Search Results (86)

Thousand-seed weight (TSW) is a critical determinant of yield in rapeseed (Brassica napus L.). Developing germplasm with high TSW is therefore a key strategy in high-yield rapeseed breeding. However, the genetic and molecular mechanisms underlying TSW in rapeseed remain poorly understood. In our earlier work, we identified a mutant, designated GRG177, which exhibits a remarkably high TSW exceeding 7 g. To unravel the mechanisms driving this elevated TSW, we conducted a comprehensive analysis of GRG177, integrating morphological, genetic, developmental, anatomical, and physiological approaches. Compared with the control germplasm GRD328 (TSW ≈ 3.5 g), GRG177 displayed a significant increase in seed weight and seed volume, larger silique surface area, and higher yield per plant. However, it also showed a notable reduction in both silique number per plant and seed number per silique. Genetic analysis of a segregating population revealed that the high-TSW trait in GRG177 is governed by two pairs of dominant epistatic major genes plus polygenes. Endogenous hormone analysis revealed significantly higher zeatin riboside (ZR) content in the early stage of seed development in GRG177, whereas indole-3-acetic acid (IAA) and abscisic acid (ABA) levels were significantly up-regulated in the late stage of seed development. Anatomical observation using paraffin sections further confirmed that enhanced cell division activity in the early stage and improved cell expansion capacity in the later stage underpin the formation of high TSW. Furthermore, BSA-seq was utilized to map four TSW-related Quantitative Trait Loci (QTLs) and screen 13 candidate genes involved in IAA, ZR, and ABA signaling pathways. In conclusion, these findings provide novel insights into the regulatory mechanisms governing high-TSW formation in rapeseed and present valuable genetic resources for high-yield breeding. Full article

Although metabolic pathways profoundly influence disease behavior, osteosarcoma (OS) still lacks a glutamine metabolism–based framework for patient stratification. By integrating single-cell RNA sequencing with bulk cohorts, we delineated a glutamine-associated transcriptional program and translated it into an externally validated, clinically oriented risk model. After rigorous quality control and doublet removal, 19 clusters were annotated into 10 cell types. Glutamine metabolism–related gene (GRG) scores, quantified by five orthogonal algorithms (AUCell, UCell, singscore, ssGSEA, and AddModuleScore), revealed pronounced intratumoral heterogeneity, particularly within osteoblastic cells. A composite GRG score correlated with 641 genes, defining 188 differentially expressed genes; intersecting positively correlated and up-regulated genes yielded 91 candidates. Through a 10-fold cross-validated benchmark of 10 machine-learning algorithms and 101 combinations, Step-Cox [forward] + Ridge emerged as the optimal pipeline, producing a five-gene prognostic model (GPX7, COL11A2, CPE, MSMO1, SGMS2) with moderate yet reproducible performance in independent cohorts. Functionally, stable MSMO1 knockdown in U2OS cells suppressed proliferation, migration, and invasion; increased apoptosis; altered GS, GLS, and α-ketoglutarate; and dampened Wnt/β-catenin signaling. Clinically, the model stratifies OS patients into molecular risk subgroups with distinct outcomes, supporting identification of high-risk individuals and informing personalized glutamine-targeted or combination therapies. Mechanistically, glutamine metabolism shapes the OS tumor microenvironment by modulating immune-evasion and angiogenic cues, underscoring its dual role in metabolic adaptation and immune–metabolic crosstalk. Collectively, this study establishes a single-cell–anchored, glutamine-coupled state in OS, introduces an externally validated prognostic tool with translational promise but modest discriminative power, and positions MSMO1 as a metabolic–signaling node warranting further mechanistic and in-vivo investigation. Full article

STRange and Odd Morphology Extragalactic Radio Sources (STROMERSs) is a new category of radio galaxies that shows extremely peculiar anatomy. A purely manual visual search is carried out for the identification of such interesting sources. We reported a total of 108 STROMERS sources from the LOFAR Two-meter Sky Survey second data release (LoTSS DR2) at 144 MHz. The host galaxies are found ∼94% of the sources. We studied the radio and optical properties of the sources. Redshifts were found in 76% of sources with known host galaxies. The redshifts of STROMERS range from 0.0015 to 1.6599 and peak at 0.15. Among the reported STROMERS sources, there are 17 giant radio galaxies (GRG) with a linear size of greater than 700 kpc. Among them, only five GRGs are new, which is a small fraction of the population of GRGs from LoTSS DR2 data. The source ILTJ164117.44 +380208.4 has the highest linear size, approximately 1.8 Mpc. To study the reasons behind these interesting morphologies, we studied the galaxy cluster environment of each candidate within a 1 Mpc search radius. We found that 53% of STROMERS candidates are associated with cluster environments with known redshifts. The source ILTJ150956.65+332642.9 is associated with a high mass galaxy cluster Abell 2034 with mass a 7.57 $\times {10}^{14}{M}_{\odot }$. We also propose that the merger scenario is one of the reasons for the formation of STROMERS in the paper. Full article

As a core wear-prone component of coal mining equipment, the wear resistance of cutting picks directly affects mining efficiency and operating costs. This study addresses the premature failure of traditional hard alloy cutting picks caused by impact fatigue and abrasive wear under complex geological conditions. By introducing WC powder, the research aims to enhance the quality of the laser cladding coating on cobalt-based reinforced cutting picks and to investigate the variation in optimal process parameters with an increasing WC ratio. Five sets of L9 orthogonal experiments were conducted using the Taguchi method. Combined with the analysis of the signal-to-noise ratio (SNR), the optimal parameters under each material ratio were obtained and experimentally verified. The errors were all within 10%, which proves the reliability and repeatability of the optimization results. Subsequently, the effects of laser power, powder feeding rate and scanning speed on coating quality were systematically evaluated. Scanning speed had the most significant effect on microhardness, while laser power predominantly influenced dilution rate. For low WC content, powder feeding rate had a greater impact on porosity; as WC content increased, laser power became the main factor affecting porosity. Grey Relational Analysis (GRA) was subsequently applied to integrate the three response targets into a single grey relational grade (GRG), optimizing the parameters for each WC ratio. And the law of mutual influence between different material ratios and their process parameters was found. Wear tests on the optimized cladding layer showed that, compared with the original and pure cobalt-based picks, wear resistance increased by 45% and 80%, respectively. These results indicate a clear correlation between WC content, process parameter optimization, and improved coating performance. Full article

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of prior distributions in the Adaptive Random Walk Metropolis method and initial parameter values in the Generalized Reduced Gradient (GRG) Solver method affect ST parameter and log-likelihood estimates. An empirical study was conducted using the FTSE 100 index to evaluate model performance. We provide a comprehensive step-by-step tutorial demonstrating how to perform estimation and sensitivity analysis using data tables in Microsoft Excel. Among seven ST distributions—namely, the asymmetric, epsilon, exponentiated half-logistic, Hansen, Jones–Faddy, Mittnik–Paolella, and Rosco–Jones–Pewsey distributions—Hansen’s ST distribution is found to be superior. This study also applied the GRG method to estimate new approaches, including Realized Real-Time GARCH, Realized ASHARV, and GARCH@CARR models. An empirical study showed that the GARCH@CARR model with the feedback effect provides the best goodness of fit. Out-of-sample forecasting evaluations further confirm the predictive dominance of models incorporating real-time information, particularly Realized Real-Time GARCH for volatility forecasting and Realized ASHARV for 1% VaR estimation. The findings offer actionable insights for portfolio managers and risk analysts, particularly in improving volatility forecasts and tail-risk assessments during market crises, thereby enhancing risk-adjusted returns and regulatory compliance. Although the GRG method is sensitive to initial values, its presence in the spreadsheet method can be a powerful and promising tool in working with probability density functions that have explicit forms and are unimodal, high-dimensional, and complex, without the need for programming experience. Full article

Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea. Full article

This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing both real-time and post-processing Precise Point Positioning (PPP) solutions using GPS-only, Galileo-only, and the combined GPS/Galileo datasets. To correct the satellite and clock errors, the instantaneous Centre National d’Études Spatiales (CNES), the final Le Groupe de Recherche de Géodésie Spatiale (GRG), GeoForschungsZentrum (GFZ), and Wuhan University (WUM) products were applied. The results demonstrate that sub-30 cm positioning accuracy is achieved in the horizontal direction using real-time and final products. Additionally, sub-50 cm positioning accuracy is attained in the vertical direction for the real-time and post-processed solutions. Furthermore, the real-time products achieved three-dimensional (3D) position accuracies of 40 cm, 29 cm, and 20 cm using GPS-only, Galileo-only, and the combined GPS/Galileo observations, respectively. The final products achieved 3D position accuracies of 24 cm, 26 cm, and 28 cm using GPS-only, Galileo-only, and the combined GPS/Galileo measurements, respectively. The attained positioning accuracy can be used in some land use and urban planning applications. Full article

Background: The Quantitative Analysis of Multi-components by Single-marker (QAMS) method has been developed as an alternative to the External Standards Method (ESM) for the quality control of medicinal herbs. Objectives: In this study, QAMS was developed to determine saponins in the raw materials of Panax vietnamensis using HPLC-PDA/ELSD. Methods: The method was developed and validated. The relative conversion factors F_x were calculated based on the linear regression for HPLC-PDA and the logarithm equation for HPLC-ELSD. The Standard Method Difference (SMD) was determined to indicate the difference in the results of QAMS and EMS. Results: Relative conversion factors (F_x) were determined for each detector to quantify five saponins (ginsenoside Rb₁, Rd, Rg₁, majnoside R2, and vina-ginsenoside R2) in VG root. The F_x values were calculated based on the ratio of the slopes of the regression equations of a single standard and an external standard. For HPLC-PDA, G-Rb1 was used as a single standard with the F_x values of 1.00 (G-Rb₁), 1.08 (G-Rd), 1.32 (G-Rg₁), and 0.04 (M-R2). For HPLC-ELSD, G-Rb₁ was used for determining the content of G-Rg₁ and G-Rb₁ with the F_x values of 1.00 (G-Rb₁) and 0.95 (G-Rg₁), while M-R2 was used for quantitating M-R2 and V-R2 with F_x of 1.00 (M-R2) and 1.05 (V-R2). An SMD value less than 5.00% confirms the close alignment of the QAMS method with ESM. Conclusions: The QAMS method proved to be a feasible and promising method for the quality control of P. vietnamensis. Full article

In recent days, aluminum-based hybrid composites have garnered more interest than monolithic alloys owing to their remarkable properties, encompassing a high strength-to-weight ratio, excellent corrosion resistance, and impressive wear durability. The present study attempts to optimize the multiple wear attribute characteristics of Al6061/SiC/Al₂O₃ hybrid composites using grey and entropy-based VIKOR techniques. The composites were produced by adding equal proportions of SiC/Al₂O₃ (0–12 wt.%) ceramics through the stir-casting process, using an ultrasonication setup. Dry sliding wear experiments were executed with tribometer variants, namely reinforcement content (wt.%), load (N), sliding velocity (v), and sliding distance (SD), following L₂₇ OA. The optimal combination of process variables for achieving high GRG values from grey analysis was found to be A₃-B₃-C₃-D₃. The S/N ratios and ANOVA results for GRG indicated that RF content (wt.%) is the predominant component determining multiple outcomes, followed by sliding distance, load, and sliding velocity. The multi-order regression model formulated for the VIKOR index (Q_i) displayed high significance and more accuracy, with a variance of 0.0216 and a coefficient of determination (R²), and adjusted R² values of 99.60% and 99.14%. Subsequent morphological studies indicated that plowing, abrasion, and adhesion mechanisms are the dominant modes of wear. Full article

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

This study investigated whether ginsenoside Rg₁ (G-Rg₁) alleviated acute gouty arthritis (AGA) in rats by modulating the TLR4/NLRP3 pathway and neutrophil extracellular trap (NET) formation. Rats were orally administered G-Rg₁ or colchicine (Col) for 7 days, and monosodium urate (MSU) was injected into the ankle joints on day 5 to induce AGA. Joint swelling, histopathology (HE staining), and serum markers (MPO, NE, MPO-DNA, IL-6, IL-1β; ELISA) were assessed at the baseline and 6–36 h post-modeling. Western blot and immunofluorescence analyzed the NET-related and TLR4/NLRP3 pathway proteins in synovial tissue. G-Rg₁ significantly reduced ankle swelling and synovial inflammation compared with the AGA group, lowered the serum IL-6, IL-1β, MPO, NE, and MPO-DNA levels, and suppressed NET-associated protein expression. Mechanistically, G-Rg₁ downregulated TLR4/NLRP3 pathway activation in synovial tissue. These findings suggest that G-Rg₁ mitigates AGA by inhibiting TLR4/NLRP3 signaling, thereby reducing inflammatory cytokine release and NET formation. Full article

Numerical process modeling of continuous casting is increasingly applied to optimize production practices for higher product quality guarantees, in which real-time temperature prediction is the key factor that makes it possible to observe and optimize the solidification behavior of the continuous casting process. For offline simulations, the completeness of boundary conditions and accuracy of calculation are core concerns; as for online control, the stability of the model calculation and the correctness are equally important. This study demonstrates a process tracking and online control model of continuous casting based on IoT technology and proposes a simplified calculation method for secondary cooling boundary conditions. The proposed method calculates a comprehensive heat transfer coefficient based on offline simulated parameters and computes the total heat flux of a secondary cooling zone. The experimental results show that the temperature field calculated by this method is stable, and the difference between the actual measured temperature is within an acceptable range of 15 °C. The proposed method no longer requires an online model to consider four types of heat transfer boundary conditions, improving the versatility of the online model. The smoother surface temperature curve is beneficial for dynamic control of secondary cooling water. It can be an alternative method for implementing a more conducive online continuous casting model. Full article

This study investigated the potential of low-grade gold deposits in modern mining, particularly in the context of declining high-grade resources. The primary method for processing these ores was gravity separation with the Knelson concentrator. A GRG test (gravity recoverable gold test) was conducted on two gold-bearing samples: a polymetallic Cu-Zn-Au ore from Zlaté Hory–Západ (Czech Republic) containing refractory gold and an ore with free gold from Kašperské Hory (Czech Republic). The study evaluated the effectiveness of the GRG test for gold recovery from these ores. The results showed that the Kašperské Hory sample predominantly contained relatively large gold grains, with recovery rates dropping significantly upon finer comminution. In the sample from the Zlaté Hory–Západ deposit, the greatest GRG release occurred in the first and last test stages, suggesting that larger sulfide grains with bound gold passed predominantly in the first stage, while fine gold with residual sulfides passed in the third. Both samples achieved high overall GRG recovery rates, with 64.2% for Kašperské Hory and more than 66% for Zlaté Hory–Západ, demonstrating the efficacy of centrifugal concentrators for both ores. Full article

Iron monosulfides and neoformed pyrite below the sulfate–methane transition zone (SMTZ) of rapidly accumulating turbiditic sediments from the Gaoping submarine canyon off southwestern Taiwan were examined by SEM-EDS-EBSD, HRTEM, and HAADF STEM to investigate their microstructural characteristics and processes of formation and transformation. Within a few meters below the SMTZ, mackinawite (Mkw) is largely replaced by interlayered greigite-pyrrhotite (Grg-Po) with {111}_Grg//{001}_Po and ⟨110⟩_Grg//⟨110⟩_Po, followed by pyrite neoformation in clusters of disseminated matrix grains consisting of coalescing pyrite microcrystals, arrays of polycrystalline interlayer pyrite grains between the cleavage planes of layer silicates, with each grain’s core having inclusions of interlayered Grg-Po locally containing relict Mkw, and amassed pyrite microcrystals on the surface of porous interlayered Grg-Po micronodules. In the deeper sediments, neoformed pyrite is absent and Mkw is largely preserved, with partial replacement by interlayered Grg-Po having an overall topotactic relationship of ⟨110⟩_Grg//⟨110 ⟩_Po//⟨100⟩_Mkw and {111}_Grg//(001)_Po//~{011}_Mkw and a sharp reaction front without transitional profiles. The mineral grain boundaries and structural discontinuities with Mkw resulting from extensive interlayering between Grg {111} cubic close-packed segments and Po {001} hexagonal close-packed layers could serve as conduits for fluid flow and mass transport to drive the replacement reaction. The conversion of Mkw to metastable interlayered Grg-Po is inferred to occur through interface-coupled dissolution–reprecipitation processes associated with partial oxidation while the partial replacement of interlayered Grg-Po ± minor relict Mkw by pyrite microcrystals with irregular grain boundaries and orientations probably occurred via a dissolution–precipitation mechanism. Mkw could be initially formed by sulfate reduction driven by anaerobic oxidation of methane in reactive iron-rich sediments in paleo-SMTZs and subsequently transformed into interlayered Grg-Po followed by pyrite neoformation in the sulfidization front below the SMTZ or recent SMTZs in the Gaoping submarine canyon sediments. Full article

Solar air heaters play a crucial role in distributing heated air at low to medium temperatures. The heart of these systems lies in the absorber plate, which directly absorbs solar heat energy and then efficiently transfers it to the flowing air. However, the challenge lies in achieving optimal thermal efficiency by modifying the absorber plate roughness. Traditional smooth absorber plates have a limited contact area for heat transfer, leading to suboptimal performance. Using passive techniques, such as corrugation on the absorber plate, may increase thermal efficiency by creating turbulence in the laminar sublayer. In this study, corrugated solar air heaters are considered, with the absorber plates roughened into square, semicircular, and triangular ribs. The flow characteristics of heat due to the roughness of the absorber plate is simulated using the computational fluid dynamic (CFD) technique. The ANSYS Fluent 2019R3 is used to investigate the turbulent air flow in the absorber plate. The simulation analyses are performed over a Reynolds number range of 4000–18,000 using three different pitches. As the main contribution, two different model equations, namely M1 and M2, for the correlation model of the solar heat transfer were proposed to predict the Nusselt number, where M2 is an original model and M1 is newly established with the fine-tuned coefficients. Also, for the first time in the literature, the recent swarm optimization algorithm, namely Honey Formation Optimization with Single Component (HFO-1), is used to optimize the solar air heater and a comparison study with a popular non-swarm optimization, namely the Generalized Reduced Gradient (GRG) optimization, is provided. The Nusselt number was obtained using HFO-1 with a percentage error (MAPE) of 2.15% and 0.86% for the models M1 and M2, respectively. Moreover, the average achievement of HFO-1 on the proposed correlation models is 50% better than that of the GRG optimization, with respect to the RMSE. Full article