Search Results (813)

Arsenic occurs in food in both inorganic (iAs) and organic (oAs) forms. Inorganic arsenic is highly toxic and classified as carcinogenic to humans, whereas oAs species, such as arsenobetaine (AB), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA), generally exhibit lower toxicity. Rice and rice-based products represent major contributors to dietary iAs exposure. Within this context, the present study provides an updated assessment of the occurrence of iAs and oAs in rice-based beverages available on the Italian market. A method for the simultaneous determination of iAs, AB, DMA, and MMA was developed and validated, and it exhibits adequate sensitivity to ensure robust occurrence data, eliminating left-censoring for iAs. A comprehensive analysis of twenty-five representative rice-based beverages was conducted, revealing that the contamination profiles exhibited a high degree of homogeneity, with iAs as the predominant species. All samples complied with the European maximum level for iAs in non-alcoholic rice-based beverages. When combined with recent Italian consumption data, these results enabled age-specific dietary exposure assessment. Although rice drinks contribute marginally to overall population exposure, estimated intakes for regular consumers in early childhood are associated with a small margin of exposure, raising potential concern for vulnerable subgroups. The increasing diversification of dietary habits and the rising consumption of plant-based beverages point to the necessity of continuous monitoring of iAs. Ongoing efforts in monitoring studies, updated food consumption surveys, and effective risk communication are essential to refine exposure assessment and thereby enhance public health protection. Full article

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may require large networks and struggle with extrapolation. In this paper, we propose a low-latency, polynomial-based IK solution for spherical-wrist robots. The method leverages spherical coordinates and low-degree polynomial fits for the first three (positional) joints, coupled with a closed-form analytical solver for the final three (wrist) joints. An iterative partial-derivative refinement adjusts the polynomial-based angle estimates using spherical-coordinate errors, ensuring near-zero final error without requiring a full Jacobian matrix. The method systematically enumerates up to eight valid IK solutions per target pose. Our experiments against Levenberg–Marquardt, damped least-squares, and an fmincon baseline show an approximate 8.1× speedup over fmincon while retaining higher accuracy and multi-branch coverage. Future extensions include enhancing robustness through uncertainty propagation, adapting the approach to non-spherical wrists, and developing criteria-based automatic solution-branch selection. Full article

Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments. Full article

To investigate the effects of multiple-element rare earth addition on U75V steel, this study produced three types of steel: sample 1 steel without rare earths, sample 2 steel containing 0.0035% La and 0.018% Ce, and sample 3 steel containing 0.02% La and 0.0023% Ce. Microstructural analysis showed that the addition of rare earth elements modified the MnS and silicoaluminate inclusions into RE₂O₂S and RE₂O₂S–oxide complexes, which reduced the number and size of inclusions while simultaneously refining the microstructure, including the grain size and the spacing of pearlite layers. Concurrently, RE addition enhanced the steel’s mechanical properties, with the degree of enhancement dependent on RE content; sample 2 exhibited the most balanced improvement. Compared to sample 1, the hardness of samples 2 and 3 increased by 15.3% and 3.6%, respectively, and their tensile strength increased by 7.9% and 6.8%, respectively. Meanwhile, their coefficients of friction decreased significantly, by 69.5% and 22.1%. The impact toughness was also enhanced by RE addition, with both samples 2 and 3 showing higher values than sample 1 at room temperature and moderate low temperatures. Nevertheless, a distinct reversal was observed at −60 °C, where the impact energy of sample 3 was 23.5% lower than that of sample 2. This result implies that while moderate RE addition is beneficial, an excessive amount can adversely affect the toughness under cryogenic conditions. Full article

This study investigates the relationship between apparent electrical resistivity (ER) and key material parameters governing moisture and pore-structure characteristics of concrete. An experimental program was conducted using six concrete mix designs, where ER was continuously measured under controlled wetting and drying cycles to characterize its dependence on the degree of saturation (DS). Results confirmed that ER decreases exponentially with increasing DS across all mixtures, with R² values between 0.896 and 0.997, establishing DS as the dominant factor affecting electrical conduction. To incorporate additional pore-structure parameters, eight input combinations consisting of DS, porosity (P), water–cement ratio (WCR), and compressive strength (f′c) were evaluated using five machine learning models. Gaussian Process Regression and Neural Networks achieved the highest accuracy, particularly when all parameters were included. SHAP analysis revealed that DS accounts for the majority of predictive influence, while porosity and WCR provide secondary but meaningful contributions to ER behavior. Guided by these insights, nonlinear multivariate regression models were formulated, with the exponential model yielding the strongest predictive capability (R² = 0.96). The integrated experimental–computational approach demonstrates that ER is governed by moisture dynamics and pore-structure refinement, offering a physically interpretable and statistically robust framework for nondestructive durability assessment of concrete. Full article

STEM education increasingly relies on technology-enhanced environments that utilize data-driven strategies, digital tools, and adaptable learning models. To support the evaluation of contemporary STEM teaching methods, this study proposes a multi-criteria analytical framework based on expert assessment. Semi-structured interviews were conducted with 41 experienced teachers from Bulgarian schools (N = 41), who evaluated six key indicators (m = 6) of STEM integration: Effectiveness, Engagement, Applicability, Flexibility, Validity, and Accessibility. The qualitative data were transformed into numerical values and analyzed using the Target Parameter Ranking method. The degree of expert agreement was assessed through the Morris–Kendall coefficient, yielding a statistically significant moderate agreement (w_k = 0.137; χ² = 28.085, df = 5, p = 3.50 × 10⁻⁵ (p < 0.001)). The results indicate that Engagement (W_j = 0.206), Flexibility (W_j = 0.188), and Effectiveness (W_j = 0.186) are the most highly weighted criteria, reflecting teachers’ prioritization of active participation, learning outcomes, and adaptability in technology-rich STEM environments. In comparison, Applicability and Accessibility show higher variability, highlighting their dependence on contextual factors such as infrastructure and resource availability. The proposed framework provides a structured, data-driven basis for evaluating and refining STEM teaching practices and can be integrated into educational decision-support systems. Full article

Vista landscapes of historic cities embody unique spatial order and cultural memory, and the scientific quantification of their visual quality presents a common challenge for both heritage conservation and urban renewal. Focusing on the Beijing Central Axis, this study integrates VR panoramic technology with the SBE-SD evaluation method to develop a visual quality assessment framework suitable for vista landscapes of historic cities, systematically evaluating sectional differences in scenic beauty and identifying their key influencing factors. Thirteen typical viewing places and 17 assessment points were selected, and panoramic images were captured at each point. The evaluation framework comprising 3 first-level factors, 11 secondary factors, and 24 third-level factors was established, and a corresponding scoring table was designed through which students from related disciplines were recruited to conduct the evaluation. After obtaining valid data, scenic beauty values and landscape factor scores were analyzed, followed by correlation tests and backward stepwise regression. The results show the following: (1) The scenic beauty of the vista landscapes along the Central Axis shows sectional differentiation, with the middle section achieving the highest scenic beauty value, followed by the northern section, with the southern section scoring the lowest; specifically, Wanchunting Pavilion South scored the highest, while Tianqiao Bridge scored the lowest. (2) In terms of landscape factor scores, within spatial form, color scored the highest, followed by texture and scale, with volume scoring the lowest; within marginal profile, integrity scored higher than visual dominance; within visual structure, visual organization scored the highest, followed by visual patches, with visual hierarchy scoring the lowest. (3) Regression analysis identified six key influencing factors, ranked in descending order of significance as follows: color coordination degree of traditional buildings, spatial openness, spatial symmetry, hierarchy sense of buildings, texture regularity of traditional buildings, and visual dominance of historical landmark buildings. This study establishes a quantitative assessment pathway that connects subjective perception and objective environment with a replicable process, providing methodological support for the refined conservation and optimization of vista landscapes in historic cities while demonstrating the application potential of VR panoramic technology in urban landscape evaluation. Full article

Direct injection in hydrogen engines enables flexible combustion control, improves engine efficiency, and reduces the risk of abnormal combustion. However, implementing this injection strategy is challenging due to the need to provide a relatively high volumetric fuel flow rate, achieve a specified degree of mixture stratification, and account for the functional and technological limitations of the injection system. These challenges highlight the relevance and objectives of the present study. The mathematical model of a turbocharged engine cycle has been refined to account for the influence of injection parameters on combustion kinetics. On the basis of mathematical modeling, the injection pressure and injector area were determined to ensure the specified injection conditions. For the late injection strategy, a method was proposed to select the start of injection based on a specified value of the “relative ignition timing” criterion. Engine operation was simulated across the full range of operating modes for both early and late injection strategies. The results show that the late injection strategy increases the maximum indicated thermal efficiency by approximately 2%, reduces peak in-cylinder pressure by about 1 MPa, lowers maximum nitrogen oxide emissions by a factor of 1.4, and ensures knock-free operation across all modes compared to early injection. Full article

Machine learning (ML) is a powerful tool in drug design, enabling the rapid analysis of large and complex molecular graphs that represent the structural and chemical properties of medications. It enhances the precision and speed with which molecular interactions are predicted, drug candidates are refined, and potential therapeutic targets are identified. When combined with graph theory, ML allows for the prediction of structural properties, molecular behaviour, and the performance of chemical compounds. This integration promotes drug development, reduces costs, and increases the likelihood of producing effective medicines. In this study, we focus on the efficacy of medications used in the treatment of coronary artery disease (CAD) using graph-theoretical methodologies, such as topological indices. We computed several degree-based topological descriptors from chemical graphs, capturing essential connectivity and structural properties. These variables were incorporated into a machine learning framework to develop predictive models that identify structural factors influencing medication performance. Our study explores a dataset of known CAD drugs using supervised learning techniques to estimate their potential efficacy and support improved molecular design. The findings highlight the utility of graph-theoretical descriptors in enhancing prediction accuracy and providing insights into fundamental structural elements related to drug efficacy. Furthermore, this work emphasises the synergy between chemical graph theory and machine learning in accelerating drug development for CAD, offering a scalable and interpretable framework for future pharmaceutical applications. Full article

Recent studies show that titanium (Ti)-based alloys combine established mechanical strength, corrosion resistance, and biocompatibility with emerging electrical and electrochemical properties relevant to bioelectronics. The main goal of the present manuscript is to give a wide-ranging overview on the use of Ti-alloys in electronics and biomedicine, focusing on a comprehensive analysis and synthesis of the existing literature to identify gaps and future directions. Concurrently, the identification of possible correlations between the effects of the manufacturing process, alloying elements, and other degrees of freedom influencing the material characteristics are put in evidence, aiming to establish a global view on efficient interdisciplinary efforts to realize high-added-value smart devices useful in the field of biomedicine, such as, for example, implantable apparatuses. This review mostly summarizes advances in surface modification approaches—including anodization, conductive coatings, and nanostructuring that improve conductivity while maintaining biological compatibility. Trends in applications demonstrate how these alloys support smart implants, biosensors, and neural interfaces by enabling reliable signal transmission and long-term integration with tissue. Key challenges remain in balancing electrical performance with biological response and in scaling laboratory modifications for clinical use. Perspectives for future work include optimizing alloy composition, refining surface treatments, and developing multifunctional designs that integrate mechanical, biological, and electronic requirements. Together, these directions highlight the potential of titanium alloys to serve as foundational materials for next-generation bioelectronic medical technologies. Full article

The Lower Danube region represents a long-standing zone of interaction between indigenous communities, mobile pastoral populations, and Mediterranean colonial networks. During the Late Iron Age, such contexts have frequently been interpreted through culture-historical frameworks that emphasise socio-economic differentiation among coexisting populations. This study examines whether communities traditionally described in culturally or economically differentiated terms can instead be understood as functionally diverse social entities responding to shared environmental conditions. Three Late Iron Age (4th–3rd centuries BC) settlements from Central Dobruja (Romania), Medgidia Hellenistic 1, 2, and 3, were investigated using an integrated bioarchaeological approach combining archaeozoological and phytolith analyses. The sites are situated along a major communication corridor linking the Danube with the western Black Sea coast, colonised by the Greeks at that time. Faunal assemblages are dominated by domestic mammals, particularly cattle, caprine, and horses, indicating a pastoral economy structured around livestock management, secondary product exploitation, and varying degrees of mobility. Phytolith assemblages reveal a strong cereal signal alongside evidence for grassland exploitation, woody resource use, and wetland vegetation, reflecting mixed agro-pastoral practices embedded within a heterogeneous landscape. Taken together, the results suggest that Getae and Scythian-associated populations did not represent temporally or hierarchically differentiated socio-economic stages, but rather coexisting communities characterised by complementary subsistence practices, shaped by mobility, seasonality, and regional connectivity. This study highlights the value of bioarchaeological evidence for refining interpretations of cultural interaction and adaptive strategies in Late Iron Age Europe. Full article

The accurate prediction of crack initiation and propagation is essential for assessing the structural integrity of mechanically joined components and other complex assemblies. To overcome the limitations of existing finite element tools, a modular Python framework has been developed to automate three-dimensional crack growth simulations. The program combines geometric reconstruction, adaptive remeshing, and the numerical evaluation of fracture mechanics parameters within a single, fully automated workflow. The framework builds on open-source components and remains solver-independent, enabling straightforward integration with commercial or research finite element codes. A dedicated sequence of modules performs all required steps, from mesh separation and crack insertion to local submodeling, stress and displacement mapping, and iterative crack-front update, without manual interaction. The methodology was verified using a mini-compact tension (Mini-CT) specimen as a benchmark case. The numerical results demonstrate the accurate reproduction of stress intensity factors and energy release rates while achieving high computational efficiency through localized refinement. The developed approach provides a robust basis for crack growth simulations of geometrically complex or residual stress-affected structures. Its high degree of automation and flexibility makes it particularly suited for analyzing cracks in clinched and riveted joints, supporting the predictive design and durability assessment of joined lightweight structures. Full article

It has been demonstrated that infants and young children exhibit immune tolerance as a consequence of immature immune systems, which are characterized by a natural Th2 bias. RSV infection has been reported to result in acute lower respiratory infection (ALRI), while formalin-inactivated vaccination has been observed to exacerbate Th2 responses, consequently leading to enhanced respiratory disease (ERD). Transcriptomic data from three independent cohorts of RSV-infected infants were analyzed (GSE246622 served as the discovery and train set; GSE105450 and GSE188427 were used as validation sets). Immune infiltration analysis revealed immunological characteristics, which were then used to perform unsupervised clustering using feature-related genes. WGCNA was used to identify co-expressed gene modules, while Mfuzz and TCseq were employed to analyze temporal expression patterns. Machine learning models were developed using a refined panel of candidate genes. Severe symptoms of RSV infection exhibited a strong correlation with age, with younger infants demonstrating more intense inflammatory responses from neutrophils, macrophages, mast cells and dendritic cells. A predictive model was constructed using ten co-expressed genes: The following genes were identified: MCEMP1, FCGR1B, ANXA3, FAM20A, CYSTM1, GYG1, ARG1, SLPI, BMX and SMPDL3A. It was observed that infants of a younger demographic demonstrated a heightened degree of immunosuppression and pronounced innate immune activation in patients of severe symptoms with RSV infection. However, eosinophils exhibited minimal involvement in these processes. These gene models pertaining to the neutrophil, macrophage or mast cell was found to be a relatively effective predictor in patients of severe symptoms. Full article

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in a query image and their boundaries may overlap or be partially occluded. We present Render-Rank-Refine, a two-stage framework operating on coarse semantic meshes without requiring textured models or per-scene fine-tuning. First, panoramas rendered from the mesh enable global retrieval of coarse pose hypotheses. Then, perspective views from the top-k candidates are compared to the query via rotation-invariant circular descriptors, which re-ranks the matches before final translation and rotation refinement. Our method increases camera localization accuracy compared to the state-of-the-art SPVLoc baseline by reducing the translation error by 40.4% and the rotation error by 29.7% in ambiguous layouts, as evaluated on the Zillow Indoor Dataset. In terms of inference throughput, our method achieves 25.8–26.4 QPS, (Queries Per Second) which is significantly faster than other recent comparable methods, while maintaining accuracy comparable to or better than the SPVLoc baseline. These results demonstrate robust, near-real-time indoor localization that overcomes structural ambiguities and heavy geometric assumptions. Full article

Firstly, based on one-dimensional Terzaghi consolidation theory, we derived and established the analytical solution of excess pore water pressure and average consolidation degree of double-layered foundation, which can reflect the effect of fiber reinforcement. Meanwhile, the one-dimensional consolidation test of a double-layered foundation was carried out by means of a modified WG-type (product series code) consolidation instrument. The accuracy of the theoretical solution was verified by designing different consolidation parameters of the basalt fiber-reinforced solidified lightweight soil (BF-SLS) layer. Secondly, our findings suggest that the settlement rate of the double-layered foundation decreased with the increase in thickness, compression modulus and fiber mixing ratio of the BF-SLS layer. Nevertheless, the average pore pressure dissipation rate changed in the opposite trend. Both increased with increasing permeability coefficient of the BF-SLS layer. Within the thickness ratio range of 0 to 1/2 between the upper and lower layers, the thickness of the BF-SLS layer significantly influenced the consolidation process of the double-layer foundation. At equivalent T_v levels, the difference in consolidation degree exceeded 60%. Finally, a comparison of various simplified methods for calculating the average consolidation degree of double-layer foundations reveals that neither the weighted consolidation coefficient method nor the average index method yields results that are in good agreement with theoretical solutions. The difference between U_s (defined by sedimentation) and U_p (defined by pore pressure) cannot be distinguished. This research can further refine the consolidation theory of “upper hard and lower soft” double-layer foundations. Full article