Search Results (140,511)

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, utilizing one-month operational data across 202 Transportation Analysis Zones (TAZs). Combining spatial analysis with explainable machine learning (XGBoost–SHAP), we systematically examine how operational factors and built environment characteristics interact to influence economic outcomes, achieving superior predictive performance (R² = 0.847) compared to baseline linear regression models (R² = 0.652). The SHAP-based interpretation reveals three key findings: (1) bike-sharing performance exhibits pronounced spatial heterogeneity that correlates strongly with urban functional patterns), with commercial districts and transit-adjacent areas demonstrating consistently higher economic returns. (2) Gradual positive relationships emerge across multiple factors—including bike supply density (maximum SHAP contribution +1.0), commercial POI distribution, and transit accessibility—with performance showing consistent but moderate improvements rather than dramatic threshold effects. (3) Significant interaction effects are quantified between key factors, with bike supply density and commercial POI density exhibiting strong synergistic relationships (interaction values 1.5–2.0), particularly in areas combining high commercial activity with good transit connectivity. The findings challenge simplistic linear assumptions in bike-sharing management while providing quantitative evidence for spatially differentiated strategies that account for moderate threshold behaviors and factor synergies. Cross-validation results (5-fold, R² = 0.89 ± 0.018) confirm model robustness, while comprehensive performance metrics demonstrate substantial improvements over traditional approaches (35.1% RMSE reduction, 36.6% MAE improvement). The proposed framework offers urban planners a data-driven tool for evidence-based decision-making in sustainable mobility systems, with broader methodological applicability for similar urban contexts. Full article

Background: L-carnitine supplementation is thought to enhance exercise performance, particularly in moderate and high-intensity activities, but evidence supporting this is mixed. This study aimed to assess whether acute L-carnitine tartrate supplementation could improve CrossFit^® performance, specifically during the “Cindy” workout, a high-intensity exercise protocol. Methods: In a randomized, double-blind, placebo-controlled crossover design, 20 trained male recreational CrossFit^® athletes completed the “Cindy” workout within a 20 min period after ingesting either 3 g of L-carnitine tartrate or a placebo 90 min before exercise. Performance was measured by total repetitions completed. Secondary outcomes included ratings of perceived exertion (RPE), gastrointestinal issues, and blood pressure (BP) measurements. Results: The results showed that L-carnitine supplementation did not significantly affect the number of repetitions performed (202.4 ± 69.9 vs. 204.5 ± 78.8, p = 0.810) compared to the placebo. There were also no significant differences in RPE (6.3 ± 1.5 vs. 6.9 ± 1.4, p = 0.180) or BP changes between groups. However, 10% of participants reported difficulty sleeping after L-carnitine supplementation. Conclusions: The findings suggest that 3 g of L-carnitine tartrate does not enhance CrossFit^® performance in recreational athletes. Further research is needed to clarify its potential benefits, especially with larger samples and consideration of factors like sex and carbohydrate co-ingestion. Full article

Background: The function and mechanism of N6-methyladenosine (m6A) methylation in pressure-overload cardiac fibrosis remains limited and unclear. This study aims to analyze and predict m6A modifications present in mouse hearts because of transverse aortic constriction (TAC). Materials and Methods: Twelve male C57BL/6 mice were randomly assigned to two groups, TAC group and sham group. The RNA Dot Blot assay was employed to evaluate the overall m6A methylation levels in both TAC and sham mice. The expression level of m6A-related enzymes were investigated through RT-PCR and Western blotting. MeRIP-seq and RNA-seq analyses were conducted to identify differentially modified m6A genes and mRNA expression genes. The protein–protein interaction (PPI) network was carried out to choose potential hub genes. Additionally, the transcription factor (TF)–microRNA (miRNA) coregulatory network and the drug–hub gene interaction network were built based on these hub genes. Furthermore, molecular docking simulations were also performed to analyze the interactions between drugs and hub genes. Results: Compared with the sham group, the TAC group demonstrated elevated levels of global m6A methylation. METTL3 and METTL14 were significantly upregulated, whereas FTO and ALKBH5 were significantly downregulated following TAC. MeRIP-seq analysis identified 17,806 m6A peaks associated with 9184 genes and 16,392 m6A peaks associated with 8550 genes in the TAC and sham groups, respectively. In conjunction with RNA-seq data, 66 genes were identified as exhibiting concurrent differences in both m6A methylation levels and mRNA expression. Six hub genes, Cd33, Irf4, Nr4a2, Hspa1b, Nr4a1, and Adcy1, were identified through the construction of a PPI network. The TF-miRNA coregulatory network contains six hub genes, 31 miRNAs, and 24 TFs. The drug–hub genes interaction network included five hub genes and 36 candidate drugs. Conclusions: The m6A modification is prevalent in TAC-induced cardiac fibrosis and significantly contributes to the fibrotic process by regulating critical genes. In the future, it may emerge as one of the potential cardiac fibrosis therapeutic targets. Full article

Objectives: The current study focused on the kidney protection and antioxidant properties along with the potential anti-ferroptotic activity of Geranium macrorrhizum L. (G. macrorrhizum) oil to ameliorate the acute renal oxidative tissue damage and toxicity of the aminoglycoside antibiotic gentamicin (GM) in an experimental murine model. Methods: The research was carried out with mature Balb/c mice distributed into four groups (n = 6). Application of GM (200 mg kg⁻¹ intraperitoneal injection for 10 days) was performed to induce kidney injury. Only saline was administered to the controls. The remaining groups were administered G. macrorrhizum oil (50 mg kg⁻¹ per dose) either used alone or in combination with GM. To assess the renal antioxidant status, the activities of specific antioxidant enzymes, indicators of lipid and DNA peroxidation and renal functional damage were examined using standard commercial kits, ELISA and EPR spectroscopy. Results: G. macrorrhizum oil analysis revealed 20 organic components belonging to mono- and sesquiterpenoids and long-chain hydrocarbons. The antioxidant and anti-inflammatory effects of G. macrorrhizum oil were demonstrated by reduced malondialdehyde, ROS, 8-hydroxy-2′-deoxyguanosine and cytokine levels (especially interleukin-1β) compared with GM. Furthermore, increased activation of superoxide dismutase, catalase and glutathione (GSH) were observed in the kidney homogenates of the animals which received GM in combination with G. macrorrhizum oil compared with the GM group. Additional changes in the GSH/glutathione peroxidase-4 axis were detected, suggesting the possible anti-ferroptotic potential of the oil. Nephroprotection was also demonstrated by elevated PGC-1α expression (peroxisome proliferator-activated receptor γ coactivator 1-alpha) and reduced KIM-1 levels (kidney injury molecule-1) following application of the oil. Conclusions: The preserved kidney antioxidant and functional properties in the groups treated with oil suggest that Geranium macrorrhizum L. could be utilized clinically to mitigate the toxic effects of GM application. Full article

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions. Full article

Background: The relationship between detectable circulating tumor DNA levels and clinical outcome following definitive therapy in patients with human papillomavirus (HPV)-mediated oropharyngeal squamous cell carcinoma has not been well established. Methods: In this retrospective analysis of patients with HPV-positive oropharyngeal squamous cell carcinoma seen from 2016 to 2024 at a single institution, 88 patients met inclusion criteria with baseline-positive tumor tissue-modified viral HPV DNA (TTMV-HPV DNA) testing and post-treatment testing performed. Results: Of the 88 patients included in the survival analysis, 77 had undetectable tumor tissue-modified viral human papillomavirus DNA after treatment, while 11 had positive (detectable) tumor tissue-modified viral human papillomavirus DNA. TTMV-HPV DNA positivity after treatment was associated with worse 1-year and 2-year overall survival outcomes, at 63.5% (37.7–100, p = 0.022) and 50.8% (25.7–100, p = 0.017) compared to 100% and 96.4% (91.6–100, p = 0.017) in patients with undetectable TTMV-HPV DNA. Inability to clear TTMV-HPV DNA after treatment was associated with worse progression-free survival, at 45.0% (95% CI 21.8–92.7, p = 0.009) at 1 year and 11.3% (95% CI 1.8–71.2, p = <0.001) at 2 years compared to 93% (95% CI 87.3–99.1) and 84.7% (95% CI, 76.3–94.0) in patients with cleared TTMV-HPV DNA after treatment. Conclusion: Tumor tissue-modified viral human papillomavirus DNA positivity after definitive treatment was associated with worse survival and disease recurrence outcomes compared to that in patients with undetectable post-treatment TTMV-HPV DNA. Prospective studies are warranted to further establish the clinical utility of TTMV-HPV DNA testing and its use in surveillance, treatment intensification, or de-intensification. Full article

Heave compensation is crucial for the improvement of marine engineering. However, there remain two main drawbacks in existing methods. One drawback is that the output accuracy of heave information is insufficient. The other drawback is that the consideration of lever arm effect errors is inadequate. To address these challenges, a new heave compensation algorithm is proposed in this paper. Firstly, a vessel’s center of gravity acceleration model incorporating lever arm effect dynamics is established. Secondly, a modified high-pass filter is presented using complementary techniques. A comparative error analysis demonstrates its superiority over traditional digital filters in real-time performance. Thirdly, the algorithm independently updates solutions of the heave of the vessel’s center of gravity and the heave caused by the lever arm vector and the vessel’s attitude to avoid oscillating temporarily. Simulation results verified the better heave measurement accuracy of the proposed algorithm. This algorithm ‌is pivotal to marine engineering. Full article

An in vitro study evaluated the fracture resistance of four CAD/CAM restorative materials: lithium disilicate ceramic (IPS e.max CAD, EM), hybrid ceramic (Vita Enamic, VE), a polymer-based composite (Cerasmart, CS), and a novel 3D-printed resin (Ceramic Crown, CC) fabricated using digital press stereolithography (DPS) technology. Standardized full-coverage crowns were designed and manufactured for each material. All specimens underwent thermocycling and fracture testing using a universal testing machine. EM exhibited the highest fracture resistance (mean: 440.49 N), while VE showed the lowest (173.82 N). CS (265.49 N) and CC (306.76 N) presented intermediate values without statistically significant differences between them. Stereomicroscopic analysis revealed differences in fracture patterns, with IPS e.max CAD showing smooth, brittle fractures, while hybrid and polymer-based materials exhibited tortuous fracture surfaces. These results suggest that DPS technology achieves mechanical performance for Ceramic Crown comparable to that of milled polymer-based composites, while offering production advantages in terms of time efficiency. As one of the first studies to evaluate Ceramic Crown and DPS technology, these findings provide initial insights into their mechanical behavior. However, further studies are required to validate their clinical performance before widespread use can be recommended. Full article

Photopolymer PQ/PMMA, as a pivotal material in the field of holographic storage, demonstrates significant application potential owing to its advantages, such as straightforward preparation processes, cost-effectiveness, and tunable thickness. However, its practical application is still constrained by the need for further enhancement in key performance indicators, including diffraction efficiency, photosensitivity, and anti-aging properties. In this study, N-vinylpyrrolidone (NVP) is employed as a comonomer. By precisely controlling the doping ratio, we systematically investigate the influence mechanism of different NVP doping concentrations on the holographic performance of NVP-PQ/PMMA materials. Research indicates that the introduction of NVP effectively increases the vinyl concentration in the PQ/PMMA matrix, thereby directly generating photoproducts with PQ during the photoreaction process and further enhancing the photopolymerization process. Consequently, the holographic performance of the novel NVP-PQ/PMMA material is improved in a multi-faceted manner compared to ordinary PQ/PMMA. Specifically, the diffraction efficiency is enhanced by 1.93 times, the photosensitivity is increased by 1.64 times, the material uniformity is improved by 38%, and the light-induced shrinkage rate is reduced by 39%. Additionally, NVP-PQ/PMMA materials exhibit excellent stability and aging resistance in high-temperature accelerated aging experiments. Doping with a monomer of specific structure enhances the optical properties, providing broad adaptability for further research on PQ/PMMA photopolymer materials. Full article

In software development, software requirement engineering (SRE) is an essential stage that guarantees requirements are clear and unambiguous. However, incomplete inconsistency, and ambiguity in requirement documents often occur, which can cause project delay, cost escalation, or total failure. In response to these challenges, this paper introduces a machine learning method to automatically identify the risk levels of software requirements according to ensemble classification methods. The labeled textual requirement dataset was preprocessed utilizing conventional preprocessing techniques, label encoding, and oversampling with the synthetic minority oversampling technique (SMOTE) to handle class imbalance. Various ensemble and baseline models such as extra trees, random forest, bagging with decision trees, XGBoost, LightGBM, gradient boosting, decision trees, support vector machine, and multi-layer perceptron were trained and compared. Five-fold cross-validation was used to provide stable performance evaluation on accuracy, area under the ROC curve (AUC), F1-score, precision, recall, root mean square error (RMSE), and error rate. The bagging (DT) classifier achieved the best overall performance, with an accuracy of 99.55%, AUC of 0.9971 and an F1-score of 97.23%, while maintaining a low RMSE of 0.03 and error rate of 0.45%. These results demonstrate the effectiveness of ensemble-based classifiers, especially bagging (DT) classifiers, in accurately predicting high-risk software requirements. The proposed method enables early detection and mitigation of requirement risks, aiding project managers and software engineers in improving resource planning, reducing rework, and enhancing overall software quality. Full article

Background: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy of immature T cells, driven by dysregulated transcriptional networks and oncogenic signaling pathways. Here, we present the first comprehensive analysis of the expression and regulation of TSPAN32, a tetraspanin implicated in lymphocyte homeostasis, in T-ALL. Methods: Transcriptomic data from the Leukemia MILE study (GSE13159) were analyzed to assess TSPAN32 expression across leukemic subtypes. Gene Set Enrichment Analysis (GSEA) was performed to explore biological pathways associated with TSPAN32-correlated genes. For mechanistic validation, HPB-ALL cells were used as a model, with NOTCH signaling inhibited by γ-secretase inhibitor (GSI) treatment and TAL1–LMO1 overexpression induced through doxycycline-inducible lentiviral vectors. Gene expression changes were quantified by RT-qPCR. Results: TSPAN32 was frequently downregulated in T-ALL compared to healthy bone marrow, although expression was retained in a subset of cases. GSEA revealed that TSPAN32-correlated genes were inversely associated with cell cycle–related programs, consistent with its established role as a negative regulator of T cell proliferation. Mechanistically, TAL1–LMO1 overexpression strongly induced TSPAN32, while GSI-mediated NOTCH inhibition partially reactivated its expression. Interestingly, GSI treatment also increased TAL1 levels despite downregulating LMO1. Conversely, TAL1–LMO1 overexpression suppressed NOTCH1 and NOTCH3, highlighting a reciprocal regulatory interplay between NOTCH and TAL1/LMO1 oncogenic circuits that shapes TSPAN32 expression dynamics in T-ALL. Conclusions: This study identifies TSPAN32 as a novel transcriptional target under the influence of key leukemogenic pathways and suggests its potential role as a modulator of leukemic T cell proliferation, with implications for therapeutic strategies targeting TAL1 and NOTCH signaling. Full article

Aiming at the problem of lacking accurate and reliable contact and bonding parameters in the discrete element simulation of whole cotton stalk harvesting equipment, this study proposed a reverse modeling method for cotton roots combining the Discrete Element Method (DEM) with 3D laser scanning. This method systematically constructed a general discrete element model and completed its parameter calibration. Firstly, cotton root samples were collected and measured to obtain key morphological parameters, providing a basis for selecting representative roots and performing 3D reverse reconstruction. Subsequently, mechanical parameters and contact parameters of the cotton roots were measured and calibrated through mechanical tests and stacking angle tests. Furthermore, based on the Hertz–Mindlin with Bonding contact model, a structured root sample model was established using a layered particle combination strategy. The bonding parameters were then optimized and calibrated through shear and tensile mechanical simulation experiments. Finally, a discrete element model of the root–soil complex was established based on the optimal parameter set. The reliability of the model was validated by comparing the simulation results with physical field tests of root extraction force. The results indicated that in the contact parameter validation test, the relative error between the simulated stacking angle and the measured value was only 0.43%, demonstrating the high accuracy of the model in simulating contact characteristics. In the bonding parameter calibration validation tests, the relative errors between the simulation results and measured values for shear and tensile mechanics were 1.22% and 1.40%, respectively, indicating that the model parameters could accurately simulate shear strength and tensile strength. Finally, in the root extraction force validation test, the relative error between the simulated extraction force and the field-measured value was 3.76%, further confirming the model’s applicability for analyzing the complex interaction mechanisms between roots and soil. The findings of this study can provide key models and parameter support for the digital design, operation process simulation, and performance optimization of whole cotton stalk harvesting equipment. Full article

Fuel layered-distribution sintering (FLDS) is a technology that can effectively reduce fuel consumption and achieve a more uniform temperature distribution within the sintering bed compared to traditional iron ore sintering. In this study, the melting quality index, combined with the maximum temperature and the duration of melting temperature, are used as performance indicators to investigate the effects of coke size and oxygen content on sintering characteristics under layered fuel distribution conditions. The results indicate that increasing the oxygen content can enhance the velocity of the flame front in the sinter pot, thereby accelerating the sintering process. However, excessive oxygen content may lead to fluctuations in the quality of the sinter. Small coke sizes provide higher melting quality in the upper region of the sinter pot, while large coke sizes perform better in the lower region. For a 600 mm sintering bed layer, an oxygen enrichment time of 6 min with oxygen concentration of 27% and coke particle diameter of 2.0 mm can balance sintered ore quality, sintering time, and flame front speed, ensuring the yield of sintered ore. These findings provide an effective pathway for energy saving and emission reduction in iron ore sintering plants. Full article

Flapping foils, inspired by the wing motions of birds and the swimming mechanisms of aquatic animals, offer a promising alternative to traditional turbines for extracting renewable energy from ambient flows found in nature. This study employs an immersed boundary-lattice Boltzmann method (IB-LBM) to numerically investigate the energy extraction performance of multiple flapping foils at a Reynolds number of 1100. Two staggered foils are systematically studied to identify the optimum spatial arrangements needed to achieve high energy-harvesting performance. The results show that the wake of the fore-foil mainly contributes to the negative performance of the hind-foil due to the loss of streamwise flow velocity, and the interaction between the two foils can enhance the energy-harvesting performance of the system, but cannot fully alleviate the effects of flow velocity loss. Therefore, the staggered arrangements, which help the hind-foil shed the wake, are essential to improve the energy-harvesting performance of the hind-foil. Comparable performance for the hind-foil is achieved at a horizontal gap of $2.5c$ and vertical gap of $2.5c$ with c being the chord length of the foil. The scaled-up systems, including three-, five-, and seven-foil configurations, are examined with gaps of $2.5c$ (horizontal) and $2.5c$ (vertical), and the results show that such `V’-shaped arrangements of these foils can achieve high energy-harvesting performance, with an enhancement up to $10.7\%$ when seven foils are used, by utilizing the high mean streamwise velocity at the boundary of the leader’s wake, confirming the versatility of the optimum staggered arrangements for flapping-foil arrays. Full article

The development of energy-efficient propulsion systems for autonomous sailboats requires innovative sail designs that balance aerodynamic performance and maritime operational reliability. This study presents a novel rigid wing sail system comprising a NACA 0020 main sail with an embedded NACA 0018 tail sail, specifically designed for uncrewed ocean navigation. Through systematic CFD analysis using ANSYS Fluent 2022R1, three configurations were compared: (1) the proposed hybrid wing–tail system, (2) a single main wing sail, and (3) traditional flap sails. The investigation focused on two key design parameters—tail sail area (25–40% of main sail area) and deflection angle (0–15°)—that were evaluated across angles of attack from 0° to 30° under typical marine wind conditions. The results reveal three critical findings: First, the hybrid system achieves a 29.5% higher peak lift coefficient than a single wing sail and an 11.6% improvement over slotted-flap sails. Second, increasing the tail sail area to 35% of the main sail optimizes both the lift coefficient (C_L max = 1.16) and the lift-to-drag ratio (L/D = 7.5 at 9° angles of attack). Third, as the tail deflection angle increases, the maximum lift–drag ratio shifts forward, and at small angles of attack, the maximum lift–drag ratio increases by 40%. The hybrid wing–tail sail design proposed in this study significantly enhances the aerodynamic performance of uncrewed sailing boats, providing new insights for the sustainable development of marine renewable energy technologies and autonomous vessels. Full article