Search Results (38,293)

The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study focuses on the northern Atlantic Ocean, employing B-spline surface fitting to derive SLA fields from satellite along-track data. The results show strong agreement with in situ measurements, yielding a mean absolute error (MAE) of 1.89 cm and a root mean square error (RMSE) of 3.02 cm. Comparative analysis against the Copernicus Marine Environment Monitoring Service (CMEMS) Level-4 gridded SSH data reveals nearly equivalent accuracy (MAE: 1.95 cm; RMSE: 3.06 cm). The relationship between the order of fitting and the spatial extent of the fitting domain is also examined. Furthermore, the influence of the coastline on the fitting results is investigated in detail. As the coastline area expanded, the MAE and RMSE for the entire region increased. But the maximum increase in MAE was only 1.20 cm, and the maximum increase in RMSE was only 2.49 cm. Notably, there was no upward trend in MAE and RMSE in the mesoscale vortex dense area, which highlights the advantage of B-spline’s local support. Geostrophic flow and vertical component of relative vorticity are computed from the satellite along-track SLA data, with results showing agreement with Level-4 gridded geostrophic flow and vertical component of relative vorticity data. Full article

Extracts, fractions and the pure compound epifriedelanol of the medicinal plant Synadenium glaucescens have antibacterial properties. Herbal products are generally considered less prone to resistance development than conventional antimicrobials, as they contain multiple compounds, which makes bacteria less likely to develop resistance. However, data supporting this notion are lacking. This study evaluated the development of resistance in Staphylococcus aureus subjected to extract, fractions and epifriedelanol of S. glaucescens. It also identified S. aureus fitness genes contributing to intrinsic resistance to extract of S. glaucescens. Fluctuation and gradient concentration assays were used to determine mutation rates and growth adaptation, respectively, which were lower following exposure to growth in crude extract than the pure compound epifriedelanol. By subjecting 1920 single gene mutants from the Nebraska Transposon Mutant Library to growth in the presence of extract of S. glaucescens, 12 genes were identified as important for natural resistance in S. aureus JE2; however, only mutation in the hemB gene decreased the minimum inhibitory concentration by greater than 4-fold (64-fold). In conclusion, purifying active antimicrobial compounds from S. glaucescens and using them as antibacterial substances as an alternative to crude extract increased the risk of resistance development. Further, the gene hemBappears to have a significant role in the natural resistance to the extracts obtained from S. glaucescens in this study. Full article

Backfilling materials are commonly employed materials in mines for filling mining waste, and the strength of the consolidated backfill formed by the binding material directly influences the stability of the surrounding rock and production safety in mines. The traditional approach to obtaining the strength of the backfill demands a considerable amount of manpower and time. The rapid and precise acquisition and optimization of backfill strength parameters hold utmost significance for mining safety. In this research, the authors carried out a backfill strength experiment with five experimental parameters, namely concentration, cement–sand ratio, waste rock–tailing ratio, curing time, and curing temperature, using an orthogonal design. They collected 174 sets of backfill strength parameters and employed six population optimization algorithms, including the Artificial Ecosystem-based Optimization (AEO) algorithm, Aquila Optimization (AO) algorithm, Germinal Center Optimization (GCO), Sand Cat Swarm Optimization (SCSO), Sparrow Search Algorithm (SSA), and Walrus Optimization Algorithm (WaOA), in combination with the CatBoost algorithm to conduct a prediction study of backfill strength. The study also utilized the Shapley Additive explanatory (SHAP) method to analyze the influence of different parameters on the prediction of backfill strength. The results demonstrate that when the population size was 60, the AEO-CatBoost algorithm model exhibited a favorable fitting effect (R² = 0.947, VAF = 93.614), and the prediction error was minimal (RMSE = 0.606, MAE = 0.465), enabling the accurate and rapid prediction of the strength parameters of the backfill under different ratios and curing conditions. Additionally, an increase in curing temperature and curing time enhanced the strength of the backfill, and the influence of the waste rock–tailing ratio on the strength of the backfill was negative at a curing temperature of 50 °C, which is attributed to the change in the pore structure at the microscopic level leading to macroscopic mechanical alterations. When the curing conditions are adequate and the parameter ratios are reasonable, the smaller the porosity rate in the backfill, the greater the backfill strength will be. This study offers a reliable and accurate method for the rapid acquisition of backfill strength and provides new technical support for the development of filling mining technology. Full article

Compression pants, as functional sportswear providing external pressure, are widely used to enhance athletic performance and accelerate recovery. However, systematic investigations into their effectiveness during anaerobic exercise and the impact of different pressure levels on performance and post-exercise recovery remain limited. This randomized crossover controlled trial recruited 20 healthy male university students to compare the effects of four garment conditions: non-compressive pants (NCP), moderate-pressure compression pants (MCP), high-pressure compression pants (HCP), and ultra-high-pressure compression pants (UHCP). Anaerobic performance was assessed through vertical jump, agility tests, and the Wingate anaerobic test, with indicators including time at peak power (TPP), peak power (PP), average power (AP), minimum power (MP), power drop (PD), and total energy produced (TEP). Post-exercise blood lactate concentrations and heart rate responses were also monitored. The results showed that both HCP and UHCP significantly improved vertical jump height (p < 0.01), while MCP outperformed all other conditions in agility performance (p < 0.05). In the Wingate test, MCP achieved a shorter TPP compared to NCP (p < 0.05), with significantly higher AP, lower PD, and greater TEP than all other groups (p < 0.05), whereas HCP showed an advantage only in PP over NCP (p < 0.05). Post-exercise, all compression pant groups recorded significantly higher peak blood lactate (Lamax) levels than NCP (p < 0.05), with MCP showing the fastest lactate clearance rate. Heart rate analysis revealed that HCP and UHCP induced higher maximum heart rates (HR_max) (p < 0.05), while MCP exhibited superior heart rate recovery at 3, 5, and 10 min post-exercise (p< 0.05). These findings suggest that compression pants with different pressure levels yield distinct effects on anaerobic performance and physiological recovery. Moderate-pressure compression pants demonstrated the most balanced and beneficial outcomes across multiple performance and recovery metrics, providing practical implications for the individualized design and application of compression garments in athletic training and rehabilitation. Full article

Blue mold of pome fruit, caused by Penicillium expansum, is controlled through postharvest applications of thiabendazole (TBZ), pyrimethanil (PYR), and fludioxonil (FDL). However, multi-fungicide-resistant isolates have emerged in the U.S. Pacific Northwest and their impact on decay control in long-term storage is unknown. This study evaluated the fitness of P. expansum isolates sensitive to all three postharvest fungicides (wild-types) and those resistant to TBZ (single-resistant), TBZ and PYR, or PYR and FDL (dual-resistant), and triple-resistant to the three fungicides. On nutrient-poor media, resistant isolates showed reduced conidial germination, whereas no significant differences were observed in germination, mycelial growth, or sporulation between phenotypes on nutrient-rich media at 1.5 and 20 °C. Regardless of their sensitivity phenotype, FDL-resistant isolates showed increased sensitivity to osmotic and oxidative stresses. Pathogenicity and virulence were not affected by the sensitivity phenotype on apples after six months of storage at 1.5 °C. Analysis of cumulative fitness changes indicated fitness loss under low-temperature in vitro and increased fitness under fungicide selection pressure on fruit in most resistant phenotypes. Gene expression analysis showed differential regulation of fitness-related genes, with most being up-regulated by TBZ. Overall, the results suggest that resistance in P. expansum may carry context-dependent fitness penalties, especially under high-stress conditions. Full article

Dental implant components are typically fabricated using subtractive manufacturing, often involving metal materials that can be costly, inefficient, and time-consuming. This study explores the use of additive manufacturing (AM) with zirconia for dental implant overdenture bars, focusing on mechanical performance, stress distribution, and fit. Solid and lattice-structured bars were designed in Fusion 360 and produced using LithaCon 210 3Y-TZP zirconia (Lithoz GmbH, Vienna, Austria) on a CeraFab 8500 printer. Post-processing included cleaning, debinding, and sintering. A 3D-printed denture was also fabricated to evaluate fit. Thermography and optical imaging were used to assess adaptation. Custom fixtures were developed for flexural testing, and fracture loads were recorded to calculate stress distribution using finite element analysis (ANSYS R2025). The FEA model assumed isotropic, homogeneous, linear-elastic material behavior. Bars were torqued to 15 Ncm on implant analogs. The average fracture loads were 1.2240 kN (solid, n = 12) and 1.1132 kN (lattice, n = 5), with corresponding stress values of 147 MPa and 143 MPa, respectively. No statistically significant difference was observed (p = 0.578; α = 0.05). The fracture occurred near high-stress regions at fixture support points. All bars demonstrated a clinically acceptable fit on the model; however, further validation and clinical evaluation are still needed. Additively manufactured zirconia bars, including lattice structures, show promise as alternatives to conventional superstructures, potentially offering reduced material use and faster production without compromising mechanical performance. Full article

This work aims to perform a detailed mineralogical, crystal-chemical, and geochemical characterization of bentonites from the Benavila outcrop, the largest known deposit of bentonites in continental Portugal. Bulk samples and different size fractions were characterized through X-Ray Diffraction (XRD). Structural formulae of the smectites were fitted from point analyses acquired by analytical electron microscopy (AEM) with transmission electron microscopy (TEM). Smectites are the major component with variable amounts of calcite and minor amounts of quartz, feldspar, illite, and chlorite. Occasionally, amphiboles and dolomite have also been identified. The high content of carbonates in different parts of the sampling area is related to the circulation of carbonate-rich fluids. The smectites present high-layer charge, are intermediate terms of the montmorillonite–beidellite series, and also show an intermediate cisvacant–transvacant configuration. Major and trace elements concentrations were determined by ICP-MS. The geochemical analysis of the samples indicates an enrichment in SiO₂ and Al₂O₃ and a depletion of the more clayey materials in REE, HFSE, and Y, among others. The calculation of the PIA and CIA alteration indices, along with other parameters observed, shows the possible alteration pathways of the Benavila deposit. Research to evaluate the ability of these bentonites to be used as engineering barrier systems (EBS) and sealing materials for radioactive waste repositories is ongoing. Full article

With the increasing depth of coal resource extraction, the creep characteristics of gangue backfill in deep backfill mining are crucial for the long-term deformation of rock strata. Existing research predominantly focuses on the instantaneous deformation response of either the backfill alone or the strata movement, lacking systematic studies that reflect the long-term time-dependent deformation characteristics of the strata-backfill system. This study addresses gangue–roof composite specimens with varying gangue particle sizes. Utilizing physical similarity ratio theory, graded loading confined compression creep experiments were designed and conducted to investigate the effects of gangue particle size and moisture content on the creep behavior of the gangue–roof composites. A fractional-order creep constitutive model for the gangue–roof composite was established, and its parameters were identified. The results indicate the following: (1) The creep of the gangue–roof composite exhibits two-stage characteristics (initial and steady-state). Instantaneous strain decreases with increasing particle size but increases with higher moisture content. Specimens reached their maximum instantaneous strain under the fourth-level loading, with values of 0.358 at a gangue particle size of 10 mm and 0.492 at a moisture content of 4.51%. (2) The fractional-order creep model demonstrated a goodness-of-fit exceeding 0.98. The elastic modulus and fractional-order coefficient showed nonlinear growth with increasing particle size, revealing the mechanism of viscoplastic attenuation in the gangue–roof composite. The findings provide theoretical support for predicting the time-dependent deformation of roofs in deep backfill mining. Full article

The objective of the research was to use sigmoidal mathematical models for the planning and control of rigid pavement works. A dataset was constructed using 140 technical files, which were then analyzed to extract the valued work schedules. These schedules contained the variables time and cost per month. Subsequently, two groups were created from the dataset: a training group comprising 80% of the data and a test group comprising the remaining 20%. Subsequently, the variables were normalized and adjusted with the proposed logistic, Von Bertalanffy, and Gompertz models using Python 3.11.13. Following the implementation of training and validation procedures, the logistic model was identified as the optimal fit, as indicated by the following metrics: R² = 0.9848, MSE = 0.0026, RMSE = 0.0506, and MAE = 0.0278. The implementation of the aforementioned model facilitates the establishment of an early warning system with a high degree of effectiveness. This system enables the evaluation of the discrepancy between the actual progress and the planned progress with an R² greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution. Full article

The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This article adopts a hybrid architecture of convolutional neural network and long short-term memory network (CNN-LSTM). In the 1D-CNN layer, the one-dimensional convolutional neural network can automatically learn and extract local transient features of time series data by sliding convolution operations on time series data through its convolution kernel, making these local transient features present a specific form in the local time window. In the double-layer LSTM layer, the neural network model captures the transient characteristics of switch current through the gating mechanism and state transfer. The hybrid architecture of the constructed model has significant advantages in accuracy, with metrics such as root mean square error (RMSE) and mean absolute error (MAE) significantly reduced, compared to traditional switch current models, solving the problem of insufficient accuracy in traditional models. The neural network model has good fitting performance at both room and high temperatures, with an average coefficient close to 1. The new neural network hybrid architecture has short running time and low computational resource consumption, meeting the needs of practical applications. Full article

Optimization of injection molding process parameters is essential for improving product quality and production efficiency. Traditional methods, which rely heavily on operator experience, often result in inconsistencies, high time consumption, high defect rates, and suboptimal energy consumption. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed, integrating dynamic inertia weight adjustment, adaptive acceleration coefficients, and position constraints to address the issue of premature convergence and enhance global search capabilities. A dual-model architecture was implemented: a constraint validation mechanism based on support vector machine (SVM) was enforced per iteration cycle to ensure stepwise quality compliance, while a fitness function derived by extreme gradient boosting (XGBoost) was formulated to minimize cycle time as the optimization objective. The results demonstrated that the average injection cycle time was reduced by 9.41% while ensuring that the product was qualified. The SVM and XGBoost models achieved high performance metrics (accuracy: 0.92; R²: 0.93; RMSE: 1.05), confirming their robustness in quality classification and cycle time prediction. This method provides a systematic and data-driven solution for multi-objective optimization in injection molding, significantly improving production efficiency and energy utilization. Full article

Background: Medical clearance for competitive sports is vital to safeguarding athletes’ health, particularly in high-intensity disciplines like football. In Italy, fitness assessments follow stringent protocols set by the Commissione di Vigilanza per il controllo dell’Idoneità Sportiva (COCIS), with a strong focus on cardiovascular screening. The primary goal is to prevent sudden cardiac death (SCD), a rare but catastrophic event in athletes. Methods: This paper provides an in-depth narrative review of the 2023 COCIS guidelines, examining the cardiological screening process, required diagnostic tests, management of identified cardiovascular conditions, and the protocols’ role in reducing SCD risk. Results: Comparisons with international standards underscore the effectiveness of the Italian approach. Conclusions: The COCIS 2023 guidelines provide clear, evidence-based protocols for cardiovascular risk assessment, significantly enhancing athlete safety and reducing the incidence of SCD in high-intensity sports. Full article

Background. Studies of comorbid (syntropic) and inversely comorbid (rarely occurring together, i.e., dystropic) diseases have focused on the search for molecular causes of this phenomenon. Materials. We investigated DNA methylation levels in regulatory regions of 23 apoptosis-associated genes as candidate loci associated with the “cancer–neurodegeneration” dystropy in patients with Huntington’s disease (HD) and patients with non–small cell lung cancer (LC). Results. Statistically significant differences in methylation levels between the HD and LC groups were found for 41 CpG sites in 16 genes. The results show that five genes (SETDB1, TWIST1, HDAC1, SP1, and GRIA2) are probably involved in the phenomenon of inverse comorbidity of these diseases. For these genes, the methylation levels of the studied CpG sites were altered in opposite directions in the two groups of patients, compared to the control group. Conclusions. For the SP1 gene, the above hypothesis is supported by our analysis of open-access data on gene expression in patients with the aforementioned diagnoses and fits a probable mechanism of the “HD–LC” dystropy. Full article

The sustainable utilization of saline water resources represents an effective strategy for alleviating water scarcity in arid regions. However, the mechanisms by which prolonged saline water irrigation influences soil salinization and dryland crop growth are not yet fully understood. This study examined the effects of six irrigation water salinity levels (CK: 0.87 g·L⁻¹, S1: 2 g·L⁻¹, S2: 4 g·L⁻¹, S3: 6 g·L⁻¹, S4: 8 g·L⁻¹, S5: 10 g·L⁻¹) on soil salinization dynamics and jujube growth during a three-year field experiment (2020–2022). The results showed that soil salinity within the 0–1 m profile significantly increased with rising irrigation water salinity and prolonged irrigation duration, with the 0–0.4 m layer accounting for 50.27–74.95% of the total salt accumulation. A distinct unimodal salt distribution was observed in the 0.3–0.6 m soil zone, with the salinity peak shifting downward from 0.4 to 0.5 m over time. Meanwhile, soil pH and sodium adsorption ratio (SAR) increased steadily over the study period. The dominant hydrochemical type shifted from SO₄²⁻-Ca²⁺·Mg²⁺ to Cl⁻-Na⁺·Mg²⁺. Crop performance exhibited a nonlinear response to irrigation salinity levels. Low salinity (2 g·L⁻¹) significantly enhanced plant height, stem diameter, leaf area index (LAI), vitamin C content, and yield, with improvements of up to 12.11%, 3.96%, 16.67%, 16.24%, and 16.52% in the early years. However, prolonged exposure to saline irrigation led to significant declines in both plant growth and water productivity (WP) by 2022. Under high-salinity conditions (S5), yield decreased by 16.75%, while WP declined by more than 30%. To comprehensively evaluate the trade-off between economic effects and soil environment, the entropy weight TOPSIS method was employed to identify S1 as the optimal irrigation treatment for the 2020–2021 period and control (CK) as the optimal treatment for 2022. Through fitting analysis, the optimal irrigation water salinity levels over 3 years were determined to be 2.75 g·L⁻¹, 2.49 g·L⁻¹, and 0.87 g·L⁻¹, respectively. These findings suggest that short-term irrigation of jujube trees with saline water at concentrations ≤ 3 g·L⁻¹ is agronomically feasible. Full article

Climate warming is anticipated to significantly alter the distribution and composition of plant species in the Arctic, thereby cascading through food webs and affecting both associated fauna and entire ecosystems. To elucidate the trend in plant distribution in response to climate change, we employed the MaxEnt model to project the future ranges of 25 representative Arctic and Circumpolar plant species (including grasses and shrubs). Species distribution data, in conjunction with bioclimatic variables derived from climate projections of three selected General Circulation Models (GCMs), ESM2, IPSl, and MPIE, were utilized to fit the MaxEnt models. Subsequently, we predicted the potential distributions of these species under three Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, and SSP585—across a timeline spanning 2010, 2050, 2100, 2200, 2250, and 2300 AD. Range shift indices were applied to quantify changes in plant distribution and range sizes. Our results show that the ranges of nearly all species are projected to diminish progressively over time, with a more pronounced rate of reduction under higher emission scenarios. The species are generally expected to shift northward, with the distances of these shifts positively correlated with both the time intervals from the current state and the intensity of thermal forcing associated with the SSPs. Arctic species (A_Spps) are anticipated to face higher extinction risks compared to Boreal–Arctic species (B_Spps). Additional indices, such as range gain, loss, and overlap, consistently corroborate these patterns. Notably, the peak range shift speeds differ markedly between SSP245 and SSP585, with the latter extending beyond 2100 AD. In conclusion, under all SSPs, A_Spps are generally expected to experience more significant range shifts than B_Spps. In the SSP585 scenario all species are projected to face substantial range reductions, with Arctic species being more severely affected and consequently facing the highest extinction risks. These findings provide valuable insights for developing conservation recommendations for polar plant species and have significant ecological and socioeconomic implications. Full article