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Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods
Investigation of the role of southwestern Asia dust events on urban air pollution: a case study of Ahvaz, a highly polluted city
Abstract Investigating aerosol composition and particle dynamics in densely populated and polluted urban centers is crucial for understanding and managing urban air quality. Ahvaz, in southwestern Iran, consistently ranks among the most polluted cities globally, primarily due to high PM10 concentrations. This study analyzes trends in suspended particle concentrations in Ahvaz over a 12-year period (2008–2019) to identify the contributions of natural and anthropogenic sources to air pollution. Diurnal, monthly, and annual variations in PM10 and PM2.5 levels were examined, revealing key insights into the city’s pollution dynamics. Diurnal PM10 peaks around noon (232 µg/m3), mainly driven by natural dust sources, with minimal anthropogenic impact indicated by similar weekend and weekday concentrations (only 1.5% difference). Monthly analysis reveals significant dust activity in June and July (maximum PM10 concentration of 388.18 µg/m3), while higher PM2.5 levels in winter (average 54.8 µg/m3) are attributed to fossil fuel combustion. The PM2.5/PM10 ratio (mean = 0.24) highlights the dominance of coarse particles from dust events, especially in summer. The Hoffmann classification identifies 3425 dusty days in the study period, with PM10 levels notably higher due to dust sources in southern Iraq and southwestern Iran. Seasonal wind patterns, particularly Shamal winds, facilitate dust transport, corroborated by Windrose and PM10 rose data. The study underscores the need for regional dust suppression strategies in southern Iraq and southwestern Iran to mitigate air pollution in Ahvaz, highlighting the importance of regional cooperation.
Development and optimization of pearl millet waste biocomposite ceiling tiles: a waste management approach
Abstract The present study aims to develop and optimize sustainable biocomposite ceiling tiles by partially replacing gypsum with pearl millet waste, combined with Waste Wheat Flour (WWF) and waste paper. It aims to provide a sustainable alternative to conventional gypsum-based product, maintaining thermal insulation and flexural strength. Taguchi method was employed for experimental design, by varying key parameters such as gypsum, Pearl Millet Seed Covering (PMSC), and ceiling tile thickness. The signal-to-noise (S/N) ratio was used to identify the optimal composition and Analysis of Variance (ANOVA) with regression analysis determines the significance of each factor on thermal conductivity and flexural strength. The optimized composition for minimum thermal conductivity (0.065 W/m·K) was identified as 45% PMSC and 10% gypsum at 12 mm thickness. For maximum flexural strength (1.24 MPa), the optimal mix was 55% PMSC and 30% gypsum at 14 mm thickness. The finding underscore the critical influence of gypsum and tile thickness in enhancing material performance. The developed regression models exhibited predictive R2 value of 96.90% for thermal conductivity and 94.44% for flexural strength and an error margin below 3%, confirming the robustness of the approach. This study is original and introduces pearl millet waste biocomposites as eco-friendly alternatives to gypsum ceiling tiles. These tiles hold practical implications in ceiling, partition walls and decorative finishes across various geographic regions, contributing significantly to environmental sustainability and energy-efficient building solutions. Graphical abstract
Analogue simulation of quantum gravity black hole models in a dc-SQUID array
USP25 maintains KRAS expression and inhibiting the deubiquitinase suppresses KRAS signaling in human cancer
Soft label collaborative view consistency enhancement with application to incomplete multi-view clustering
Incomplete multi-view clustering (IMVC) is an unsupervised technique for clustering multi-view data when some view information is absent. However, most existing IMVC methods usually suffer from several significant challenges: (1) Inaccurate imputation or padding of missing data degrades clustering performance; (2) The ability to extract view features may decrease due to low-quality views, especially those that are inaccurately imputed. To overcome these challenges, in this paper, we introduce a novel IMVC framework, called soft label collaborative view consistency enhancement (SLC_CE). Firstly, we leverage the encoders of Transformers to construct a soft-label view information interaction module, which fully utilizes soft-labels to enhance view feature embeddings. Secondly, we employ soft labels to collaboratively impute missing features, addressing the incomplete multi-view data problem. Finally, we implement a consistency enhancement strategy across multi-level view features and soft labels to ensure high-quality feature extraction and imputation. Extensive experiments on several benchmark datasets demonstrate that the proposed SLC_CE method outperforms other state-of-the-art methods in real IMVC tasks.
The function of PCSK9 in doxorubicin-induced cardiotoxicity and its underlying mechanism
Stability criterion and control technology of roadway surrounding rock in Thick coal seam with multi-layer gangue roof
Adaptation of grassweeds to spring cropping through changes in germination, flowering time and fecundity
Abstract The ability of weed populations to adapt is pivotal for their success in overcoming adverse conditions, including control measures and climate change. Here, we show evidence for the local adaptation of blackgrass (Alopecurus myosuroides) and Italian ryegrass (Lolium multiflorum) to the distinct seasonal windows for crop establishment. We assessed germination, flowering, and fecundity traits in 16 parental populations (eight each of A. myosuroides and L. multiflorum) sourced from fields with repeated autumn- or spring-cropping backgrounds. We find evidence of contrasting maternal and genetic influences on the response of A. myosuroides germination to light exposure. Alopecurus myosuroides seeds collected from fields with a spring-cropping background demonstrated higher germination rates (86%) in darkness than seeds collected from fields with an autumn-cropping system (46%), evidence of maternal effects. Conversely, when parent plants from those same autumn and spring background populations were grown under identical conditions, the offspring seeds (one generation removed from the field) from an autumn-cropping linage germinated more in darkness (81%) than those from a spring-cropping linage (42%), evidence of a contrasting genetic effect. Parental biotypes of both species from fields with a spring-cropping history flowered earlier under spring-like vernalisation treatments, including no vernalisation, compared to their autumn counterparts. Additionally, parental seeds from spring-cropping fields of both species had higher seed production under treatments reflecting the cropping history of their parent populations. These findings provide phenotypic evidence for the potential adaptation of A. myosuroides and L. multiflorum to spring cropping systems. Diversifying cropping practices, such as integrating seasonal rotations within crop establishment windows, is therefore essential for disrupting weed adaptation and promoting sustainable weed management strategies.
Comprehensive geospatial assessment: morphometric parameters and hydrological implications in five Mexican basins
Using deep learning to capture gravel soil microstructure and hydraulic characteristics
Abstract The special hydraulic properties of gravel soil, attributed to its varying fine particle content, can be effectively analyzed using the Wasserstein Generative Adversarial Networks (WGANs) technique. This approach enables the reconstruction of 3D digital samples of gravel soil, allowing for the generation of specific microstructure realizations, including complex pore characteristics. This capability is crucial for gaining insights into the hydraulic behavior of gravel soil. In a specific case involving gravel soil samples from Guilin city, China, three samples with similar structural features were carefully selected for analysis. These samples were scanned using µ-CT to create the training dataset for the reconstruction model. The WGAN with Gradient Penalty technique was then applied to simulate the reconstruction of the digital gravel soil samples. The results demonstrated a high consistency between the reconstructed model of gravel soil realizations and the original samples in terms of porosity, two-point correlation function, linear path function, specific surface, and Euler characteristics number. Furthermore, through the evaluation of permeability, it was shown that the reconstructed realizations effectively captured and represented the actual soil prototype. This allowed for the analysis of seepage characteristics and internal stability within a range of magnitudes up to 10− 2 cm/s. Compared with the machine learning model generated in previous literature, the machine learning model recommended in this paper can capture the hydraulic properties of gravel soil with obvious difference in coarse and fine particle size, which is reflected in the difference of permeability of the two orders of magnitude.
Trends and gender disparities in the burden of rheumatoid arthritis in Pakistan from 1990 to 2021
A novel D-latch with low energy consumption in quantum-dot cellular automata technology
Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis
Colchicine inhibits vascular calcification by suppressing inflammasome activation through the enhancement of the Sirt2-PP2Ac signaling pathway
Plasma and fecal hormone profiles in an Endangered, oviparous colubrid, the Louisiana pinesnake
Reptiles are sorely under-represented in endocrinology research. The majority of studies are conducted on lizards and turtles, rarely are oviparous snakes investigated. We utilized our breeding population of captive Louisiana pinesnakes (Pituophis ruthveni, LPS) to describe annual hormone cycles in an egg-laying colubrid. We collected fecal and blood samples from adult male and female snakes (20M.23F) throughout the year. After validating extraction methods and assays, we measured four hormones (corticosterone, estradiol, progesterone, and testosterone) in samples collected (200 + fecal and 600 + blood samples) over a two-year period. While blood samples were collected on a schedule, fecal samples were collected opportunistically. We found differences in patterns exhibited by males and females and between sample types. In females, neither fecal nor plasma samples showed significant differences between any of the collection periods, excepting increased levels found in female plasma progesterone PreLay compared to PostLay (n = 4 animals, p = 0.0323) demonstrating the importance of circulating progesterone in oviparous snake reproduction. In males, time played a significant role in fecal corticosterone levels (p = 0.0164). Male plasma showed a number of significant changes throughout the year: a significant increase from Post-Brumation to Breeding levels of corticosterone (p = 0.0058), Breeding estradiol and testosterone levels significantly higher (E2: p < 0.036, T: p < 0.005) than all other time bins except Post-Brumation (E2: p = 0.062, T: p = 0.1). Comparing differences and similarities between two different sample types, there is a clear advantage to collecting blood on a set schedule; we were able to analyze samples based on significant life history events and maintain larger sample sizes which may have contributed to the lack of differences measured in fecal hormone levels. This study helps to better understand the seasonal hormonal patterns in egg-laying snakes, and to aid in the recovery of this endangered species.