Search Results (360,860)

Suspended sediment (SS) has a wide range of negative effects such as increased water turbidity, altered habitat structures, sedimentation, and effects on hydraulic systems and environmental engineering projects. Nevertheless, the methods for accurately determining SS sources on a basin-scale are poorly understood. Herein, we used a simplified neural network analysis (NNA) model to identify the sources of SS in Japan’s Oromushi River Catchment Basin. Fine soil samples were collected from different locations of the catchment basin, processed, and separately analysed using X-ray fluorescence (XRF) and X-ray diffraction (XRD). The sampling stations were grouped according to the type of soil cover, vegetation type and land-use pattern. The geochemical components of each group were fed into the same neural network layer, and a series of equations were applied to estimate the sediment contribution from each group to the downstream side of the river. Samples from the same sampling locations were also analysed by XRD, and the obtained peak intensity values were used as the input in the NNA model. SS mainly originated from agricultural fields, with regions where the ground is covered with volcanic ash identified as the key sources through XRF and XRD analysis, respectively. Therefore, based on the nature of the surface soil cover and the land use pattern in the catchment basin, NNA was found to be a reliable data analytical technique. Moreover, XRD analysis does not incorporate carbon, and also provides detailed information on crystalline phases. The results obtained in this study, therefore, do not depend on seasonal uncertainty due to organic matter. Full article

Objectives: The main goal of this study is to publish findings on the lifestyle factors of pregnant women in Hungary and their impact on early childhood health status by examining changes over time and regional/geographical disparities. Methods: The source of the data is the raw indicators reported by health visitors as per mandatory annual report data for the period of 1997–2019. To examine the association, we used indicators of pregnant women’s states as explanatory variables (for example, pregnant women in very late care, prenatal smoking habits, and pregnant women without care), and the outcome indicators were prematurity, intrauterine malnutrition, and newborn babies with developmental disorders. A univariate Poisson regression was used to examine the correlations. Results: Our results show a decreasing trend in the proportion of pregnant women who smoke and of pregnant women who apply late for care (after 28 weeks of pregnancy), with an increasing indicator of regional differences. The research results of the prenatal and perinatal indicators show that the counties Borsod-Abaúj-Zemplén and Szabolcs-Szatmár-Bereg are the most critical areas in terms of health status in Hungary. The number of pregnancies attended very late (after 28 weeks) and the number of women who gave birth without health visitor care are associated with the number of preterm births (R² = 0.7313; p < 0.001; R² = 0.5519; p < 0.001) and intrauterine growth restrictions (R² = 0.3306; p < 0.001; R² = 0.2632; p < 0.001). Conclusion: Interventions to improve early childhood health in some counties of Hungary are urgently needed to reduce regional disparities. Such counties include Borsod-Abaúj-Zemplén, Szabolcs-Szatmár-Bereg, Heves, Somogy, Bács-Kiskun, and Nógrád. Health education for pregnant women and activities to strengthen the compliance of pregnant women are key to improving early childhood health outcomes. Full article

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm. Full article

Training load monitoring is employed to quantify training demands, to determine individual physiological adaptions and to examine the dose–response relationship, ultimately reducing the likelihood of injury and making a meaningful impact on performance. The purpose of this study is to explore the relationship between training load and injury in competitive swimmers, using the session rate of perceived exertion (sRPE) method. Data were collected using a prospective, longitudinal study design across 104 weeks. Data were collected from 34 athletes centralised in two of Swim Ireland’s National Centres. Bayesian mixed effects logistic regression models were used to analyse the relationship between sRPE-TL and medical attention injuries. The average weekly swim volume was 33.5 ± 12.9 km. The weekly total training load (AU) averaged 3838 ± 1616.1. A total of 58 medical attention injury events were recorded. The probability of an association between training load and injury ranged from 70% to 98%; however, evidence for these relationships was deemed weak or highly uncertain. The findings suggest that using a single training load metric in isolation cannot decisively inform when an injury will occur. Instead, coaches should utilise monitoring tools to ensure that the athletes are exposed to an appropriate training load to optimise physiological adaptation. Future research should strive to investigate the relationship between additional risk factors (e.g., wellbeing, lifestyle factors or previous injury history), in combination with training load and injury, in competitive swimmers. Full article

In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress indicators, were derived by analyzing the EEG data collected. The EEG analysis revealed that agricultural work stress manifested when participants engaged in agricultural tasks following a period of rest. Additionally, the right prefrontal cortex was identified where the values of SEF95% and RGP increased concurrently with the rise in noise (61.42–88.39 dBA) and vibration (0.332–1.598 m/s²). This study’s results are expected to be utilized as foundational data to determine the agricultural work stress felt by farmers during work through EEG analysis in response to changes in noise and vibration. Full article

Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments. Full article

The computer-assisted inverse design of photonic computing, especially by leveraging artificial intelligence algorithms, offers great convenience to accelerate the speed of development and improve calculation accuracy. However, traditional thickness-based modulation methods are hindered by large volume and difficult fabrication process, making it hard to meet the data-driven requirements of flexible light modulation. Here, we propose a diffractive deep neural network (D²NN) framework based on a three-layer all-dielectric phased transmitarray as hidden layers, which can perform the classification of handwritten digits. By tailoring the radius of a silicon nanodisk of a meta-atom, the metasurface can realize the phase profile calculated by D²NN and maintain a relative high transmittance of 0.9 at a wavelength of 600 nm. The designed image classifier consists of three layers of phase-only metasurfaces, each of which contains 1024 units, mimicking a fully connected neural network through the diffraction of light fields. The classification task of handwriting digits from the ‘0’ to ‘5’ dataset is verified, with an accuracy of over 90% on the blind test dataset, as well as demonstrated by the full-wave simulation. Furthermore, the performance of the more complex animal image classification task is also validated by increasing the number of neurons to enhance the connectivity of the neural network. This study may provide a possible solution for practical applications such as biomedical detection, image processing, and machine vision based on all-optical computing. Full article

In this work we study the use of artificial intelligence models, particularly focusing on transfer learning and interpretability, to predict polymer properties. Given the challenges imposed by data scarcity in polymer science, transfer learning offers a promising solution by using learnt features of models pre-trained on other datasets. We conducted a comparative analysis of direct modelling and transfer learning-based approaches using a polyacrylates’ glass transitions dataset as a proof-of-concept study. The AI models utilized tokenized SMILES strings to represent polymer structures, with convolutional neural networks processing these representations to predict Tg. To enhance model interpretability, Shapley value analysis was employed to assess the contribution of specific chemical groups to the predictions. The results indicate that while transfer learning provides robust predictive capabilities, direct modelling on polymer-specific data offers superior performance, particularly in capturing the complex interactions influencing Tg. This work highlights the importance of model interpretability and the limitations of applying molecular-level models to polymer systems. Full article

Application of the FOLFOX scheme to colorectal cancer (CRC) patients often results in the development of chemo-resistance, leading to therapy failure. This study aimed to develop a functional and easy-to-use algorithm to predict patients’ response to FOLFOX treatment. Transcriptomic data of CRC patient’s samples treated with FOLFOX were downloaded from the Gene Expression Omnibus database (GSE83129, GSE28702, GSE69657, GSE19860 and GSE41568). Comparing the expression of top up- and downregulated genes in FOLFOX responder and non-responder patients’ groups, we selected 30 potential markers that were used to create a step-by-step eliminative procedure based on modified radial data visualization, which depicts the interplay between the expression level of chosen attributes (genes) to locate data points in low-dimensional space. Our analysis proved that FOLFOX-resistant CRC samples are predominantly characterized by upregulated expression levels of TMEM182 and MCM9 and downregulated LRRFIP1. Additionally, the procedure developed based on expression levels of TMEM182, MCM9, LRRFIP1, LAMP1, FAM161A, KLHL36, ETV5, RNF168, SRSF11, NCKAP5, CRTAP, VAMP2, ZBTB49 and RIMBP2 proved to be capable in predicting FOLFOX therapy response. In conclusion, our approach can give a unique insight into clinical decision-making regarding therapy scheme administration, potentially increasing patients’ survival and, consequently, medical futility due to incorrect therapy application. Full article

Abstract: Introduction: Gold threads became widely used in esthetic surgery in the early 1990s. Produced in Spain, these threads consisted of a gold thread (5/0) with a diameter of 0.1 mm, composed of 99.99% pure gold, which was combined with a polyglycolic thread. Since then, discussions about their effectiveness have continued, which is the focus of our study. Patients and Methods: Gold thread implantation was performed on 11,062 patients in four clinics in Moscow over the course of 28 years. This study used a comparative analysis of photo collages created for the visual representation of data and changes before and after the procedure, as well as patient satisfaction assessments using the Patient Satisfaction Scale (PSS). We examined the results of 492 patients who underwent gold thread implantation in the face between 1996 and 2024. Of these patients, 86% were women aged 30 to 60 years, 11% were women aged 20 to 30 years, and 3% were men aged 25 to 60 years. Results: One year after thread implantation, the PSS assessment showed a 91% success rate with minimal complications. Patient satisfaction was high, with an average score of 4.8 out of 5 after one month, 4.7 after six months, and 4.6 after one-year post-procedure. Discussion: Gold thread implantation is a minimally invasive procedure that has demonstrated a high level of safety, making it an effective option for facial rejuvenation. Histological studies have shown that gold threads stimulate the production of collagen and elastin and activate angiogenesis, thereby improving skin nourishment and hydration, as well as enhancing the skin tone, elasticity, and turgor. To improve the qualitative characteristics of the skin, it is necessary to work in the subcutaneous layer. Conclusions: Gold thread implantation strengthens the connective tissue framework at the implantation site, thus improving skin nourishment and hydration. Gold threads provide a long-term rejuvenating effect, slowing the ptosis of the soft tissue of the face and neck. Gold thread implantation does not interfere with tissue dissection during surgical interventions or the performance of any cosmetic procedures, including hardware-based treatments. Full article

High-precision DCBs are essential for effective multi-frequency and multi-constellation GNSS integration, especially in processing compatible signal observations. This study utilizes data from MGEX, iGMAS, and CORS stations to estimate and analyze long time series of BDS/GNSS DCBs, focusing on stability and influencing factors. Results indicate that DCBs for the same signal, but different channels exhibit similar ranges and trends. Among BDS DCBs, those from satellites with rubidium atomic clocks are more stable than those with hydrogen atomic clocks. An upgrade and maintenance of BDS in late 2022, reported by NABU, likely contributed to DCB jumps. BDS-compatible signal DCBs show weaker stability compared to GPS and Galileo. Variations in GNSS signal processing and receiver algorithms also impact DCB stability. Converting DCBs to OSBs and performing RMS statistics revealed that smaller differences between signals increase the susceptibility of observation equations to observation quality. Full article

Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to comprehensively describe the 3D structure of the target. Therefore, developing an effective 3D point cloud fusion method can fully utilize information from multiple observations to improve the accuracy of 3D reconstruction. To this end, this paper addresses the problems of shape distortion and sparse point cloud density in existing 3D point cloud fusion methods by proposing a 3D point cloud fusion method based on Earth mover’s distance (EMD) auto-evolution and local parameterization network. Our method is divided into two stages. In the first stage, EMD is introduced as a key metric for evaluating the fusion results, and a point cloud fusion method based on EMD auto-evolution is constructed. The method uses an alternating iterative technique to sequentially update the variables and produce an initial fusion result. The second stage focuses on point cloud optimization by constructing a local parameterization network for the point cloud, mapping the upsampled point cloud in the 2D parameter domain back to the 3D space to complete the optimization. Through these two steps, the method achieves the fusion of two sets of non-uniform point cloud data obtained from satellite stereo images into a single, denser 3D point cloud that more closely resembles the true target shape. Experimental results demonstrate that our fusion method outperforms other classical comparison algorithms for targets such as buildings, planes, and ships, and achieves a fused RMSE of approximately 2 m and an EMD accuracy better than 0.5. Full article

Abstract: The spatial distribution and trend of carbon emissions in the Yellow River Basin—an important ecological barrier and economic belt in China—directly affect the stability of the ecosystem and the sustainable development of the regional economy. Based on the data for carbon emissions in China’s counties from 1997 to 2017, this paper utilizes standard deviation ellipses, Theil index nested decomposition, and geographic detector models to make a comprehensive description of the spatial and temporal distribution and dynamic evolution characteristics of carbon emissions in the Yellow River Basin. Factors influencing carbon emissions are also analyzed from multiple dimensions. According to the findings, (1) carbon emissions at the county level show a clear upward trend without reaching a peak, exhibiting a spatial distribution of higher emissions in the east and lower in the west and higher in the south and lower in the north, with the mid-lower reaches being the center. The junction of the Shandong, Shaanxi, and Gansu provinces further exhibits a significant expansion, forming two core areas of carbon emissions. (2) Carbon emissions at the county level in the Yellow River Basin are influenced by both economic and geographic factors, exhibiting a significant high carbon spillover effect and a low carbon lock-in effect. The gravity center of the distribution has shifted towards the mid-lower reaches, with the upper reaches displaying dispersion tendencies. (3) Intra-regional disparities are the main source of the overall spatial differences in carbon emissions, with the largest disparities being observed in the upper reaches, followed by the middle reaches, and the smallest disparities being observed in the lower reaches. Further analysis shows that the level of economic development is the primary factor influencing the spatial variation of carbon emissions, and the combined effects of population size and industrial agglomeration are the key drivers of the annual growth in carbon emissions. Full article

The severe contamination of groundwater supplies in rural areas is a global problem that requires strict environmental measures. Related to this, one of the most important challenges at present is the elimination of local sources of pollution. Therefore, this research examined the local water quality changes following the construction of the sewerage network, under the framework of long-term monitoring (2011–2022) in Báránd, Hungary, using water quality indices and GIS (Geographic Information System) techniques. In order to understand the purification processes and spatial and temporal changes, three periods were determined: the pre-sewerage period (2011–2014), the transitional period (2015–2018), and the post-sewerage period (2019–2022). Forty monitoring wells were included in the study, ensuring complete coverage of the municipality. The results revealed a high level of pollution in the area in the pre-sewerage period. Based on the calculated indices, an average of 80% of the wells were ranked in categories 4–5, indicating poor water quality, while less than 8% were classified in categories 1–2, indicating good water quality. No significant purification process was detected in the transitional period. However, marked changes were observed in the post-sewerage period as a result of the elimination of local sources of pollution. In the post-sewerage period, the number of monitoring wells ranked as excellent and good increased significantly. Additionally, the number of wells assigned to category 5 decreased markedly, compared to the reference period. The significant difference between the three periods was confirmed by the Wilcoxon test as well (p < 0.05). Based on interpolated maps, it was found that, in the post-sewerage period, an increasing section of the settlement had good or excellent water quality. In addition to an assessment of long-term tendencies, the annual fluctuations in the water quality of the wells were also examined. This showed that the purification processes do not occur in a linear pattern but are influenced by various factors (e.g., precipitation). Our results highlight the importance of protecting and improving groundwater resources in municipal areas and the relevance of long-term monitoring of water adequate management policy. Full article

Pb(II) contamination in aquatic environments has adverse effects on humans even at a low concentration, so the efficient removal of Pb at a low cost is vital for achieving an environmentally friendly, sustainable, and healthy society. A variety of CaCO₃-based functional adsorbents have been synthesized to remove Pb, but the adsorption capacity is still unsatisfactory. Herein, calcite CaCO₃ microcubes/parallelepipeds are synthesized via simple precipitation and a hydrothermal approach and found to outperform previously reported nano-adsorbents considerably. The CaCO₃ achieves a high removal efficiency for Pb(II) (>99%) at a very low dosage (0.04–0.1 g/L) and an initial Pb(II) concentration of 100 mg/L. The CaCO₃ presents an excellent adsorption capacity of 4018 mg/g for Pb(II) removal and depicts good stability over a wide range of pH 6–11. The maximum adsorption kinetics are fitted well by the pseudo-second-order kinetic model, whereas the Freundlich isotherm delineates the adsorption data at equilibrium well, indicating a multilayer adsorption process. The ex situ study confirms that the Pb(II) adsorption mechanism by CaCO₃ can be attributed to the rapid metal-ion-exchange reaction between Pb(II) and Ca²⁺. Furthermore, a red shift in the Fourier Transform Infrared (FTIR) spectroscopy peak from 1386 cm⁻¹ to 1374 cm⁻¹ of CaCO₃ after Pb removal indicates the adsorption of Pb onto the surface. This adsorbent provides an opportunity to treat wastewater and can be extended to remove other toxic heavy metals. Full article