Search Results (15,957)

The concentration of inevitable ionic species in regenerated water significantly alters the flotation characteristics of rare earth minerals, thereby hindering the effective extraction of bastnaesite. Therefore, it is of great significance to study the influence and mechanism of inevitable ions on the flotation of bastnaesite. This paper systematically investigated the effects of Ca²⁺, Mg²⁺, and Fe³⁺ on the flotation behavior of bastnaesite using a BHA/OHA combined collector system and studied the mechanism of action using contact angle testing, Raman spectroscopy, and Visual MINTEQ solution chemistry calculations. The results showed that the BHA/OHA combined collector had good collecting performance for bastnaesite, while Ca²⁺, Mg²⁺, and Fe³⁺ all had varying degrees of inhibitory effects on its flotation, with the order of influence being Fe³⁺ > Mg²⁺ > Ca²⁺. Contact angle tests showed that the presence of inevitable ions weakened the effect of the combined collector on improving the hydrophobicity of the bastnaesite surface. Raman spectroscopy results indicated that inevitable ions interfered with the adsorption of the combined collector on the mineral surface, with Fe³⁺ having the most significant effect. Solution chemistry analysis further demonstrated that Ca²⁺ and Mg²⁺ have been the primary ions influencing flotation because of their interactions with the mineral surface and collector molecules, but not Fe³⁺, which is mainly adsorbed on the mineral surface in the form of hydrolyzed species, thereby inhibiting the reagent adsorption and enhancing the surface hydrophilicity. Based on this, this paper revealed the differentiated interference mechanisms of different inevitable ions on the flotation of bastnaesite, and applied the relevant insights to guide the recovery of rare earth resources in molybdenum tailings, providing a theoretical basis and new research ideas for the flotation control of bastnaesite and the efficient utilization of rare earth resources under complex backwater conditions. Full article

The circular economy (CE) can help businesses use resources more efficiently, but empirical evidence on CE adoption among non-European SMEs remains limited. This study examines CE practices, benefits, challenges, and future intentions in 220 Saudi Arabian SMIs. A structured survey collected data on four CE practice domains (resource efficiency, waste management, eco-design, and reverse logistics), four benefit dimensions (economic, environmental, operational, and reputational), four challenge dimensions (financial, organizational, technical, and regulatory), and six future intention items. CE adoption was moderate (M = 3.29 on a five-point scale) and balanced across all four practice domains, with resource efficiency scoring highest (M = 3.32). Benefit scores averaged 3.46, far outpacing challenges (M = 2.78). This benefit surplus of 0.68 points (on a five-point scale) indicates that Saudi SMIs perceive CE as worthwhile and view its barriers as manageable rather than prohibitive. Together, perceived benefits and perceived challenges explained 54.3% of the variance in CE adoption (R² = 0.543) in multiple regression analysis. Reducing perceived challenges may be a more effective lever for promoting CE adoption than amplifying perceived benefits, as challenges exerted a larger absolute standardised effect (β = −0.50) than perceived benefits (β = 0.39). Once perceptions were controlled, perceived benefits and challenges significantly predicted future CE intentions, but current CE practices did not. According to the Theory of Planned Behavior’s attitudinal pathway, firms without CE experience can develop strong forward-looking intentions if the business case is convincing and barriers are perceived as manageable. Technical and organizational barriers outweighed financial ones, indicating the need for capacity-building interventions over supplementary financing, unlike European findings. About 79% of respondents were neutral or positive about government-supported CE expansion. CE adoption did not differ significantly by firm size, geographic location, or ownership structure, suggesting that Vision 2030’s sustainability messaging has established a broad baseline of CE awareness across Saudi SMIs. Full article

Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article

Background/Objectives: The human brain is responsible for controlling various physiological functions, and hence, the presence of tumors in the brain is a major concern in the medical field. The correct identification and categorization of tumors in the brain using Magnetic Resonance Imaging (MRI) is a major requirement for the diagnosis and treatment of a tumor. The proposed research will focus on designing a CNN model that is optimized for tumor image classification. Methods: This research proposes an optimized CNN model featuring strategically placed dropout layers and hyperparameter optimization. This study uses a dataset of 640 MRI scans (320 tumor and 320 non-tumor) collected from a private hospital in Saudi Arabia. The proposed method utilizes a learning rate of 0.001 in combination with the Adam optimizer to ensure stable and efficient convergence. Its performance was benchmarked against established architectures, including VGG-19, Inception V3, ResNet-10, and ResNet-50, with evaluation based on classification accuracy and computational cost. Results: The experimental results show that the optimized CNN proposed in this work performs much better than the deeper architectures. The network reached a maximum training accuracy of 97.77% and a final test accuracy of 95.35% with a small test loss of 0.2223. The test accuracy of the optimized VGG-19 and Inception V3 networks was much lower, with a training time per epoch that was several orders of magnitude higher. The validation stability of the proposed network was high (92.25% to 95.35%) during the final stages of training. Conclusions: The conclusion drawn from this study is that hyperparameter optimization and strategic regularization are more advantageous for tumor classification using MRI images than the mere depth of the model. The accuracy of 95.35% with low computational complexity makes this lightweight CNN model a feasible solution for real-time applications. Full article

This study evaluates the potential of marc, a by-product of clitoria (Clitoria ternatea L.) and borage (Borago officinalis L.) infusions, as a preliminary step toward their subsequent conversion into functional food ingredients. After infusion, the marc was collected and processed by carrier-assisted crushing, aqueous maceration, and subsequent separation into extract and residue fractions. The impact of flower pretreatment by milling and marc matrix modification by inulin and maltodextrin was studied on the physical (dry matter (DM), water activity, color), chemical (total phenolic content (TPC), sum of individual phenolic compounds, and antioxidant capacity), and solubility of the microencapsulated fractions. Inulin-formulated powders derived from intact flowers’ marc were characterized by higher dry matter, decreased water activity, and improved chemical profiles. Under these conditions, clitoria by-products exhibited mean dry matter 94.17 ± 0.20%, water activity 0.301 ± 0.003, TPC 3.285 ± 0.052 mg GAE/g DM, sum of individual phenolic compounds 6.267 ± 0.103 mg/g DM, and ABTS-determined antioxidant capacity 0.100 ± 0.001 mmol Trolox/g DM. For borage by-products under identical conditions, dry matter content (−1.60%), water activity (−12.62%), TPC (−39.82%), sum of individual phenolic compounds (−67.55%), and antioxidant capacity (−65.00%) were lower compared with clitoria by-products. An efficient extraction and stabilization approach can open opportunities for upcycling post-extraction herbal residues into high-value food ingredients. Full article

Small ruminant lentiviruses are longstanding viral infections affecting sheep and goats worldwide, resulting in reduced efficiency and economic losses. In Portugal, updated epidemiological data are scarce. The aim of this study was to assess the seroprevalence and risk factors for SRLV in Portugal. The study was conducted in Portuguese flocks of ovine and caprine species. Flocks were randomly chosen, and producers were invited to answer a questionnaire. The indirect ELISA test, ID Screen^® MVV/CAEV Indirect, was made to detect infection. We collected samples from 59 flocks, of which 55.93% (CI 95%: 43.26–68.60%) had at least one positive animal. Of these flocks, 1302 individual samples presented a seroprevalence of 32.95% (CI 95%: 30.08–35.81%). Regarding the risk factor analysis, the multivariable mixed-effects logistic regression model at the individual level identified variables with increased odds of SRLV seropositivity. Caprine species (OR = 2.47; CI 95%: 1.01–6.03), non-autochthonous breed (OR = 2.95; CI 95%: 1.23–7.06), animals older than two years old (OR = 1.95; CI 95%: 1.29–2.94), dairy aptitude (OR = 8.15; CI 95%: 2.53–26.24), unknown serostatus of newly acquired animals (OR = 9.41; CI 95%: 2.93–30.23) and participation in livestock competitions (OR = 4.25; CI 95%: 1.42–12.73) were significantly associated with increased odds of seropositivity. SRLV has been confirmed in both regions of Portugal studied, with a significant regional disparity that is likely attributable to differences in management practices. The identification of risk factors specific to each production system is crucial for the development and implementation of voluntary control programs. Full article

This study investigated the interactive effects of corn silage and ramie silage on in vitro rumen fermentation characteristics, aiming to provide a scientific basis and empirical evidence for the rational incorporation of ramie into ruminant diets. Four binary substrate mixtures were formulated based on dry matter (DM) mass ratios of corn silage to ramie silage: 100:0 (CON), 60:40 (R40), 20:80 (R80), and 0:100 (R100). Rumen fluid was collected from three adult Liuyang black goats surgically fitted with permanent rumen cannulas, and a standardized 48 h in vitro batch culture assay was conducted. Results demonstrated that increasing the proportion of ramie silage significantly decreased (p < 0.05) the DM degradation rate, neutral detergent fiber (NDF) degradation rate, acid detergent fiber (ADF) degradation rate, and total gas production per gram of substrate DM. Specifically, CON and R40 exhibited significantly higher values for all four parameters than R80 and R100 (p < 0.05). Methane production was significantly reduced in all ramie-containing treatments relative to CON (p < 0.05), whereas hydrogen production increased progressively with ramie inclusion level, with CON yielding significantly less H₂ than both R80 and R100 (p < 0.05). Regarding fermentation parameters, increasing ramie proportion elevated (p < 0.05) both fermentation fluid pH and the acetate-to-propionate ratio, while total volatile fatty acid (TVFA) concentration declined linearly (p < 0.05). TVFA concentrations did not differ significantly between CON and R40, yet both were significantly greater than those in R80 and R100 (p < 0.05). Collectively, these findings indicate that ramie silage is a nutritionally valuable forage with potential as a high-quality partial replacement for conventional silages in ruminant feeding systems; however, its inclusion in corn–ramie mixed silages should not exceed 40% (on a DM basis) to maintain optimal fermentative efficiency and nutrient degradability. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

Influenza A virus (IAV) surveillance in swine relies heavily on molecular detection, yet RNA stability in diagnostic specimens such as oral fluids can be rapidly compromised when cold-chain conditions are not maintained. This pilot study evaluated the ability of four molecular-grade carbohydrates (20% trehalose, sorbitol, sucrose, and mannitol) and two commercial nucleic acid stabilizers (PrimeStore^® MTM and RNAlater^®) to preserve RT-qPCR-detectable IAV RNA in swine oral fluids exposed to field-relevant stress conditions. Oral fluid samples collected from pigs experimentally infected with H1N2 (Study 1: n = 150; DPIs 2, 3, 4) or with H1N2 and H3N2 (Study 2: n = 58; DPI 5) were subjected to storage at 25 °C for up to 144 h (Study 1) or 2, 5, 10, or 15 freeze–thaw cycles (Study 2), with DPIs (Study 1) or subtypes (Study 2) serving as biological replicates, given the limited sample size. IAV detection was quantified as efficiency standardized Cq values (ECq) and analyzed using a linear mixed-effects model. Overall, both carbohydrates (trehalose, sorbitol, sucrose) and commercial stabilizers maintained higher ECq values than untreated oral fluids under both thermal and freeze–thaw stress conditions. Due to the limited sample size, these findings should be interpreted cautiously, yet they demonstrate the potential utility of carbohydrates as a low-cost, non-inactivating alternative for stabilizing IAV RNA in field-collected oral fluids. Full article

Background: Tartary buckwheat (Fagopyrum tataricum) serves as an excellent model for studying plant water adaptation mechanisms due to its exceptional drought tolerance. While aquaporins (AQPs) mediate the transmembrane transport of water and solutes in plants, their fine-tuned regulatory networks underlying stress resilience in Tartary buckwheat remain largely elusive. Methods: Here, we combined bioinformatics and transcriptomics to systematically identify 30 highly conserved FtAQP genes at the genome-wide level. Results: Cross-validated by qRT-PCR, our analysis revealed their distinct expression patterns across different organs. Based on our transcriptomic data, we hypothesize that FtAQP family members potentially participate in a coordinated whole-plant water management network through differential spatiotemporal expression. Specifically, the robust transcription of FtAQP8, FtAQP12, and FtAQP28 in roots is associated with the initial water uptake process. As water undergoes long-distance transport, the synergistic upregulation of FtAQP13, FtAQP17, FtAQP20, and FtAQP29 in the stem suggests a potential role in facilitating critical lateral water flow. Furthermore, during reproductive development, FtAQP27 exhibits extreme tissue specificity in floral organs, implying its possible involvement in maintaining local osmotic homeostasis. Furthermore, the promoter regions of FtAQPs are highly enriched with cis-acting elements responsive to light, abscisic acid (ABA), and cold stress, suggesting they are intimately regulated by a coupling of endogenous phytohormones and environmental cues. Conclusions: Ultimately, this study provides valuable insights into the potential molecular basis of multidimensional water regulation in Tartary buckwheat, and identifies candidate genetic targets for improving water use efficiency in dryland agriculture through the precise manipulation of aquaporins. Collectively, while these observational findings provide valuable predictive models, future in vivo experimental validations are required to confirm their exact biological functions. Full article

Biodiesel is a sustainable and promising alternative energy source produced from renewable raw materials using various methods. One effective approach is simultaneous esterification and transesterification, which relies on suitable catalysts that can be either homogeneous or heterogeneous. Homogeneous catalysts (acid or base) offer high activity but are corrosive and difficult to recover, necessitating energy-intensive processes such as aqueous quenching and neutralization, which can lead to soap formation and stable emulsions. By comparison, heterogeneous catalytic systems overcome many of these challenges due to their ease of recovery, reusability, and simplified product separation, which collectively enhance economic viability and environmental sustainability. This review highlights recent progress in the application of zeolite-based solid catalysts for biodiesel synthesis, with particular emphasis on their use in converting waste cooking oil and other low-cost feedstocks, including non-edible oils, non-food biomass sources, algal resources, and genetically engineered microorganisms. Key factors such as catalytic activity, selectivity, catalyst loading, and reusability are discussed, highlighting the advantages of zeolites due to their unique crystal structure, high thermal stability, and ease of product recovery. Overall, this review underscores the challenges and opportunities in zeolite-based catalysis to provide a comprehensive understanding of its potential to enhance the efficiency and scalability of biodiesel production. Full article

The synergistic effects of soil acidification and heavy metal pollution present major challenges for global agroecosystems. To systematically trace the evolution of research and identify key topics in this field, this study employed CiteSpace to visualize and analyze 691 records from the China National Knowledge Infrastructure (CNKI) and 6747 highly relevant articles or reviews from the Web of Science (WOS) Core Collection database from 2005 to 2025. The results indicate a steady to rapid rise in global publications, with China contributing the largest share, at 2468 publications. This has produced a research cluster centered around the Chinese Academy of Sciences (CAS); however, the centrality of its international cooperation remains limited. Studies in the CNKI database are driven by agricultural needs, focusing on national food security, rice yield stability, improvement of arable land, and heavy metal passivation and remediation, with a concentration on basic agricultural science. By contrast, research in the WOS database emphasizes fundamental mechanisms and interdisciplinary integration, addressing aluminum toxicity, microbial communities, the nitrogen cycle, and global climate change, intersecting fields such as environmental science, soil science, ecology, and microbiology. The evolution of research hotspots shows a clear trajectory: from acidity regulation and chemical speciation analysis of heavy metals (2005–2013), to heavy metal passivation, remediation, and phytoremediation (2014–2018), and then to biochar materials, microbiome analysis, and the synergistic role of carbon sequestration (2019–2025). This study argues that future research should move beyond single remediation measures and adopt integrated strategic management to jointly improve bioremediation efficiency, promote soil carbon sequestration and soil health, and enhance microbial adaptation to global climate change. Full article

Water age reflects water sources, storage, and pathways, and regulates the solute retention and dissolution associated with biogeochemical processes, highlighting its hydrological and ecological importance. However, accurate water age estimation in tracer-aided models depends heavily on the quality and spatio-temporal resolution of precipitation isotopic signals. This study investigates how distributed rainfall δ²H signals affect the simulation of young water fraction (F_yw) via the Storage Age Selection (SAS) model in topographically complex subtropical mountain catchments. Eight precipitation δ²H scenarios were generated using two temporal approaches (stepwise and sinewave) and four spatial interpolation methods: (1) raw data, (2) reversed effective recharge elevation method (rERE), (3) linear regression with elevation (ER), and (4) regression-kriging (RK). Later on, the time-variant SAS model was calibrated against observed stream water δ²H collected from the year 2022 to the year 2024. Results show that the SAS model consistently produced similar F_yw estimates for catchments (8%~40%) across all eight scenarios, demonstrating strong robustness to input uncertainty and validating the dominant role of catchment characteristics in regulating water age. The combined stepwise temporal and rERE spatial approach provided better agreement with observed stream δ²H, particularly in the eastern, steeper catchments, yielding superior model efficiency along with better constrained uncertainty. This study highlights the sensitivity of age-tracking models to precipitation isotopic inputs and provides practical guidance for selecting an interpolation strategy in data-limited mountainous environments. Full article

Background: Etiologies of neural tube defects (NTDs) are multifactorial. Genetic, epigenetic and environmental factors may contribute to their reported variation in prevalence across the globe. Ethiopia has among the highest reported NTD prevalence globally, making investigation of genetic determinants in this high-risk population particularly important for advancing the understanding of NTD etiology. Genes involved in folate metabolism, such as the reduced folate carrier 1 (RFC1), have been investigated for the potential associations with NTDs, but findings throughout the literature remain inconsistent and inconclusive. Objective: The aim of this study was to determine an association of RFC-1 polymorphism at rs1131596 and rs1051266 loci (functional variants previously implicated in folate transport efficiency and NTD susceptibility) among mothers with the occurrence of NTDs in their offspring in Ethiopia. Methods: A case–control study involving 250 mothers (187 controls and 63 cases) of children with or without NTDs was conducted in Addis Ababa, Ethiopia from April 2022, to September 2024. A total of 250 maternal whole blood samples were systematically collected and subjected to genetic analysis at loci rs1131596 and rs1051266 by polymerase chain reaction (PCR) and Sanger sequencing. Results: Detection of heterozygous (TC) and homozygous (CC) genotypes for SNP rs1131596 (−43T>C) in the RFC1 gene was 27.2%, with heterozygous (TC) comprising 10.4% and homozygous (CC) 16.8%. In contrast, for the rs1051266 (80A>G), the prevalence of the AG polymorphism was 28% while the GG polymorphism was 16.4%, resulting in a cumulative prevalence of 44.4%. The presence of maternal RFC-1 polymorphism at these two locations were not associated with significantly (p = 0.601 & p = 0.225 respectively) higher odds for NTD births. Conclusions: This study did not reveal significant association between maternal RFC1 gene polymorphisms and NTD-affected births. Comprehensive whole-genome sequencing of affected off-spring is essential to identify specific mutations or polymorphisms that may individually or collaboratively affect the risk of NTDs in the Ethiopian context. Full article