Search Results (45)

This study investigates the emission characteristics and environmental impacts of pollutants from bagasse-fired biomass boilers through the integrated field monitoring of two sugarcane processing plants in Guangxi, China. Comprehensive analyses of flue gas components, including PM_2.5, NOx, CO, heavy metals, VOCs, HCl, and HF, revealed distinct physicochemical and emission profiles. Bagasse exhibited lower C, H, and S content but higher moisture (47~53%) and O (24~30%) levels compared to coal, reducing the calorific values (8.93~11.89 MJ/kg). Particulate matter removal efficiency exceeded 98% (water film dust collector) and 95% (bag filter), while NOx removal varied (10~56%) due to water solubility differences. Heavy metals (Cu, Cr, Ni, Pb) in fuel migrated to fly ash and flue gas, with Hg and Mn showing notable volatility. VOC speciation identified oxygenated compounds (OVOCs, 87%) as dominant in small boilers, while aromatics (60%) and alkenes (34%) prevailed in larger systems. Ozone formation potential (OFP: 3.34~4.39 mg/m³) and secondary organic aerosol formation potential (SOAFP: 0.33~1.9 mg/m³) highlighted aromatic hydrocarbons (e.g., benzene, xylene) as critical contributors to secondary pollution. Despite compliance with current emission standards (e.g., PM < 20 mg/m³), elevated CO (>1000 mg/m³) in large boilers indicated incomplete combustion. This work underscores the necessity of tailored control strategies for OVOCs, aromatics, and heavy metals, advocating for stricter fuel quality and clear emission standards to align biomass energy utilization with environmental sustainability goals. Full article

Background: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. Methods: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. Results: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. Conclusions: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. Full article

To elucidate the genetic basis of thousand-kernel weight (TKW) related to fundamental traits such as kernel length (KL), kernel width (KW), and kernel diameter ratio (KDR) at the individual quantitative trait loci (QTL) level, both unconditional QTL analysis and conditional QTL analysis for TKW were analyzed using a recombinant inbred line (RIL) population, along with a simplified physical map. A total of 37 unconditional QTLs and 34 conditional QTLs were identified. Six QTLs exhibited independent effects from individual traits (KL, KW, or KDR), while 18 QTLs showed common influences from two or three of these traits simultaneously. Additionally, 26 pairs of epistatically interacting QTLs involving 16 loci were detected. Subsequently, fine mapping of the stable and major-effect QTL QTkw1B was carried out using the derived near-isogenic lines (NILs), ultimately locating it within the interval of 698.15–700.19 Mb on chromosome 1B of the KN9204 genome. The conditional QTL analysis and genetic effect analysis based on NILs both indicated that the increase in TKW was primarily contributed by kernel length. The QTL identified in the present study through the combination of conditional and unconditional QTL mapping could increase the understanding of the genetic interrelationships between TKW and kernel size traits at the individual QTL level and provide a theoretical basis for subsequent candidate gene mining. Full article

The ultra-thin Invar alloy strips are widely used in the manufacture of the fine masks; cold rolling of such thin strips (<100 μm) poses significant difficulties, primarily due to the limited number of grains within the thickness range. Consequently, it is important to understand the grain structure and property evolutions of the ultra-thin Invar alloy strips during cold rolling. In this study, an annealed Invar36 alloy strip, 100 µm thick, was cold rolled to different thicknesses, and the surface deformation morphologies, cross-sectional grain structure, intracrystalline microstructure and tensile properties of these thin strips were characterized and analyzed. The results show that plastic deformation of the initial annealed equiaxed grains is not uniform, depending on the grain orientation, resulting in different slip bands morphologies, unevenness and increase in roughness. Meanwhile, the grain rotation and rolling texture develop with increasing cold rolling reduction. The dislocation density in the 60% cold-rolled strip is about decuple that of the original annealed strip, and high-density tangled dislocations are formed, making the tensile strength increase from 430 MPa to 738 MPa. Grain refining and proper intermediate annealing are proposed to optimize the thickness uniformity, evenness and surface roughness. Full article

Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO’s fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy. Full article

Deep mining in metal mines faces more and more complex geological conditions, such as “three highs and one disturbance” (high ground stress, high permeability, high temperature, and mining-induced disturbance), which can easily trigger water inrush disasters and seriously affect the safety and efficiency of deep mining. This paper focuses on the deep hydrogeological structural characteristics of the Huize lead–zinc mine. Firstly, two main factors affecting the production safety of the mining area, namely the water source and water channel of the mine, were analyzed. Based on this analysis, nine factors were determined as indicators for the risk assessment of water inrush, including the water head difference, water-bearing capacity, permeability coefficient, aquifer thickness, water pressure, fault type, fault scale, fault water conductivity, and karst zoning characteristics. Then, a water inrush risk assessment model for the deep mine was constructed, and the weights of the individual factors were determined using the analytic hierarchy process (AHP) and entropy weight method (EWM). Combined with the multi-factor spatial fitting function of the GIS, a zoning map of the risk assessment of water inrush was developed. The results showed that the aquifer groups of the Permian Liangshan Formation and the Carboniferous Maping Formation (P₁l + C₃m) were relatively safe, whereas the karst fissure aquifer of the Qixia–Maokou Formation (P₁q + m) posed a high risk of water inrush, necessitating advanced exploration and water drainage in the area. These findings provide guidance for water control measures in the Huize lead–zinc mine and offer valuable insights into the prediction and prevention of mine water hazards associated with ore body mining in karst aquifers. Full article

Background: Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. Methods: To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017–2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, asari and centWave, were applied. Results: In comparison to centWave, asari showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. Conclusions: This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers. Full article

The model organism Caenorhabditis elegans and its relationship with the gut microbiome are gaining traction, especially for the study of neurodegenerative diseases such as Parkinson’s Disease (PD). Gut microbes are known to be able to alter kynurenine metabolites in the host, directly influencing innate immunity in C. elegans. While the mitochondrial unfolded protein response (UPR^mt) was first characterized in C. elegans in 2007, its relevance in host–microbiome interactions has only become apparent in recent years. In this review, we provide novel insights into the current understanding of the microbiome–gut–brain axis with a focus on tripartite interactions between the UPR^mt, kynurenine pathway, and microbiome in C. elegans, and explore their relationships for PD remediations. Full article

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study’s aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts. Full article

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students’ historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students’ knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using Variational Autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets. Full article

Crop rotation increases crop yield, improves soil health, and reduces plant disease. However, few studies were conducted on the use of intensive cropping patterns to improve the microenvironment of saline soils. The present study thoroughly evaluated the impact of a three-year maize–peanut–millet crop rotation pattern on the crop yield. The rhizosphere soil of the crop was collected at maturity to assess the effects of crop rotation on the composition and function of microbial communities in different tillage layers (0–20 cm and 20–40 cm) of sandy saline–alkaline soils. After three years of crop rotation, the maize yield and economic benefits rose by an average of 32.07% and 22.25%, respectively, while output/input grew by 10.26%. The pH of the 0–40 cm tillage layer of saline–alkaline soils decreased by 2.36%, organic matter rose by 13.44%–15.84%, and soil-available nutrients of the 0–20 cm tillage layer increased by 11.94%–69.14%. As compared to continuous cropping, crop rotation boosted soil nitrogen and phosphorus metabolism capacity by 8.61%–88.65%. Enrichment of Actinobacteria and Basidiomycota increased crop yield. Crop rotation increases microbial community richness while decreasing diversity. The increase in abundance can diminish competitive relationships between species, boost synergistic capabilities, alter bacterial and fungal community structure, and enhance microbial community function, all of which elevate crop yields. The obtained insights can contribute to achieving optimal management of intensive cultivation patterns and green sustainable development. Full article

Chemotherapy-induced peripheral neuropathy (CIPN) remains a clinical challenge for up to 80% of breast cancer survivors. In an open-label study, participants underwent three interventions: standard care (duloxetine) for 1 month (Phase 1), oral cannabidiol (CBD) for 2 months (Phase 2), and CBD plus multi-modal exercise (MME) for another 2 months (Phase 3). Clinical outcomes and gut microbiota composition were assessed at baseline and after each phase. We present the case of a 52-year-old female with a history of triple-negative breast cancer in remission for over five years presenting with CIPN. She showed decreased monocyte counts, c-reactive protein, and systemic inflammatory index after each phase. Duloxetine provided moderate benefits and intolerable side effects (hyperhidrosis). She experienced the best improvement and least side effects with the combined (CBD plus MME) phase. Noteworthy were clinically meaningful improvements in CIPN symptoms, quality of life (QoL), and perceived physical function, as well as improvements in pain, mobility, hand/finger dexterity, and upper and lower body strength. CBD and MME altered gut microbiota, showing enrichment of genera that produce short-chain fatty acids. CBD and MME may improve CIPN symptoms, QoL, and physical function through anti-inflammatory and neuroprotective effects in cancer survivors suffering from long-standing CIPN. Full article

This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively. Full article

Due to the diverse types of sedimentary source supply systems, the research on the distribution patterns of fine-grained sedimentary rocks in rifted lake basins is relatively underdeveloped. The authors of this article selected the lower third member of the Shahejie Formation (${\mathrm{E}\mathrm{s}}_3^{\mathrm{L}}$) in Zhanhua Sag as a research object. Using rock core, thin section, and other data, we analyzed rock types and genesis mechanisms, identified the spatiotemporal distribution patterns of facies within the sag, and established a fine-grained depositional model for the rifted lake basin. Five lithofacies were identified, revealing the differential distribution patterns of fine-grained sedimentary rocks in the region. The northern steep slope zone and the deep depression zone are characterized by the deposition of gravity flow-formed terrestrial layered-pebbly mudstone and gravity flow-formed mixed-source pebbly mudstone, respectively. In the central lake basin zone, mixed-source laminated mudstone with vertical rhythmic interlayers has developed. In the southern gentle slope zone, biologically cemented endogenic irregularly bedded algal limestone and chemically precipitated endogenic laminated limestone have developed. The characteristics of strong northern and weak southern terrestrial inputs in the study area control the differential distribution of lithofacies, and based on this, a fine-grained rock depositional model for the rifted lake basin was established. Full article

It is crucial to detect Pb²⁺ accurately and rapidly. This work proposes an ultra-sensitive optical fiber surface plasmon resonance (SPR) sensor functionalized with glutathione (GSH) for label-free detection of the ultra-low Pb²⁺ concentration, in which the refractive index (RI) sensitivity of the multimode-singlemode-multimode (MSM) hetero-core fiber is largely enhanced by the gold nanoparticles (AuNPs)/Au film coupling SPR effect. The GSH is modified on the fiber as the sensing probe to capture and identify Pb²⁺ specifically. Its working principle is that the Pb²⁺ chemically reacts with deprotonated carboxyl groups in GSH through ligand bonding, resulting in the formation of stable and specific chelates, inducing the variation of the local RI on the sensor surface, which in turn leads to the SPR wavelength shift in the transmission spectrum. Attributing to the AuNPs, both the Au substrates can be fully functionalized with the GSH molecules as the probes, which largely increases the number of active sites for Pb²⁺ trapping. Combined with the SPR effect, the sensor achieves a sensitivity of 2.32 × 10¹¹ nm/M and a limit of detection (LOD) of 0.43 pM. It also demonstrates exceptional specificity, stability, and reproducibility, making it suitable for various applications in water pollution, biomedicine, and food safety. Full article