Search Results (24)

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Background/Objectives: Accurate preoperative discrimination between women with ovarian pathology and healthy controls, as well as between benign and malignant ovarian tumors, remains challenging. This study aimed to evaluate the usefulness of osteopontin and galectin-7 on the diagnosis of ovarian tumors. Methods: This prospective single-center case–control study was conducted at the Third Department of Obstetrics & Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, between 2018 and 2024. Preoperative serum levels of osteopontin, galectin-7, and established tumor markers (CA-125, CA19-9, CA15-3, CEA, AFP) were analyzed. Biomarker distributions were compared using non-parametric tests. Associations with clinical variables were explored using correlation analyses. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. Results: The study population included 116 women: 52 healthy controls, 45 patients with benign ovarian tumors, and 19 patients with malignant ovarian tumors. Serum osteopontin and galectin-7 levels did not differ significantly between control and study group (p = 0.562 and p = 0.138, respectively), nor between benign and malignant tumors (p = 0.784 and p = 0.140, respectively). Osteopontin showed no discriminatory ability (AUC = 0.47), while galectin-7 demonstrated weak discrimination (AUC = 0.63). A combined model yielded modest improvement (AUC = 0.69), remaining below clinically meaningful thresholds. CA-125 was the only biomarker significantly associated with malignancy (OR = 1.03, p = 0.038). Galectin-7 levels were higher in premenopausal women and inversely correlated with age, suggesting demographic rather than malignant influence. Conclusions: Despite strong biological relevance, circulating osteopontin and galectin-7 did not provide meaningful diagnostic discrimination between women with ovarian pathology and healthy controls or between benign and malignant ovarian tumors. CA-125 remained the most informative serum marker in this setting. Future efforts should focus on multi-marker strategies integrated with imaging and clinical assessment. Full article

In this study, we develop a regression analysis method, namely, the Semiparametric Binary Logistic Regression (SBLR), by extending the classical logistic regression that integrates both parametric and nonparametric components, which allows it to simultaneously model linear and non-linear relationships. Here, to obtain the estimation of a nonparametric component in the form of a non-linear curve (sigmoid curve), we use the penalized spline, which is a smoothing technique used in the nonparametric approach due to its ability to produce smooth and adaptive curves for fluctuating data. In this smoothing technique, selecting the optimal smoothing parameters plays an important role in fitting the model. Commonly, this selection is based on the minimum value of ordinary Cross-Validation (CV) or Generalized Cross-Validation (GCV). However, these CV and GCV criteria cannot be used when the CV and GCV curves continuously decline and never rise; the minimum CV and GCV values would not be achieved because they are not directly applicable due to the non-quadratic nature of the log-likelihood function. Therefore, a Generalized Approximate Cross-Validation (GACV) criterion is used to address such cases. This distinguishes it from previous studies that used the CV or GCV criterion. In the application to real data, we define an SBLR model of Coronary Heart Disease (CHD) risk factors that can be used for prediction and interpretation purposes. The results of the study successfully demonstrate the efficacy of the proposed method in identifying critical non-linear thresholds for CHD risk factors, and it is statistically valid and highly effective for CHD risk prediction. In the future, we can use the results of this research as a basis of an early warning system, specifically alerting individuals with moderate stress levels and dietary habits exceeding the identified thresholds to be aware of the heightened probability of developing CHD. In addition, this research aligns with point three of the Sustainable Development Goals (SDGs), namely, premature mortality reduction from non-communicable diseases by 2030. Full article

Laryngeal squamous cell carcinoma (SCC) remains a major clinical challenge due to substantial mortality and limited preoperative risk stratification. Narrow-Band Imaging (NBI) enables real-time visualization of mucosal microvasculature, yet the molecular correlates of high-risk NBI phenotypes in vocal fold lesions are incompletely defined. In a prospective cohort of 145 patients with vocal fold lesions, NBI microvascular patterns were graded using the Ni classification and dichotomized using a pre-specified high-risk threshold (Ni ≥ 4 vs. Ni ≤ 3). Histopathology was classified according to WHO 2017. Epithelial expression of IL-6, LIF, LIFR and CXCL9 was quantified by immunohistochemistry using the immunoreactive score (IRS). Associations were tested using non-parametric methods and logistic regression, and diagnostic performance was assessed by ROC analysis. SCC was diagnosed in 63/145 cases. The Ni category showed a strong stepwise association with WHO 2017 histopathological severity. Using Ni ≥ 4, diagnostic performance for SCC was balanced (sensitivity 82.5%, specificity 82.9%; accuracy 82.8%). LIF and LIFR expression decreased with increasing histopathological severity and higher-NBI-risk categories, whereas CXCL9 increased with more suspicious NBI patterns; epithelial IL-6 did not differ across lesion categories. In multivariable logistic regression, Ni ≥ 4 was the strongest independent predictor of SCC (adjusted OR 8.90), while higher LIF (adjusted OR 0.73) and LIFR (adjusted OR 0.78) were independently associated with lower odds of SCC (model AUC 0.943). Multivariable analysis confirmed NBI as the strongest independent predictor of carcinoma, while epithelial LIF and LIFR expression showed inverse associations with histological malignancy and high-risk NBI vascular patterns. LIF/LIFR and CXCL9 show distinct, biologically plausible associations with NBI risk phenotypes, suggesting that selected tissue markers may complement NBI for refined SCC risk stratification. Full article

Background: Clear aligner therapy (CAT) represents a biomimetic orthodontic approach that uses flexible thermoplastic materials to reproduce the physiological tooth movement and mechanical load distribution of natural tissues. While these materials promote oral hygiene and aesthetic comfort, their long-term biological impact on the caries process remains uncertain. This retrospective study aimed to evaluate changes in the number of decayed teeth (ΔD) before and after clear aligner treatment and to identify duration-dependent risk factors. Methods: This retrospective study included 362 patients (279 females, 83 males) treated with Invisalign^® aligners between 2020 and 2024. Baseline and post-treatment panoramic radiographs were analyzed to determine decayed tooth counts. Age, sex, and total aligner count were recorded. Non-parametric tests, multivariable regression, and ROC analysis were used to assess predictors of ΔD. Results: The mean number of decayed teeth increased slightly from 3.54 ± 2.76 to 3.83 ± 2.93 (p < 0.001). Longer treatment duration was independently associated with caries progression (β = +0.0088 per tray, p = 0.0037), and each 10-tray increment increased the odds of new decay by 55% (OR = 1.55, 95% CI: 1.26–1.90). ROC analysis identified ≥42 trays as a clinically relevant threshold (AUC = 0.67). Conclusions: Clear aligner therapy demonstrated a statistically significant yet clinically small increase in caries incidence, primarily related to treatment duration. As a biomimetic orthodontic approach that integrates mechanical and biological dynamics, extended clear aligner use may alter biofilm–surface interactions and salivary conditions over time. Therefore, preventive strategies–such as professional fluoride applications, strict cleaning protocols, and shorter recall intervals–should be emphasized for long-duration treatments to preserve the biological benefits of this biomimetic system. Full article

Background and Objectives: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) present overlapping clinical and neuroanatomical features, complicating early diagnosis. Therefore, this study evaluated whether EEG microstate analysis can provide reliable markers to distinguish patients with dementia from healthy controls. Materials and Methods: Resting-state EEG was recorded from 36 AD patients, 23 FTD patients, and 29 healthy controls. Preprocessing and microstate analysis were conducted using the MICROSTATELAB pipeline in EEGLAB. Clustering solutions ranging from four to seven classes were tested, with grand mean fitting and variance thresholds. Temporal parameters (duration, occurrence, and coverage) and their ratio-normalized forms were compared across groups using ANCOVA and nonparametric tests. Associations with Mini-Mental State Examination (MMSE) scores were assessed by regression analyses. Results: The four- and seven-class clustering solutions achieved high variance overlap with published microstate templates. In the four-class solution, temporal parameters of microstates B and D significantly differentiated controls from dementia groups, while in the seven-class solution, microstates C and G were the most informative. Ratio-normalized parameters improved group discrimination and were associated with MMSE scores. Conclusions: EEG microstates capture disease-related alterations in large-scale brain dynamics that differentiate patients with dementia from healthy individuals. Full article

The relationship between temperature and mortality is well-documented, yet most existing studies assume this relationship remains static over time. This study investigates whether the temperature-mortality association in Chicago from 1987 to 2000 has changed in shape or location of key features, such as change points. We apply nonparametric regression techniques to estimate the temperature-mortality functions for each year using daily mortality and temperature data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) database. A permutation-based test is used to assess whether the shapes of these functions differ across time, while a bootstrap procedure evaluates the consistency of change points. Intensive simulation studies are conducted to evaluate the permutation-based test and bootstrap procedure based on Type I error and power. The proposed tests are compared with F tests in terms of Type I error and power. For the real data set, the analysis finds significant variation in the functional shapes across years, indicating evolving mortality responses to temperature. However, the estimated change points—temperatures associated with peak mortality—remain statistically consistent. These findings suggest that while the population’s overall vulnerability pattern may shift, the temperature threshold linked to maximum mortality has remained stable. This study contributes to understanding the temporal dynamics of climate-sensitive health outcomes and highlights the importance of flexible modeling in public health and climate adaptation planning. Full article

Introduction: Infertility affects 15% of couples, with oxidative stress recognized as a key contributor to male infertility. Night-shift work, through circadian disruption, may exacerbate oxidative imbalance and impair reproductive function. This study investigates the impact of night-shift work on oxidative stress and semen quality and evaluates the potential benefits of antioxidant supplementation in this context. Materials and Methods: We retrospectively analysed 96 white-European men aged 18–45, seeking fertility assessment at a single academic centre. Participants were classified as day or night workers based on their shift schedule, and all underwent standardised clinical, hormonal, and semen evaluations. Oxidative stress was assessed using the d-ROMs test. A subgroup of 40 patients (20 per group) treated for 3 months with antioxidant supplementation (Drolessano) to evaluate changes in oxidative stress and semen parameters was also considered. Statistical comparisons were performed using non-parametric tests and logistic regression analyses. Results: Night-shift workers exhibit significantly higher oxidative stress levels compared to day workers (median D-ROMs values of 340 vs. 280 U.CARR, p = 0.01), and a greater proportion of men exceeding the oxidative stress threshold (74.4% vs. 24.4%, p = 0.01). Logistic regression confirmed night-shift work as an independent predictor of elevated oxidative stress (OR 2.1, p = 0.001), even after adjusting for age and smoking. Following three months of antioxidant supplementation with Drolessano, both groups experienced significant reductions in oxidative stress (all p < 0.01), but night workers showed a substantially greater decrease (mean change −58.5 vs. −15.4 U.CARR, p = 0.001). Improvements in semen quality, including sperm concentration, motility, and morphology, were also more pronounced in the night group after treatment. Conclusions: At baseline, night-shift workers had significantly higher oxidative stress than day workers, likely due to circadian disruption. Both groups improved after antioxidant treatment, but night workers showed a greater reduction in D-ROMs. This pilot study might suggest a potential benefit of antioxidant therapy particularly in night workers. Full article

We investigated the excessive external financing problem in Chinese industrial firms by examining the potential threshold effect of the leverage ratio on the total factor productivity of firms. We hypothesized the existence of a turning point in leverage ratio at which the productivity of these firms is maximized, and this point may vary by firm ownership type. To test our hypotheses, we proposed a nonparametric panel threshold regression model. From a modeling perspective, our approach contributes to the literature by allowing the threshold variable to be endogenous, accounting for unobserved firm heterogeneities, and imposing no restrictions on the functional form of regression. We employed a two-step estimation procedure, first estimating the threshold using an extreme kernel estimator and then conducting local linear regression based on the estimated threshold. We obtained standard errors via bootstrapping and demonstrated the favorable numerical performance of our estimator through simulation studies. Consistent with our hypotheses, we found that excessive leverage relative to the identified turning point significantly restrains productivity growth. Additionally, the estimated turning point varies by ownership type, particularly in state-owned enterprises (SOEs), where leverage exceeding the threshold negatively impacts productivity. Consequently, regions with a higher concentration of SOEs experience stagnant productivity growth. Our results were statistically supported by nonparametric tests and remained consistent when using leverage growth rate as an alternative measure of external financing. Full article

Wavelet analysis algorithms in combination with thresholding procedures are widely used in nonparametric regression problems when estimating a signal function from noisy data. The advantages of these methods lie in their computational efficiency and the ability to adapt to the local features of the estimated function. It is usually assumed that the signal function belongs to some special class. For example, it can be piecewise continuous or piecewise differentiable and have a compact support. These assumptions, as a rule, allow the signal function to be economically represented on some specially selected basis in such a way that the useful signal is concentrated in a relatively small number of large absolute value expansion coefficients. Then, thresholding is performed to remove the noise coefficients. Typically, the noise distribution is assumed to be additive and Gaussian. This model is well studied in the literature, and various types of thresholding and parameter selection strategies adapted for specific applications have been proposed. The risk analysis of thresholding methods is an important practical task, since it makes it possible to assess the quality of both the methods themselves and the equipment used for processing. Most of the studies in this area investigate the asymptotic order of the theoretical risk. In practical situations, the theoretical risk cannot be calculated because it depends explicitly on the unobserved, noise-free signal. However, a statistical risk estimate constructed on the basis of the observed data can also be used to assess the quality of noise reduction methods. In this paper, a model of a signal contaminated with additive Gaussian noise is considered, and the general formulation of the thresholding problem with threshold functions belonging to a special class is discussed. Lower bounds are obtained for the threshold values that minimize the unbiased risk estimate. Conditions are also given under which this risk estimate is asymptotically normal and strongly consistent. The results of these studies can provide the basis for further research in the field of constructing confidence intervals and obtaining estimates of the convergence rate, which, in turn, will make it possible to obtain specific values of errors in signal processing for a wide range of thresholding methods. Full article

(1) Background: A low socioeconomic status significantly increases the risk of hypertension and its associated cardiovascular diseases due to limited access to healthcare and may be even more accentuated by the presence of unhealthy lifestyle habits. The aim of the present research was to study if associations exist between having a family income under the poverty threshold and having an unhealthy diet, being physically inactive, being an alcohol drinker, perceiving one’s own health as bad, and suffering from congestive heart failure, coronary heart disease, angina pectoris, heart attack, or stroke. Additionally, the odds ratios of having these unhealthy habits and of suffering from the abovementioned cardiac complications of participants under the poverty threshold were calculated. (2) Methods: This cross-sectional study was based on the National Health and Nutrition Examination Survey (NHANES) 2011–2020. The sample comprised 6120 adults with hypertension (3188 males and 2932 females). A descriptive analysis and non-parametric chi-squared tests were used to study the associations. A binary logistic regression model and backward LR method were used to calculate the odds ratios, normalized by age and sex. (3) Results: The chi-squared test showed associations between having a family income under the poverty threshold and being physically inactive (p < 0.001), having an unhealthy diet (p < 0.001), being an alcohol drinker (p < 0.001), perceiving one’s own health as bad (p < 0.001), and suffering from congestive heart failure (p = 0.002), heart attack (p = 0.001), or stroke (p = 0.02). A significantly increased odds ratio for these unhealthy habits and cardiac complications, and also for having coronary heart disease and angina pectoris, were found for hypertension sufferers under the poverty threshold. (4) Conclusions: It was confirmed that having a family income under the poverty threshold is associated with perceiving one’s own health as bad, having a series of negative habits in terms of physical activity, diet, and alcohol consumption, and with suffering from congestive heart failure, heart attack, or stroke. Increased odds ratios for these unhealthy habits and these conditions, plus coronary heart disease and angina pectoris, were found for hypertension sufferers under the poverty threshold. Full article

Scientific and technological innovation is the main driving force of the growth in the 14th Five-Year Plan with the aim of “carbon peaking and neutralization.” This research analyzes the carbon total factor productivity (CTFP) improvement mechanism induced by micro-subject technological innovation and macro-technological progress (TP). This research constructed the Malmquist index based on a relaxed nonparametric DEA model, measured the TP level and CTFP in China, and considered the non-strict externalization of technological progress. The endogenous dynamic threshold model was used to test the nonlinear dynamic effect of TP driving the increase in CTFP. Through the intertemporal distance DEA model, undesired output model, and dynamic threshold regression model, we found that science and technology innovation of the TP drive the function of the carbon total factor productivity; there was a threshold effect (−0.556) on the driving impact of TP caused by technological innovation on CTFP, and the lag period of TP and CTFP had a positive driving role for CTFP. The driving effect on the left side of the threshold value was better than that on the right side. Considering the reality of slowing down the growth of capital and labor factor input in the 14th Five-Year Plan, it is essential to take active policy measures to promote the growth rate of TP by promoting the speed of micro-scientific and technological innovation. It is crucial to promote green TP in micro renewable energy enterprises, which, in turn, drive the growth of CTFP, improve the performance of low-carbon development, and reduce the negative impact of the “two-carbon” target on economic growth while realizing low-carbon transition. Full article

Infrared small target detection, especially under low SCR conditions and complex backgrounds, is still a challenging research task. Considering the scale change caused by small targets rapidly moving, in this paper, a nonparametric regression-based multi-scale gradient correlation filtering (MGCF) detection method is proposed. First, a nonparametric regression method is applied to calculate the gradient of each point. Then, based on the unique gradient characteristics of small targets, a multi-scale gradient correlation (MGC) template is designed to distinguish small targets from clutter. After that, a multi-scale gradient correlation filtering method is proposed to enhance the target intensity and suppress clutter. At last, based on the obtained filtering response, an adaptive threshold segmentation method is adopted to extract real small targets. Experimental results demonstrate that the proposed method can fully improve the signal-to-clutter ratio (SCR) of small targets under different complex backgrounds. Moreover, compared with other baseline methods, the proposed method exhibits excellent detection performance. Full article

This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with asymmetric-threshold GARCH models for marginal distributions, detects the directional dependence better than the copula directional dependence method by an asymmetric GARCH model. Full article

Estimating the impact of climate change risks on rice distribution is one of the most important elements of climate risk management. This paper is based on the GEE (Google Earth Engine) platform and multi-source remote sensing data; the authors quantitatively extracted rice production distribution data in China from 1990 to 2019, analysed the evolution pattern of rice distribution and clusters and explored the driving effects between climatic and environmental conditions on the evolution of rice production distribution using the non-parametric quantile regression model. The results show that: The spatial variation of rice distribution is significant, mainly concentrated in the northeast, south and southwest regions of China; the distribution of rice in the northeast is expanding, while the distribution of rice in the south is extending northward, showing a spatial evolution trend of “north rising and south retreating”. The positive effect of precipitation on the spatial distribution of rice has a significant threshold. This shows that when precipitation is greater than 800 mm, there is a significant positive effect on the spatial distribution of rice production, and this effect will increase with precipitation increases. Climate change may lead to a continuous northward shift in the extent of rice production, especially extending to the northwest of China. This paper’s results will help implement more spatially targeted climate change adaptation measures for rice to cope with the changes in food production distribution caused by climate change. Full article