Browse Articles

Serum IgA/C3 ratio as a diagnostic and prognostic biomarker for IgA nephropathy

Carbon dot based molecularly imprinted polymer for selective fluorometric determination of tetracycline and metronidazole in pharmaceuticals and human plasma

Abstract A highly sensitive, selective, and rapid spectrofluorometric method has been developed using fluorescence sensors. This method is based on the highly fluorescent graphene quantum dots coated with silica molecularly imprinted polymers (GQDs-SMIPs) for the determination of tetracycline HCl (TET) and metronidazole (MET). Upon excitation of the GQDs-SMIPs at 260.0 nm for TET and 245.0 nm for MET, strong fluorescence emissions at 292.0 nm were produced for both sensors. Such fluorescence was quenched by the addition of their corresponding molecularly imprinted polymer (MIPs) templates. The quenching effect was linear over the concentration ranges of 15.0–120.0 µM and 15.0–140.0 µM for TET and MET, respectively. Limits of detection (LOD) were 3.55 µM, and 4.48 µM while limits of quantification (LOQ) were 10.75 µM and 13.57 µM for TET and MET, respectively. The fabricated GQDs-SMIPs were characterized using scanning electronic microscopy, Fourier-transform IR spectroscopy, and X-ray diffraction. Selectivity of the method was investigated against potentially interfering substances, including two official impurities of TET and MET. Additionally, this fluorescent technique was successfully applied for the determination of these drugs in pharmaceutical dosage forms and spiked human plasma samples. This approach provides a selective and sensitive fluorometric platform for the determination of the studied drugs in complex matrices and quality control laboratories.

Association between age-related macular degeneration and osteoporosis in US

Regional factors affecting the prevalence of hypertension using geographic information system

Design of an integral sliding mode controller for reducing CO2 emissions in the transport sector to control global warming

Innovative diagnostic approaches for lung cancer: integrating traditional cytology with qPCR for rapid and reliable results

Growth of complex oxide single crystals with high melting point over 2200 °C using tungsten crucible

Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia

High-gravitational effect on process stabilization evaluation for material extrusion using polylactic acid filament

Deep neural network approach integrated with reinforcement learning for forecasting exchange rates using time series data and influential factors

Impact of massive open online courses in higher education using machine learning and decision based fuzzy frank power aggregation operators models

Flavonoids from Polygonum hydropiper L. regulate PCV2-induced oxidative stress of RAW264.7 cells via Pi3k/AKT and Nrf2/HO-1 signaling pathways

A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems

An improved EAE-DETR model for defect detection of server motherboard

Analysing community-level spending behaviour contributing to high carbon emissions using stochastic block models

Abstract Large financial transaction datasets are increasingly used to estimate carbon emissions associated with individual spending. However, to effectively target high-emission spending areas and implement successful carbon reduction strategies, policymakers and financial institutions need to understand individual consumer spending behaviour. In this study, we describe an approach to identify spending patterns in large financial transaction datasets, using stochastic block modelling for community detection on a bipartite network. This is an effective method to form communities of consumers who share similar spending patterns across merchant categories, allowing us to identify the categories causing high carbon emissions for each group of consumers. We also introduce a modification to the weights of the bipartite network which allows us to keep the average community spending constant across different categories. The impact and applications of this study are twofold. First, it highlights the importance of transaction datasets and stochastic block modelling in providing insights for financial institutions in their efforts to decarbonise by identifying areas for targeted behavioural strategies for carbon reduction. Second, it provides researchers with a framework to examine how different factors, such as consumer spending patterns, energy usage, or transportation habits, interact with one another. This is done while keeping overall spending levels consistent across various communities, allowing for a controlled analysis of behavioural and economic impacts on carbon reduction efforts.

Reproducible Ala-Gly oligomerization catalyzed by the natural Borate colemanite in prebiotic conditions

Quantum computation for robot posture optimization

Dual branch attention network for image super-resolution

Abstract The advancement of deep convolutional neural networks (CNNs) has resulted in remarkable achievements in image super-resolution methods utilizing CNNs. However, these methods have been limited by a narrow perceptual field and often require a high number of parameters and computational complexity, making them unsuitable for resource-constrained devices. Recently, the Transformer architecture has shown significant potential in image super-resolution due to its ability to perceive global features. Yet, the quadratic computational complexity of self-attention mechanisms in these Transformer-based methods leads to substantial computational and parameter overhead, limiting their practical application. To address these challenges, we introduce the Dual Branch Attention Network (DBAN), a novel Transformer model that integrates prior knowledge from traditional dictionary learning with the global feature perception capabilities of Transformers, enabling image super-resolution. Our model features a ”token dictionary” mechanism that uses auxiliary labeling to provide external prior information, enhancing cross-attention and self-attention computations while maintaining a linear relationship between computational complexity and image size. We also propose a Feature Aggregation Module (FAM) that efficiently extracts local contextual information and performs channel feature fusion, substantially enhancing the model’s performance and efficiency. By reasonably arranging the number of modules and the depth of the network, we reduce the complexity of the model. Extensive experiments have demonstrated that our DBAN achieves excellent performance.

Evaluation of protection benefit of sand barrier fence with different heights on desert highway

As the first barrier of desert highway protection, sand-blocking fence is very important to the safety of the line. Based on the background of Wuma Expressway, this paper uses CFD numerical simulation to study the wind and sand-blocking effect of sand-blocking fence with different heights. The results show that: (1) Between the first and second sand-blocking fences, when the height of sand-blocking fence is 2m, 2.5m, 3.0m and 3.5m, the wind speed near the surface (0.1m ~ 0.3m) is reduced by 87% ~ 97% of the initial wind speed. Between the second and third sand-blocking fences, when the height of sand-blocking fence is 2.5m, the increase of wind speed is 13.87% lower than that of 2m height. The decrease is the largest, and sand particles are easy to deposit here in large quantities. When the height is 2.5m and above, the windbreak efficiency is greater than 90%, and the windbreak effect is significantly improved. (2) The change of sand barrier height has a significant effect on the windbreak efficiency between the second and third sand barriers. (3) Among the three sand-blocking fences, when the height of the sand-blocking fence is 2.5m, the thickness of the sand is 50.51% and 58.33% higher than that of the 2m high sand-blocking fence, and the sand-blocking effect is the most significant. After the height is increased to 3.5m, the thickness of the sand is no longer increased. (4) The height of sand-blocking fence is 2.5m, and the area of sand at the top of embankment is obviously reduced. The area of sand volume fraction 0.55–0.6 is 73.44% lower than that of 2m sand-blocking fence, and the effect of wind and sand prevention is the best.

Modulation of growth characteristics and endogenous hormone by cutting intensity in Eleutherococcus giraldii

Eleutherococcus giraldii (E. giraldii) is a quintessential medicinal plant in traditional Chinese medicine. This study established control, heavy pruning, and light pruning groups to reveal growth indexes and endogenous phytohormones in cultivated E. giraldii using enzyme-linked immunosorbent assay (ELISA). Results indicated that light pruning significantly promoted length of new branch elongation, thereby increasing E. giraldii branch bark yield. In contrast, heavy pruning inhibited length of new branch elongation and reduced branch bark production. Both pruning intensities enhanced the number and length of newly sprouted clonal plants, facilitating population expansion of E. giraldii. The heavy pruning altered the trends of indoleacetic acid (IAA) and abscisic acid (ABA) in apical leaves, as well as IAA and gibberellic acid 3 (GA3) in lateral leaves. The light pruning modified the trends of IAA, ABA, and isopentenyl adenine nucleoside (iPA) in apical leaves, as well as IAA and GA3 in lateral leaves. Apical leaf IAA promoted new branch growth in E. giraldii, while lateral leaf IAA exhibited the opposite effect. iPA played a significant role in eliminating apical dominance and enhancing stress resistance in E. giraldii. GA3 inhibited new branch growth while delaying leaf senescence. Apical leaf ABA was closely associated with improved stress resistance, whereas lateral leaf ABA primarily inhibited new branch growth. This study provides valuable insights for establishing sustainable logging strategies for E. giraldii, protecting wild resources, and offers reference for research on endogenous hormone responses in shrubs under logging interventions.