except BIC), and leave-one-out cross-validation (LOO), which achieved a good balance between false and true positive rates. Full article

Although the theory of mixed-integer inference is well developed for GNSS parameter estimation, such is not yet the case for the validation and monitoring of mixed-integer GNSS carrier-phase models. It is the goal of this research to contribute to this field by introducing a class of mixed-integer model (MIM) tests for carrier-phase GNSS. Members from this class and their distributional properties are worked out for different model validation applications relevant to GNSS, such as detection, identification, significance testing, and integer testing. The power performance of the various tests is characterized, thereby showing how they are capable of significantly outperforming the customary ambiguity-float tests. Full article