Search Results (3,446)

Background: The influence of serum uric acid (SUA) on bone metabolism, as suggested by previous studies, remains a contentious issue. SUA plays a complex role in bone health and hypertension, making it challenging to discern its impact on the skeletal status of middle-aged and elderly hypertensive patients. This study aims to elucidate the effects of SUA on bone health, with a particular focus on its association with osteoporosis and the risk of fractures. Methods: Multiple linear regression analyzed SUA levels against bone mineral density (BMD) and future fracture risk. Additionally, multivariate logistic regression was used to examine the association between SUA and osteoporosis. Dose–response relationship analysis was conducted using generalized smooth curve fitting (GSCF) and restricted cubic spline (RCS) methods. Results: With the exception of the total femur region, SUA and BMD showed a positive connection. GSCF analysis revealed an inverted U-shaped relationship between SUA and BMD, alongside a U-shaped trend with FRAX scores. Moreover, RCS analysis indicated a U-shaped relationship between osteoporosis risk and SUA levels, with higher risks identified in the first and third tertiles compared to the second tertile. Conclusions: In individuals with middle-aged and older hypertension, SUA is substantially linked to bone health. The identification of an inverted U-shaped relationship with BMD and U-shaped relationships with FRAX scores and osteoporosis risk highlights the nuanced influence of SUA. These findings suggest that both low and high SUA levels may adversely affect bone health, emphasizing the need for further research. Full article

(1) Background: In the context of global population aging, how to enhance the health of older people has become a focus of attention in various fields. Although it is widely recognized that the effects of urban green space built environments on physical activity can substantially improve the health of older people, few studies have been conducted to understand the relationship between green spaces, physical activity, and the health of older people at the community level. This research gap has become a key issue hindering the sustainable development of health among the elderly. (2) Methods: This study used survey data from 1989 elderly individuals in Qingshan District, Wuhan, and applied multiple linear regression models to explore the relationship between community green space built environments and the overall intensity of physical activity, as well as the relationship with low, moderate, and high-intensity physical activity levels. (3) Results: The results show that education level, income level, health status, companionship, green view index, road cleanliness, and fitness facilities are positively correlated with the overall intensity of physical activity, while gender, age, self-assessed psychological stress, and road intersection density are negatively correlated with it. Companionship, green view index, road cleanliness, and recreational facilities are positively correlated with low-intensity physical activity levels among the elderly, while gender, income level, and fitness facilities are negatively correlated with them. Companionship, green view index, and road cleanliness are positively correlated with moderate-intensity physical activity among the elderly, while gender is negatively correlated with it. For high-intensity activities, education level and fitness facilities are positively correlated, while gender, self-assessed psychological stress, and road intersection density are negatively correlated. (4) Conclusions: Future research could expand the sample size while incorporating more longitudinal designs, expand the types of influencing factors, conduct more detailed classifications, and carry out broader data collection procedures to comprehensively analyze the effects of the community green space built environment on physical activity among older people, providing a stronger scientific basis for the formulation of healthy city policies. Full article

Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R² of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration. Full article

The most effective way to solve urban traffic congestion in mega cities is to develop rail transit, which is also an important strategy for sustainable urban development. Improving the service performance of rail transit equipment is the key to ensuring the sustainable operation of urban rail transit. Automatic fare collection (AFC) is an indispensable system in urban rail transit. AFC directly serves passengers, and its condition directly affects the sustainability and safety of urban rail transit. This study proposes remaining useful life (RUL) prediction framework for AFC systems. Firstly, it proposes the quantification of AFC health state based on health degree, and proposes a health state assessment method based on digital analog fusion, which compensates for the shortcomings of single data-driven or model driven health methods. Secondly, it constructs a multi feature extraction method based on multi-layer LSTM, which can capture long-term temporal dependencies and multi-dimensional feature, overcoming the limitation of low model accuracy because of the weak data features. Then, the SSA-XGBoost model for AFC RUL prediction is proposed, which effectively performs global and local searches, reduces the possibility of overfitting, and improves the accuracy of the prediction model. Finally, we put it into practice of the AFC system of Shanghai Metro Line 10. The experiment shows that the proposed model has an MSE of 0.00111 and MAE of 0.02869 on the test set, while on the validation set, MSE is 0.00004 and MAE is 0.00659. These indicators are significantly better than other comparative models such as XGBoost, random forest regression, and linear regression. In addition, the SSA-XGBoost model also performs well on R-squared, further verifying its effectiveness in prediction accuracy and model fitting. Full article

Microplastics (MPs) can serve as vectors for heavy metals in aquatic environments; however, the adsorption behavior of MPs on multiple heavy metal systems is still unclear. This study investigated the adsorption characteristics of biodegradable poly (butylene succinate) (PBS) for cadmium (Cd(II)) and arsenic (As(III)) in both single and binary systems. Adsorption isotherms were studied using the Linear, Langmuir, and Freundlich models, and further analysis of MPs adsorption characteristics was conducted using site energy distribution theory and density functional theory. The results indicate that the maximum adsorption capacities of PBS for Cd(II) and As(III) are 2.997 mg/g and 2.606 mg/g, respectively, with the Freundlich model providing the best fit, suggesting multilayer adsorption on heterogeneous sites. As(III) has a higher adsorption affinity for PBS than Cd(II), with a binding energy of −11.219 kcal/mol. Additionally, the adsorption mechanisms of Cd(II) and As(III) on PBS include electrostatic interactions and surface complexation, with the primary adsorption sites at the C=O of the carboxyl group and the hydroxyl group. The comprehension of interfacial interactions between biodegradable plastics and heavy metals is facilitated by a combination of theoretical calculations and experimental investigations. Full article

After the lifting of the COVID-19 pandemic restrictions, urban socio-economic development has been continuously recovering. Researchers’ attention to urban vitality recovery has increased. However, few studies have paid attention to the recovery and driving of urban vitality in university fringe areas. This study aims to address this gap by exploring the driving mechanisms of urban vitality recovery in the university fringe areas using both linear and nonlinear models. The results reveal the following: (1) The recovery of urban vitality in university fringe areas follows a distinct pattern where central urban areas with greater openness recover more rapidly, while university fringe areas farther from the city center with stricter management experience slower recovery. (2) The fitting coefficients of the student enrollment, school area, the density of various POIs, and opening hours are 0.0020, −0.0105, −0.0053, and 0.0041 respectively. These variables exhibit a more pronounced linear relationship, and the significance level is quite high. Recovery effects also express significant spatial heterogeneity. (3) Both university opening hours and school area show a nonlinear positive relationship with the urban vitality recovery of university fringe areas, demonstrating a clear threshold effect. This relationship is characterized by slow growth at lower values, rapid acceleration once a critical threshold is reached, and eventual stabilization at higher values. This study offers targeted strategies for urban planning, fostering more responsive and adaptive urban governance that aligns with the evolving needs of urban development. Full article

The Chang’e-6 mission achieved the first successful sample collection and return from the Moon’s far side. Accurate alignment detection of the primary packaging container is critical for the success of this mission, as it ensures proper retrieval of lunar soil. To address challenges such as complex backgrounds, uneven lighting, and reflective surfaces, this paper introduces an alignment detection method that integrates YOLO object recognition, Devernay subpixel edge detection, and the RANSAC fitting algorithm. By employing both linear and elliptical fitting techniques, the method accurately determines the median line of the primary packaging container, ensuring precise alignment detection. The effectiveness of this approach is demonstrated by an average alignment distance of 0.28 mm with a standard deviation of 0.03 mm in lunar surface images, underscoring its accuracy and reliability. Full article

This study evaluated the feed digestibility of diets including autotrophic Chlorella (C.) vulgaris in 252 male broilers (Ross 308), comparing unprocessed biomass (trial 1) and pulsed electric field (PEF) processed biomass (trial 2) at inclusion levels up to 20%. In trial 2, performance and meat color were also evaluated. Each trial included seven treatments (0%, 1%, 2%, 5%, 10%, 15%, and 20% (%w/w on dry matter (DM)) C. vulgaris) with six replicates (three birds per replicate) per treatment. Data were analyzed using linear, quadratic, and broken-line models. Control feeds without microalgae inclusion achieved a crude protein digestibility of 82.04 ± 1.42% (trial 1) and 81.63 ± 1.90% (trial 2), while feed with 20% non-processed microalgae inclusion only had a protein digestibility of 66.96 ± 1.16% (trial 1) and feed with PEF processed microalgae at 20% had a protein digestibility of 72.75 ± 0.34% (trial 2). In general, increasing inclusion levels of C. vulgaris impaired nutrient digestibility, significantly reducing crude protein, crude fat, gross energy, and crude ash digestibility (p < 0.001). Broken-line models identified critical inclusion thresholds beyond which digestibility declined significantly, i.e., at 10% for crude protein, 12.53% for crude fat, and 9.26% for gross energy in unprocessed microalgae feeds (trial 1). For PEF-processed microalgae, only a broken line fit was obtained for gross energy, with a breakpoint at 5% (trial 2). Furthermore, a significant linear decrease in body weight (BW) (p < 0.001), average daily gain (ADG) (p < 0.001), average daily feed intake (ADFI) (p = 0.006), and relative and absolute breast filet weight was observed as microalgae inclusion level increased (trial 2). Color parameters also changed significantly with increasing microalgae inclusion level: L* showed a significant linear decrease (p = 0.029), b* and a* showed a significant linear increase (p < 0.001) (trial 2). This research advances the exploration of sustainable protein alternatives, highlighting the potential of microalgae in broiler feed and the benefits of processing methods such as PEF to enhance nutrient utilization. Full article

The prediction of ocean ambient noise is crucial for protecting the marine ecosystem and ensuring communication and navigation safety, especially under extreme weather conditions such as typhoons and strong winds. Ocean ambient noise is primarily caused by ship activities, wind waves, and other factors, and its complexity makes it a significant challenge to effectively utilize limited data to observe future changes in noise energy. To address this issue, we have designed a multi-modal linear model based on a “decomposition-prediction-modal trend fusion-total fusion” framework. This model simultaneously decomposes wind speed data and ocean ambient noise data into trend and residual components, enabling the wind speed information to effectively extract key trend features of ocean ambient noise. Compared to polynomial fitting methods, single-modal models, and LSTM multi-modal models, the average error of the relative sound pressure level was reduced by 1.3 dB, 0.5 dB, and 0.3 dB, respectively. Our approach demonstrates significant improvements in predicting future trends and detailed fittings of the data. Full article

Magnetic induction phase shift is a promising technology for the detection of cerebral hemorrhage, owing to its nonradioactive, noninvasive, and real-time detection properties. To enhance the detection sensitivity and linearity, a zero-flow sensor was proposed. The uniform primary magnetic field and its counteraction were achieved. Phase-change responses to solutions of varying conductivities and rabbits with cerebral hemorrhage were investigated and compared with traditional sensors. The sensitivities in detecting solutions with different conductivities were 1.84, 1.39, and 1.22 times higher than those for a low-pass birdcage coil, planar gradiometer, and Bx-sensor, respectively. The results for rabbits with cerebral hemorrhage showed that the sensitivities increased by 1.17, 1.67, and 6.3 times compared with a low-pass birdcage coil, symmetric cancelation-type sensor, and single co-axial coil, respectively. This sensor could accurately detect three stages in the pathological process. Blood loss of 1 mL meant that the compensatory mechanism of cerebrospinal fluid began to fail, and 1.4 mL of blood loss meant that the compensatory mechanism failed completely. The adjusted R-squared value of the first-order linear fit was above 0.98 in both physical and animal experiments, indicating that high detection linearity was achieved. The proposed sensor provides a more accurate method for cerebral hemorrhage detection and facilitates the practical application of magnetic induction phase shift in pre-hospital and bedside real-time detection. Full article

The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features. Full article

Objectives: The aim of this study was to systematically revise the state of art of the accuracy of digital and conventional impressions in clinical full-arch scenarios. Methods: Electronic and manual searches were conducted up to December 2024. Only trials comparing the accuracy of digital versus conventional impressions were selected by two independent reviewers. Accuracy was evaluated by analysing the fit of the prostheses obtained through conventional workflows and those obtained from digital workflows using intraoral scanners. Alternatively, accuracy was assessed by comparing the standard tessellation language data acquired from intraoral scanning with those obtained from scanning the physical model. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Meta-analysis was conducted to pool the mean differences from the included studies, with heterogeneity tested by Cochran’s Q test and quantified by the I² index. Results: We included 9 relevant studies from a total of 2535 identified studies. The risk of bias was evaluated as low, and the main results of all the included articles reported similar accuracy between digital and conventional impressions. Random effects meta-analysis resulted in a pooled mean difference of 152.46 (95% C.I. = 76.46–228.46, p-value < 0.001, I² = 93.48%). Conclusions: In conclusion, the results of the present systematic review reveal contradictory findings regarding the accuracy of digital impressions. However, most studies analysing the clinical performance of prostheses obtained through digital impressions suggest that their accuracy falls within clinically acceptable thresholds. Future research should report comparable outcomes and focus attention on linear deviations, comparing differences between conventional and digital impressions not in absolute terms, but relative to the distance measured. Full article

Recovering the relaxation spectrum, a fundamental rheological characteristic of polymers, from experiment data requires special identification methods since it is a difficult ill-posed inverse problem. Recently, a new approach relating the identification index directly with a completely unknown real relaxation spectrum has been proposed. The integral square error of the relaxation spectrum model was applied. This paper concerns regularization aspects of the linear-quadratic optimization task that arise from applying Tikhonov regularization to relaxation spectra direct identification problem. An influence of the regularization parameter on the norms of the optimal relaxation spectra models and on the fit of the related relaxation modulus model to the experimental data was investigated. The trade-off between the integral square norms of the spectra models and the mean square error of the relaxation modulus model, parameterized by varying regularization parameter, motivated the definition of two new multiplicative indices for choosing the appropriate regularization parameter. Two new problems of the regularization parameter optimal selection were formulated and solved. The first and second order optimality conditions were derived and expressed in the matrix-vector form and, alternatively, in finite series terms. A complete identification algorithm is presented. The usefulness of the new regularization parameter selection rules is demonstrated by three examples concerning the Kohlrausch–Williams–Watts spectrum with short relaxation times and uni- and double-mode Gauss-like spectra with middle and short relaxation times. Full article

The urbanization process has led to significant changes in the landscape, shifting the epidemiological profile of the visceral leishmaniasis (VL) in Brazil. Dogs are considered the main urban reservoir of VL, whose infections precede cases in humans. In order to understand the socio-environmental determinants associated with canine visceral leishmaniasis (CVL), we conducted a spatial analysis of CVL cases in northeastern Brazil from 2013 to 2015, georeferencing 3288 domiciled dogs. We used linear mixed models to understand the ecoepidemiological determinants of CVL spatial relative risk (CVL SRR). Our findings indicate heterogeneity in CVL distribution, with 1 km diameter clusters potentially connected within an estimated 4.9 km diameter by the Ripley-K statistic. In our best-fit model, the CVL SRR was positively correlated with the proportion of households with literate heads, with trees, and with open sewage, but negatively correlated with vegetation phenology and mean income of the census sector. Here, we discuss the potential maintenance source of urban CVL clusters on a One Health framework. These findings highlight the complex interplay of socioeconomic and environmental factors in shaping the spatial distribution of CVL. Full article

The concept of a rapidly sign-switching cosmological constant, interpreted as a mirror AdS-dS transition in the late universe and known as the ΛsCDM, has significantly improved the fit to observational data, offering a promising framework for alleviating major cosmological tensions such as the H0 and S8 tensions. However, when considered within general relativity, this scenario does not predict any effects on the evolution of the matter density contrast beyond modifications to the background functions. In this work, we propose a new gravitational model in which the background dynamics predicted by the ΛsCDM framework are mapped into f(T) gravity, dubbed f(T)-ΛsCDM, rendering the models indistinguishable at the background level. However, in this new scenario, the sign-switching cosmological constant dynamics modify the evolution of linear matter perturbations through an effective gravitational constant, Geff. We investigate the evolution of the growth rate and derive new observational constraints for this scenario using RSD measurements. We also present new constraints in the standard ΛsCDM case, incorporating the latest Type Ia supernovae data samples available in the literature, along with BAO data from DESI. Our findings indicate that the new corrections expected at the linear perturbative level, as revealed through RSD samples, can provide significant evidence in favor of this new scenario. Additionally, this model may be an excellent candidate for resolving the current S8 tension. Full article