Search Results (11,939)

Recent scientific evidence confirms that there is no safe threshold for bisphenol A intake, prompting strict regulatory actions and new prohibitions in the European Union. As a result, bisphenol A has increasingly been replaced by other analogues that are also toxic but less regulated and insufficiently studied, posing a new risk to human health due to cumulative exposure. Since food is the primary source of exposure to these compounds, this review aimed to evaluate the most appropriate existing chromatographic methods for their determination under newly introduced near-zero tolerance limits, as well as to assess current cumulative dietary exposure and associated health risks. A systematic literature search was conducted in major scientific databases and relevant regulatory sources covering the period from 2015 to 2025, following PRISMA guidelines. Of the 489 identified publications, 22 met the eligibility criteria for full-text analysis. The findings indicate a clear methodological shift towards simultaneous quantification of multiple bisphenol analogues, with LC-MS/MS emerging as the dominant and most robust analytical technique. Dietary exposure to bisphenol A is expected to decline due to stricter regulations; however, this may trigger a rise in the use of its structural analogues as alternatives. Exposure assessments indicate that combined dietary intake of bisphenol A and its analogues can result in a Hazard Index exceeding 1, primarily due to the substantially reduced Tolerable Daily Intake for bisphenol A. This highlights the need for continuous monitoring under stricter regulatory frameworks. Full article

In this study, a three-dimensional simulation of a walking-beam reheating furnace was developed to improve the steel slab reheating process and reduce surface oxidation kinetics using computational fluid dynamics (CFD). Combustion, heat transfer, fluid dynamics, and chemical reaction models were integrated into the numerical framework of this study. In addition, dynamic mesh remeshing was coupled through user-defined functions (UDFs), enabling the simultaneous simulation of slab movement and evolution of the involved transport phenomena. Turbulence was modeled with the realizable k-ε formulation, combustion with the Eddy Dissipation model, and radiation with the P-1 model coupled with WSGGM to include CO₂ and H₂O gas radiation. Scale formation was modeled using customized functions based on Arrhenius-type kinetics and Wagner’s oxidation model, evaluating its growth as a function of time, temperature, and furnace atmosphere. The predicted thermal evolution inside the furnace was validated using industrial data, yielding an average deviation of 5%. Furthermore, the proposed operating conditions led to an average slab temperature of 1289.77 °C at the exit of the homogenization zone, which was 16 °C higher than that under the current operation but still within the target range (1250 ± 50 °C). The reduction in combustion air decreased energy losses and improved product quality, lowering the molar oxygen content in the furnace atmosphere from 4.9 × 10² mol to 6.7 × 10¹ mol. Additionally, annual savings of 4,793,472 kg of natural gas and 13,884 tons of steel were estimated owing to reduced oxidation losses. The proposed air–fuel adjustment led to estimated annual energy savings (equivalent to 4,793,472 kg of natural gas) and a reduction in material loss due to oxidation from 4.5% to 3.75% (an absolute reduction of 0.75 percentage points; relative reduction ≈ 16.7%), which has a significant industrial impact on metal conservation and descaling cost reduction. Although there are CFD studies on plate overheating and scale growth separately, this work presents three main contributions: (1) the integration, within a single numerical framework, of combustion, radiation, species transport, oxidation kinetics, and actual plate movement using a dynamic mesh; (2) validation against continuous industrial records (16 thermocouples) and quantification of operational benefits such as fuel savings and reduced material loss; and (3) a comparative analysis between actual and optimized conditions, which standardize the air–methane ratio. Full article

Mobile Laser Scanning (MLS) integrated with Simultaneous Localization and Mapping (SLAM) has emerged as a key technology for digitizing GNSS-denied environments, such as underground mines. However, the automated interpretation of unstructured, high-density point clouds into semantic engineering models remains challenging due to extreme geometric anisotropy in point distributions and severe class imbalance inherent to narrow tunnel environments. To address these issues, this study proposes a highly automated scan-to-model framework for precise semantic segmentation and vectorized two-dimensional (2D) profile reconstruction. First, an enhanced hierarchical deep learning network tailored for point clouds is introduced. The architecture incorporates a context-aware sampling strategy with an expanded receptive field of up to 10 m to preserve axial continuity, coupled with a spatial–geometric dual-attention mechanism to refine boundary delineation. In addition, a composite Focal–Dice loss function is employed to alleviate the dominance of wall points during network training. Experimental validation on a field-collected dataset comprising 16 mine tunnels demonstrates that the proposed model achieves a mean Intersection over Union (mIoU) of 85.15% (±0.29%) and an Overall Accuracy (OA) of 95.13% (±0.13%). Building on this semantic foundation, a robust geometric modeling pipeline is established using curvature-guided filtering and density-adaptive B-spline fitting. The reconstructed profiles accurately recover the geometric mean surface of the tunnel wall, yielding an overall filtered Root Mean Square Error (RMSE) of 4.96 ± 0.48 cm. The proposed framework provides an efficient end-to-end solution for deformation analysis and digital twinning of underground mining infrastructure. Full article

To address the engineering problems of high cement content, high brittleness, and weak frost resistance of cement-improved loess in the seasonal frozen soil area of Northwest China, F1 ion curing agent (F1) and cement composite improved loess (FCIL) were used in this paper. Through unconfined compressive (UC) strength tests, consolidated undrained (CU) triaxial shear tests, and microscopic pore characteristics analysis, the mechanical properties, freeze–thaw cycle deterioration law, and microscopic pore structure of FCIL were studied. The effects of cement content (C_c), F1 dosage (C_F), number of freeze–thaw cycles (N_F-T), and confining pressure (σ₃) on the strength, deformation behavior, and pore characteristics of FCIL were analyzed. The synergistic improvement mechanism of FCIL, as well as the freeze–thaw damage mechanism, was elucidated. The results show that C_c is the primary factor controlling the strength of improved loess. The incorporation of F1 can further increase UCS and markedly enhance the failure strain (ε_f), thereby achieving simultaneous improvements in strength and ductility. An appropriate mix proportion was identified as C_F = 0.2 L/m³ and C_c = 6%. After 7 d curing, FCIL exhibited a UCS of 1.35 MPa, a cohesion (c) of 205 kPa, an internal friction angle (φ) of 36.2°, and ε_f 1.8 times that of loess improved with C_c = 6% cement alone. CU triaxial shear tests indicate that, under all tested conditions, the stress–strain responses of FCIL exhibit σ3-sensitive strain-softening behavior. As C_c and σ₃ increase, triaxial peak strength (q_max) and secant modulus (E₅₀) increase significantly. Compared with natural loess (NL), FCIL shows a markedly lower porosity (n), a substantial increase in the proportion of micropores, and reductions in medium and small pores. After multiple freeze–thaw cycles, the evolution of the pore structure is effectively restrained. This indicates that the combined use of F1 and cement promotes the formation of a dense layered stacking structure, significantly improves the microscopic pore-size distribution, and enhances the mechanical performance of loess under freeze–thaw environments. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Background/Objectives: Reaction time, hand–eye coordination, spatial orientation, and attention play a key role in handball, which is characterized by high intensity as well as high cognitive and motor demands. The level of these abilities may change during the season, potentially reflecting training adaptations and increasing physical fatigue. The aim of the study was to compare the level of psychomotor abilities in professional handball players before the start of the competition round and after the end of the league season. The study included 77 handball players playing in the Polish Handball Super League (average age: 25.6 ± 5.2 years). The players were divided according to position: pivot, center, and wing. Methods: Psychomotor abilities were assessed using the Test2Drive computer system, employing tests of simple and choice reaction time, eye–hand coordination, spatial orientation, perception and attention, and movement anticipation. Results: At the end of the season, a statistically significant reduction in reaction time was observed in the choice reaction (p = 0.001), eye–hand coordination (p = 0.002), and spatial orientation tests (p = 0.003). In terms of motor skills, an increase in time was observed in the SIRT test (p = 0.003), CHORT (p = 0.005) and HECOR (p = 0.011) tests, while the time in the PUT test was shortened for both neutral (p = 0.002) and critical (p = 0.025) stimuli. Positional analysis showed that after the season, the pivot player achieved higher effectiveness in the CHORT test than the wing player (p = 0.020). Additionally, statistically significant differences were observed for correct responses in the SPANT test (p = 0.032). In terms of correct answers in the PAMT test, the pivot player had the lowest effectiveness. Conclusions: Participation in the full season of competition coincided with significant changes in the psychomotor profile of handball players, with a simultaneous improvement in reaction speed and deterioration in movement time parameters. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and measurement technologies is essential. This paper presents a comparative study on the applications of four sensor types in bearing condition monitoring. These four sensor types are vibration accelerometer, encoder, acoustic emission (AE) sensor and motor current probe. Their effectiveness and practicability in bearing fault detection are evaluted. Data simultaneously measured from these four sensor types on a split roller bearing within an experimental rig are used for the analysis. Different factors such as machine operating speeds, bearing fault sizes and their location are considered during the experiments to understand the effectiveness and fault detectability of different sensors on a common bearing. Both the accelerometer and the AE sensor are observed to effectively detect all bearing faults from small to extended sizes and from low to high operating speeds. However, the other two sensors, the encoder and motor current probe, have been found to be sensitive only to relatively larger fault sizes and higher operating speeds. The study presents valuable insights into their advantages and limitations through a systematic comparison of roller bearing fault detection. The study provides a basis for sensor selection in bearing condition monitoring and fault detection to enhance the reliability of industrial maintenance activities. Full article

Background: The global rise in antimicrobial resistance (AMR) has created an urgent need for innovative antibacterial strategies and localized delivery systems. This study aimed to develop and characterize electrospun poly (vinyl alcohol) (PVA) nanofibers co-loaded with atorvastatin (ATR) and zinc oxide (ZnO) nanoparticles for use as a multifunctional topical platform for wound healing and infection control. Methods: ZnO nanoparticles were prepared via ball milling and characterized for size and zeta potential. Four PVA-based nanofiber formulations were fabricated using electrospinning: blank (F1), ZnO-loaded (F2), ATR-loaded (F3), and ATR/ZnO co-loaded (F4). The nanofibers were evaluated for morphology, thermal properties, crystallinity, and drug release. Antibacterial efficacy was tested against S. aureus, S. epidermidis, MRSA, and P. aeruginosa using broth microdilution and checkerboard assays. Biocompatibility and wound healing potential were assessed via MTT and fibroblast scratch assays on human foreskin fibroblasts (hFFs). Results: SEM imaging confirmed the production of uniform, bead-free nanofibers. ATR and ZnO nanoparticles were successfully incorporated in the nanofiber. The co-loaded formulation (F4) demonstrated a sustained release profile, releasing approximately 78.7% of ATR over 24 h. While all treatments showed limited activity against P. aeruginosa, the ATR/ZnO co-loaded nanofibers exhibited significantly enhanced antibacterial activity against Gram-positive strains, achieving the lowest MIC values (1.5–2.0 mg/mL). Synergy analysis confirmed an enhanced effect with ATR and ZnO against MRSA. Furthermore, F4 achieved the highest wound closure rate of 92.41% in 24 h while maintaining acceptable cytocompatibility. Conclusions: The integration of ATR and ZnO into PVA nanofibers provides an enhanced antibacterial effect consistent with the synergistic potential observed between free agents targeting Gram-positive wound pathogens. The platform’s ability to simultaneously inhibit bacterial growth and promote rapid fibroblast migration positions it as a promising localized therapeutic for managing infected wounds. Full article

In this article, we investigate the existence and approximate controllability of a class of Sobolev-type fractional stochastic differential equations of order $1<\delta <2$ with infinite delay. The analysis is carried out in an abstract Hilbert space framework, incorporating fractional dynamics together with stochastic perturbations. By employing techniques from fractional calculus, semigroup theory, and fixed point theory, particularly the Banach contraction principle along with compactness arguments, we establish the existence of mild solutions for the proposed system. Subsequently, sufficient conditions for approximate controllability are derived by combining operator-theoretic methods with stochastic analysis. The novelty of this work lies in extending controllability results to Sobolev-type fractional stochastic systems of order $1<\delta <2$, where both the higher-order fractional structure and stochastic effects are treated simultaneously within a unified framework. This generalizes and complements several existing results in the literature that mainly address deterministic systems or fractional differential equations of order $0<\delta \le 1$. Finally, an illustrative example is presented to demonstrate the applicability and effectiveness of the theoretical findings. Full article

Soil organic carbon (SOC) and total nitrogen (TN) are key indicators of soil fertility; however, the dynamics of carbon (C) and nitrogen (N) fractions during winter wheat growth under different tillage systems remain poorly understood. This study examined the effects of three tillage practices: no tillage (NT), subsoiling tillage (SS), and deep tillage (DT) on four soil organic carbon fractions (SOC, soil organic carbon; EOC, easily oxidized organic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon) and four nitrogen fractions (TN, total nitrogen; NO₃^–-N, nitrate nitrogen; NH₄⁺-N, ammonium nitrogen; DON, dissolved organic nitrogen) across five winter wheat growth stages (sowing, overwintering, jointing, filling and harvest) in the 0–50 cm soil profile. The results showed that SOC, its labile fractions, and TN all decreased with increasing soil depth, with tillage effects mainly confined to the 0–20 cm layer. SS achieved the highest SOC and TN contents in the topsoil, while NT and SS significantly enhanced the surface enrichment of C and N. In contrast, DT promoted more uniform nutrient distribution into the 30–50 cm subsoil. DON continuously accumulated throughout the growing season with faster accumulation rates under SS and NT; DOC peaked at the jointing stage, while EOC and NH₄⁺-N followed a consistent “decline–recovery–decline” seasonal pattern. SS yielded the highest total SOC stock (166.20 t ha⁻¹) in the 0–50 cm profile, particularly in the 0–30 cm layer. Correlation analysis showed that the coupling relationships among C and N indicators varied with soil depth, with the strongest positive correlation between SOC and EOC in the topsoil. Both SS and DT maintained higher soil water content (SWC) than NT in the 20–50 cm layers throughout the experimental period. In conclusion, SS emerges as the optimal balanced tillage strategy for dryland wheat fields on the Loess Plateau, simultaneously improving topsoil fertility, water retention, and C sequestration; meanwhile, DT is more effective for enhancing subsoil water and nutrient conditions. These findings provide a scientific basis for targeted tillage management to sustain soil fertility and productivity in rainfed dryland farming systems. Full article

To characterize the transcriptional and physiological alterations induced by manganese stress in Coilia nasus, juveniles (mean weight 5.0 ± 0.2 g) were subjected to either manganese exposure (5.50 ± 0.03 mg/L) or control (0 mg/L) for a 12 h period. Subsequently, gill tissues were excised for evaluation of antioxidant parameters and RNA-Seq analysis. A total of 753 DEGs were identified in the manganese exposure group compared to controls, comprising 287 up-regulated and 466 down-regulated genes. GO and KEGG enrichment analysis of DEGs showed that most of the DEGs were involved in immune and metabolic pathways, which disturbed the biological processes related to immunity and metabolism at the molecular level. The acute manganese stress initiated a multi-level antioxidant response to cope with oxidative stress in Coilia nasus. This finding was further supported by the significant increase in MDA content and significant decrease in GSH content and GSH-Px activity under manganese exposure, while SOD and CAT activities were significantly increased. Simultaneously, the acute manganese stress triggered profound metabolic reprogramming to cope with energy pressure in Coilia nasus, which showed that manganese exposure significantly down-regulated energy metabolism-related genes (pfkm, pgam2, eno3, pkm, aqp9, apoa1, tkt, sds); furthermore, the overall energy metabolism network was widely inhibited, while lipid metabolism-related genes (fabp3, cpt1a) were significantly up-regulated to compensatorily activate fatty acid transport and β-oxidation pathways. In addition, the acute manganese stress initiated a complex immune response pattern to cope with cell damage in Coilia nasus, which showed that manganese exposure significantly enhanced the expression of inflammatory signaling genes (mapk1, stat1, tgfb3); furthermore, certain inflammatory pathways were activated, while the expressions of immune regulatory genes (traf6, il-10) were significantly decreased. In summary, these results indicated that manganese exposure could impair immune function, disrupt metabolism, and induce oxidative stress in Coilia nasus. Full article

To address the mounting environmental burden caused by solid waste from the food supply chain—specifically agricultural residues and plastic packaging—this study systematically investigated the synergistic mechanisms and product regulation pathways in the co-pyrolysis of four representative food processing by-products—rice husk, pine wood, corn stover, and chestnut shell—with polypropylene, a common food packaging material. A comprehensive methodology integrating thermogravimetric analysis, kinetic modeling, and product characterization was employed. The results demonstrate that incorporating polypropylene into co-pyrolysis systems, such as those involving waste oil, significantly reduces the average activation energy, indicating a catalytic effect that enhances reaction kinetics. Notably, the co-catalytic interaction between corn stover and PP led to a substantial 54.90% reduction in oxygen content, underscoring PP’s role as an effective hydrogen donor that promotes deoxygenation and free radical reactions, thereby increasing hydrocarbon production. At an optimal pyrolysis temperature of 600 °C, product distribution was effectively regulated: the hydrocarbon yield in the CP (corn stover/PP) system increased from 39.8% to a maximum of 65.6%, reflecting a targeted conversion of oxygenated compounds into high-value hydrocarbons. Furthermore, greenhouse gas (GHG) emission calculation and techno-economic analyses indicate that a natural gas-assisted co-pyrolysis process (Scenario C) can generate a net daily profit of 1835 RMB while reducing annual CO₂ emissions by 6515 tons, demonstrating both economic feasibility and environmental benefits. This study provides a theoretical foundation for the circular economy in the food industry, offering a viable technical pathway for the simultaneous treatment of organic food waste and packaging plastics, thereby supporting the sustainable development of the agri-food sector. Full article

In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the extremely short engagement window necessitates strict convergence within the predefined finite time. While PPC offers a promising framework to ensure such convergence with guaranteed transient performance, it suffers from singularity when target uncertainties drive tracking errors beyond performance bounds. To address these challenges, this paper proposes an adaptive prescribed-performance guidance law with predefined-time convergence for UAVs. Built upon the analysis that jump-gliding targets exhibit predominantly longitudinal oscillatory maneuvers, we first establish a velocity model to characterize their motion uncertainties. Using the derived uncertainty bounds and estimated parameters, a predefined-time performance function (PPF) is then developed and robustly modified to eliminate the singularity risk. By integrating this modified PPC with an adaptive law, the proposed framework achieves robust predefined-time convergence of the line-of-sight angle while simultaneously compensating for unknown target maneuvers. Theoretical analysis verifies the framework’s stability, and simulation results demonstrate its effectiveness in intercepting highly maneuverable targets. Full article

Film scenography often suffers from a dual problem of invisibility in academic theory and hypervisibility as mere ‘spectacle’ in popular reception. This study addresses the lack of integrated theoretical frameworks that connect scenographic design to its emotional and narrative functions. Utilizing a reception-focused analytical approach, this research applies Peter Wuss’s model of film perception to Wes Anderson’s The Grand Budapest Hotel (2014). Analyzing the broad range of the film’s scenographic methods, this article investigates how high degrees of scenographic visibility operate as affective mechanisms rather than just stylistic signatures. The analysis identifies specific epistemic qualities of film space that facilitate emotional engagement and narrative movement. By examining scenographic elements across multiple scales, this study reveals how these design choices operate simultaneously across concept-guided, perception-guided, and stereotype-guided cognitive structures. Ultimately, the research demonstrates that scenographic visibility is intrinsically motivated by affective function. This challenges conventional film theory dichotomies and repositions scenography as fundamental to understanding cinema’s epistemic operations. Full article