Search Results (53,893)

The aim of the study was to evaluate how controlled laboratory addition with Pb, Cd, and Cu affects the fatty acid profile of milk and acid-coagulated cheese from three geographical regions (R1, R2, R3), considering the influence of regional characteristics and the March–April 2025 harvesting period. Comparative analysis of the lipid profile (SFA and UFA) and the ratios between fatty acids showed that region R2 displayed the most balanced nutritional structure, followed by regions R1 and R3. The lipid indices (IA 2.5–4, IT 3–4.4, HH 0.4–0.6, HPI 0.2–0.4) confirmed this pattern across all regions, indicating that R2 is characterized by a favorable, antiatherogenic, and antithrombotic lipid profile, whereas R1 exhibits an intermediate profile and R3 a markedly unbalanced profile. The same trend was observed for the lipid composition of the blank cheese samples. Heavy metal fortification produced major shifts in fatty acid composition and lipid indices. At the maximum level permitted by legislation, the changes were moderate, with SFA increasing from 71% to 77% and essential ω-3 and ω-6 PUFA decreasing, resulting in increased IA and IT and reduced HH and HPI. At 10× the maximum limit, the lipid profile became severely unbalanced: SFA increased to 81%, UFA dropped to 17%, ω-3 fatty acids were nearly absent, and ω-6 levels declined sharply, amplifying their imbalance. These changes were accompanied by a substantial deterioration in all lipid indices. These findings demonstrate that fatty acid composition (SFA, MUFA, PUFA) and lipid parameters (IA, IT, HH, HPI) serve as highly sensitive markers of heavy metal-induced oxidative stress in dairy products. Overall, the study shows that while the fatty acid profiles of milk from different regions reliably indicate both geographical origin and nutritional quality, exposure to heavy metal addition profoundly disrupts these profiles, together with their lipid indices, producing changes significant enough to signal compromised safety and diminished functional value of the resulting cheese. Full article

The lake systems of the Qinghai–Tibet Plateau, while serving as vital hubs for socioeconomic development, have become critical zones of heavy metal contamination, posing severe threats to the fragile “Third Pole” ecosystem and regional environmental security. This study investigated the concentration, distribution, sources, and ecological risks of eight heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) in lake sediments and riparian soils of Bangong Co Lake, a remote alpine lake on the Qinghai–Tibet Plateau. Lake sediment and soil samples were collected and tested from various shoreline types, including natural and human-affected areas. The Pollution Load Index (PLI) was applied to assess contamination levels, and source apportionment was performed using principal component analysis (PCA) combined with the Absolute Principal Component Score–Multiple Linear Regression (APCS-MLR) receptor model. Results revealed that heavy metal concentrations were generally higher in soils than in sediments. Compared to regional background values, elevated levels of most heavy metals were observed in human-affected shores, while natural-type soils exhibited higher concentrations of Co, Cr, Ni, and As. In sediments, only Cd and As were notably elevated in human-affected areas. The PLI results indicated that most sampling sites were either uncontaminated or slightly contaminated, with higher pollution levels occurring primarily in human-affected shoreline zones. Source apportionment demonstrated that heavy metals in sediments were predominantly derived from natural sources such as rock weathering, with anthropogenic contributions being relatively limited. In contrast, soils exhibited significant anthropogenic influences, with industrial, transportation, and agricultural activities contributing substantially to Cu (53.27%), Pb (58.64%), Zn (57.98%), Cd (34.09%), and As (39.87%). The research underscores the differential impacts of human activities on heavy metal accumulation in sediments and soils of high-altitude lake systems. It offers valuable baseline data for monitoring and managing heavy metal pollution in ecologically sensitive alpine regions. Full article

Background: Oral lichen planus (OLP) is a chronic inflammatory disease of unknown etiology. Its clinical and histopathological diagnosis remains challenging due to the variability of its manifestations and the subjectivity involved in interpretation. Objective: This study aimed to examine the relationship between different clinical phenotypes of OLP (reticular, erosive, and mixed) and histomorphological features obtained through digital analysis with semi-automated segmentation. Methods: A retrospective review of 100 OLP cases was conducted. Clinically, the samples were classified into three groups: 68 reticular, 16 erosive, and 16 mixed. Epithelial and connective tissue parameters were evaluated on hematoxylin–eosin-stained sections using digital tools and segmentation algorithms. Results: The erosive phenotype showed greater irregularity of suprabasal nuclei (p = 0.008) and a higher basal nucleus-to-cytoplasm ratio (p = 0.02). No significant differences were found among the groups regarding epithelial thickness or lymphocyte density (p > 0.05). Conclusions: The cellular alterations observed in the erosive subtype may reflect higher tissue activity and provide additional elements for its characterization. Digital morphometric analysis appears to be a promising complementary tool, although further studies are needed to confirm its diagnostic applicability. Full article

Background: Advancements in low-cost additive manufacturing and artificial intelligence have enabled new avenues for developing accessible myoelectric prostheses. However, achieving reliable real-time control and ensuring mechanical durability remain significant challenges, particularly for affordable systems designed for resource-constrained settings. Objective: This study aimed to design and validate a low-cost, 3D-printed prosthetic arm that integrates single-channel electromyography (EMG) sensing with machine learning for real-time gesture classification. The device incorporates an anatomically inspired structure with 14 passive mechanical degrees of freedom (DOF) and 5 actively actuated tendon-driven DOF. The objective was to evaluate the system’s ability to recognize open, close, and power-grip gestures and to assess its functional grasping performance. Method: A Fast Fourier Transform (FFT)-based feature extraction pipeline was implemented on single-channel EMG data collected from able-bodied participants. A Support Vector Machine (SVM) classifier was trained on 5000 EMG samples to distinguish three gesture classes and benchmarked against alternative models. Mechanical performance was assessed through power-grip evaluation, while material feasibility was examined using PLA-based 3D-printed components. No amputee trials or long-term durability tests were conducted in this phase. Results: The SVM classifier achieved 92.7% accuracy, outperforming K-Nearest Neighbors and Artificial Neural Networks. The prosthetic hand demonstrated a 96.4% power-grip success rate, confirming stable grasping performance despite its simplified tendon-driven actuation. Limitations include the reliance on single-channel EMG, testing restricted to able-bodied subjects, and the absence of dynamic loading or long-term mechanical reliability assessments, which collectively limit clinical generalizability. Overall, the findings confirm the technical feasibility of integrating low-cost EMG sensing, machine learning, and 3D printing for real-time prosthetic control while emphasizing the need for expanded biomechanical testing and amputee-specific validation prior to clinical application. Full article

Deep learning (DL), a hierarchical feature extraction method, has garnered increasing attention in the remote sensing community. Recently, self-supervised learning (SSL) methods in DL have gained wide recognition due to their ability to mitigate the dependence on both the quantity and quality of samples. This advantage is particularly significant when dealing with limited labeled samples in hyperspectral images (HSIs). However, conventional SSL methods face two main challenges. They struggle to construct self-supervised signals based on the unique characteristics of HSI. Moreover, they fail to design network optimization strategies that leverage the intrinsic manifold geometry within HSI. To tackle these issues, we propose a novel self-supervised learning method termed Manifold Geometry-Leveraged Self-supervised Learning (MSSL) for HSI classification. The approach employs a two-stage training strategy. In the initial pre-training stage, it develops self-supervised signals that exploit spatial homogeneity and spectral coherence properties of HSI. Furthermore, it introduces a manifold geometry-guided loss function that enhances feature discrimination by increasing intra-class compactness and inter-class separation. The second stage is a fine-tuning phase utilizing a small set of labeled samples. This stage optimizes the pre-trained model, enabling effective feature extraction from hyperspectral data for classification tasks. Experiments conducted on real-world HSI datasets demonstrate that MSSL achieves superior classification performance compared to several relevant state-of-the-art methods. Full article

Soil permeability is an important factor in the mining and geotechnical industry, impacting slope stability and tailings management. It directly influences the stability of structures, the control of water in tailings ponds, and the safety of workers. Various additives, such as cement kiln dust (CKD), bentonite, fly ash, polymers, lime, and asphalt, are incorporated into soil structures to improve permeability and stability. Any significant changes in soil permeability will alter the soil’s behavior. However, the long-term effect of most additives on structures remains unexplored. This study investigates the long-term impact of CKD on the permeability of a CKD-treated slope. The slope surface was treated with 0%, 5%, 10%, and 15% of CKD by the dry weight of the soil in 2008 and was evaluated in 2024. The permeability test results of the collected soil sample from the slope (2024) showed that the permeability of the soil decreases with an increase in the soil CKD content. The coefficient of permeability, k, is more than 100 times less for a CKD content of 15% by the dry weight of the soil compared to the permeability of the untreated native soil. The treated soil becomes almost impermeable when the CKD content increases to 20% (by the dry weight of the soil). However, the treated slope’s permeability increased over time, possibly due to erosion, resulting in a reduction in CKD content. The surface permeability of the slope exhibits an irregular distribution, resulting from the evolving spatial distribution of Cement Kiln Dust over time. Full article

Background/Objectives: Seasonal influenza, pneumococcal disease, and COVID-19 pose major public health challenges, particularly for individuals with chronic illnesses. This study examined vaccination coverage for influenza, pneumococcal disease, and SARS-CoV-2 among patients with chronic liver disease and cirrhosis and explored the sociodemographic and clinical factors influencing it. Methods: A cross-sectional study, conducted from March 2022 to July 2023 at two university hepatology outpatient clinics in Athens, Greece. The study population consisted of patients with a diagnosis of chronic liver disease (hepatocellular carcinoma and hepatitis) and liver cirrhosis. Results: A convenience sample size of 300 patients (age ≥ 30) participated. Regarding their vaccination, 88.3% were vaccinated against SAR-COVID-19, 44.8% against pneumococcus, and 54.7% against seasonal influenza this year. Patients’ belief that annual vaccination is the best method for influenza prevention was found to be significantly higher among older patients and those with comorbidities. Additionally, patients who had been vaccinated against seasonal influenza (this year or every year), against pneumococcus, or SARS-CoV-2 agreed significantly that annual vaccination is the best method for influenza prevention. In addition, patients who were informed about vaccination by their doctor/nurse agreed significantly more with that. Multiple logistic regression found that a four times greater probability of being fully vaccinated according to the national vaccination program was found in patients who were informed about vaccination by a doctor/nurse. Moreover, as patients’ age increased, so did the probability of being fully vaccinated. Conclusions: The study’s findings are significant and can be utilized within national public health initiatives and by healthcare professionals during patient interactions, ensuring that younger patients and those apprehensive about vaccine efficacy and safety receive focused attention to facilitate adherence to annual vaccinations and all vaccines included in national programs. Full article

Multifunctional nanomaterials have been extensively investigated in theranostics to enhance therapeutic specificity, biocompatibility, and responsiveness to external magnetic gradients. We synthesized magnetoplasmonic nanocomposites comprising magnetite nanoparticles modified with gold and silver. Magnetite was synthesized via chemical co-precipitation and stabilized in an aqueous medium using glucose, which also served as a reducing agent for Au³⁺ and Ag⁺ ions on the nanoparticle surface. Microstructural, magnetic, spectral, and optical characterizations confirmed the successful formation of nanocomposites with properties suitable for biomedical applications. Plasmonic behavior was evidenced by visible-range absorbance maxima at 398 nm (Ag) and 538 nm (Au), while Transmission Electron Microscopy (TEM) revealed mean diameters of 21 and 23 nm. Zeta potential values of +23 mV for magnetite–silver and −40 mV for magnetite–gold nanocomposite samples indicated good suspension stability. Antibacterial activity against Gram-positive and Gram-negative bacteria was evaluated using agar diffusion and by determining the minimum inhibitory (MIC) and bactericidal (MBC) concentrations. Silver-modified magnetite nanocomposites exhibited the most potent effects, with MIC values of 0.01 mg/mL for Escherichia coli (E. coli) and 0.02 mg/mL for Staphylococcus aureus (S. aureus), and corresponding MBC values of 0.027 mg/mL and 0.055 mg/mL, respectively. These magnetoplasmonic nanostructures have significant potential for overcoming antibiotic resistance and enabling targeted therapeutic action through magnetic guidance. Full article

Although general-domain Named Entity Recognition (NER) has achieved substantial progress in the past decade, its application to cybersecurity NER is hindered by the lack of publicly available annotated datasets, primarily because of the sensitive and privacy-related nature of security data. Prior research has largely sought to improve performance by expanding annotation volumes, while overlooking the intrinsic characteristics of training data. In this study, we propose a cybersecurity Named Entity Recognition (NER) method based on hard and easy labeled training data discrimination. Firstly, a hybrid strategy that integrates a deep learning (DL)-based discriminator and a rule-based discriminator is employed to partition the original dataset into hard and easy samples. Secondly, the proportion of hard and easy data in the training set is adjusted to determine the optimal balance. Finally, a data augmentation algorithm is applied to the partitioned dataset to further improve model performance. The results demonstrate that, under a fixed total training data size, the ratio of hard to easy samples has a significant impact on NER performance, with the optimal strategy achieved at a 1:1 proportion. Moreover, the proposed method further improves the overall performance of cybersecurity NER. Full article

Background/Objectives: In Brazil, quilombos—African-descendant resistance communities—emerged during slavery and persisted beyond its abolition. The state of Paraná, in Southern Brazil, is home to 86 quilombos, yet their genetic diversity remains entirely unexplored, and little is known about their subcontinental African origins. Methods: To explore the demographic history of these communities and the reach of the Transatlantic Slave Trade in Southern Brazil, we analyzed Y and mitochondrial DNA haplotypes in samples from two quilombo communities from Paraná, Feixo (n = 117) and Restinga (n = 47). Results: Our findings reveal a significant African maternal ancestry in both communities, with Feixo exhibiting 35% and Restinga showing a striking 78.72% of maternal haplogroups of African origin. Feixo’s mtDNA haplotypes display affinities with Bantu-speaking populations from Central-Western and Southeastern Africa (such as Angola, Congo, and Mozambique), whereas those found in Restinga are more closely aligned with lineages frequent in Western Africa. Y-chromosome data reveal 39.4% and 25% African paternal ancestry in Feixo and Restinga, respectively, with most African chromosomes assigned to haplogroup E1b1b1-M35, which has a broad frequency across eastern Africa. Conclusions: These results offer novel insights into the history of the African diaspora in a previously unstudied Brazilian region, suggesting African sources—including underdocumented Eastern/Southern lineages—and contributing useful new clues to their broader within-Africa affinities. Full article

Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets. Full article

Biological samples of non-human origin, commonly encountered in wildlife crime investigations, present distinct challenges regarding forensic DNA analysis efforts. Although the types of samples encountered in human identity testing can vary to some degree, analyzing DNA from one species is facilitated by unified processes, common genetic marker systems, and national DNA databases. In contrast, non-human animal species identification is confounded by a diverse range of target species and a variety of sampling materials, such as feathers, processed animal parts in traditional medicine, and taxidermy specimens, which often contain degraded DNA in low quantities, are contaminated with chemical inhibitors, and may be comingled with other species. These complexities require specialized analytical approaches. Compounding these issues is a lack of validated non-human species forensic sampling and typing kits, and the risk of human DNA contamination during evidence collection. Markers residing on the mitochondrial genome (mtDNA) are routinely sought because of the large datasets available for comparison and their greater sensitivity of detection. However, the barcoding results can be complicated at times for achieving species-level resolution, the presence of nuclear inserts of mitochondrial DNA (NUMTs), and the limitation of mtDNA analysis alone to detect hybrids. Species-specific genetic markers for identification have been developed for a few high-profile species; however, many CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora)-listed organisms lack specific, validated forensic analytical tools, creating a significant gap in investigative enforcement capabilities. This deficiency stems in part from the low commercial nature of wildlife forensics efforts, a government research-driven field, the difficulty of obtaining sufficient reference samples from wild populations, limited training and education infrastructure, and inadequate funding support. Full article

X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and industrial applications. To mitigate such reliance, a novel approach based on the Gated Adaptive Network for Deep Automated Learning of Features (GANDALF) is proposed. To effectively extract multi-scale information from the XANES spectrum, the spectrum was segmented into multiple scales. Each segment was fitted using a pseudo-Voigt function, with the absorption edge position. The GANDALF algorithm, a table-based deep learning approach, was employed to model the coordination environment of absorbing elements. The proposed method was validated using a previously published open-access dataset. For vanadium-containing samples, the model achieved R² values of 0.7837 on test sets with non-integer coordination numbers, whereas the random forest model only achieved 0.6328. Furthermore, our results highlight the significant importance of the post-edge peak when predicting coordination numbers using the full spectrum. Full article

Karstic regions are globally distributed, and the soil-forming parent rocks and their weathering process primarily cause elevated geochemical heavy metal (HM) accumulation in karst soils. However, the patterns of HMs, the genes related to resistance, and their interactions in karstic soils developed from different parent materials remain unexplored. In this study, 36 field karst soil samples originating from two parent materials were collected, including 19 samples from the residues of the weathering and leaching of carbonate rocks (Car) and 17 samples from Quaternary sediments (Qua). In the Car soils, the levels of As, Cd, Cr, Zn, Cu, Ni, and Pb exceeded the risk screening values for soil contamination of agricultural land set by the Chinese standard GB15618-2018 by 100%, 100%, 94.11%, 64.71%, 64.71%, 47.06%, and 41.18%, respectively, while only 11.76% of As in Qua soils exceeded the risk screening values. The proportion of metal resistance genes (MRGs) and antibiotic resistance genes (ARGs) in Car soils was significantly higher than that in Qua soils. However, HM content had a significantly positive correlation with Nemerow integrated pollution index (NIPI), individual HM-related genes, MRGs, ARGs, and mobile genetic elements (MGEs) in Qua soils, respectively. Although the corresponding correlation was positive in the Car soils, it was not statistically significant. Results demonstrated that microbial activity was more crucial for the accumulation of HMs in Qua soils compared with Car soils. Meanwhile, our in-depth research also provides new perspectives to establish a more rational ecological assessment for the elevated HMs by identifying applicable and valid biomarkers from functional genes, which is vital in contamination monitoring, prevention, and standard establishment in agricultural soils of karst regions. Full article

This study examines how physical and transition climate risks affect the greenium, assuming that implied volatility serves as a proxy for investor sentiment generated by these risks. Applying a Gated Recurrent Unit (GRU) deep learning model to daily data from January 2020 to June 2025 with a rigorous train–test split to get around the drawbacks of full-sample estimations and guarantee strong out-of-sample generalizability is a significant empirical contribution. Our findings show that adding the interaction between these climate risks and the sentiment proxy slightly increases predictive power. The GRU model outperforms random forest and linear regression benchmarks in terms of generalizability, but it remains sensitive to different data splits and hyperparameter tuning. This highlights the use of complex, non-linear models for risk forecasting and portfolio allocation for investors and risk managers, as well as the need for regular climate disclosure for policymakers to reduce information asymmetry. The GRU’s stringent validation framework directly enables more reliable pricing and exposure management. Full article