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Drift Boss: Drive really far
No full textGet ready to slide into the driver's seat of Drift Boss, a deceptively simple yet addictive driving game where timing is everything. Forget complex controls; this game is all about the one-button mechanic. Your mission is to navigate endless, winding tracks floating high in the sky, drifting around sharp corners with perfect precision. It sounds easy, but the procedurally generated paths demand intense focus and quick reflexes. As you collect coins and unlock unique vehicles—from taxis to fire trucks—you’ll find yourself hooked, constantly chasing that next high score. Can you stay on track, or will you plummet into the abyss
The effect of Pomegranate juice on C. elegans under Thermal Stress
No full textThis experiment analyzed the effects of pomegranate juice on C. elegans under thermal stress. C. elegans, a nematode with similar genes to humans, is an effective model for studying human diseases. Thermal stress occurs when an organism is exposed to high temperatures, which results in the release of heat shock proteins. Heat shock proteins act as molecular chaperones that help restore protein homeostasis following heat stress. Despite their presence, thermal stress can still weaken antioxidant defense in organisms. However, pomegranate juice is rich in antioxidants and polyphenols, which can help strengthen antioxidant defense. In this study, pomegranate extract was dissolved in water and mixed with agar powder. Using this pomegranate mixed agar, E. coli was cultured for 24 hours. The results showed that the 5 mg/ml concentration increased survival rates of C. elegans. However, the 10 and 20 mg/ml concentrations lowered survival rates, which can be attributed to dose-dependent toxicity. Interestingly, the higher concentrations of pomegranate extract significantly increased C. elegans reproductive rates. This research expands on previous studies that examined the effect of pomegranate juice on C. elegans and distinctly focuses on its role under thermal stress. This distinction helps gain a deeper insight into pomegranate juice's effect on oxidative stress.
 
Enhancing Wind Turbine Reliability: A Hybrid State-Space and Generative Approach to SCADA-Based Fault Detection
No full textWind turbine reliability is essential for the renewable energy sector, as failures in key parts such as gearboxes and main bearings lead to more than $10 billion in downtime and maintenance costs each year. Supervisory control and data acquisition (SCADA) systems can monitor turbines using signals such as vibration, power output, and wind speed; however, applying machine learning to this data type is challenging due to the presence of unbalanced fault types and complex time patterns. Previous research has explored physics-informed deep learning, digital twins, and contrastive learning, achieving noteable fault detection accuracy. However, challenges remain in detecting rare faults, dealing with imbalanced data, combining data sources, and model generalization. This study presents StateSpaceNetWithGen (SS-Gen), a hybrid model integrating state-space modeling for temporal dynamics with generative augmentation for class imbalance. Tested on a 35,000-sample SCADA dataset (2018–2019), SS-Gen achieved high accuracy (≈1.00) and F1-score (≈1.00) on this specific dataset, improving by 33% over baselines. To further validate the strengths of the proposed method, the methodology is validated on a second dataset with different distribution. These results support more reliable and interpretable wind turbine health monitoring and move the field toward stronger physics-informed and federated machine learning solutions.OPEN ACCESS Received: 06/10/2025 Accepted: 19/11/2025 Published: 23/01/202
Design and Implementation of a Vulkan-Based Rasterization System in the PC Environment
No full textGraphics application programming interfaces (APIs) for threedimensional (3D) rendering have been employed in computer graphics. Traditional 3D graphics APIs, such as Open Graphics Library (OpenGL), have structured the entire pipeline for the programmer’s convenience and are relatively easy to use. With the advancement of graphics hardware technology, new graphics APIs, including Vulkan, have been introduced to control specific function units of the graphics card and reveal the graphics processing power. Vulkan has many advantages, such as graphics processing power and parallel processing support. However, it also has the disadvantage of increasing the implementation costs because it requires detailed controls. This paper aims to implement a rasterization pipeline that applies a local illumination model with Vulkan. The implemented system can be used for standardized 3D graphics tasks as is and can also be used as a starting point for varying the pipeline configurations. This work designs and implements a Vulkan-based local rasterization pipeline. With this implemented platform, practical 3D rendering scenes are executed to be compared and analyzed with the rendering results from traditional OpenGL. The experimental results demonstrate the correctness and efficiency of the implementation. The implementation will be used as a testbed for various rendering experiments in the future.OPEN ACCESS Received: 27/07/2025 Accepted: 11/10/2025 Published: 23/01/202
Advanced Reliability Analysis with Applications of Hjorth Constant-Stress Normal-Operating Setting Using Newly Progressive Censored Data
No full textModern high-reliability products fail rarely, so researchers rely on accelerated life testing to obtain failure information within practical time limits. This study presents a useful framework integrating constant-stress accelerated life tests with an improved adaptive progressive Type-II censoring plan to estimate the reliability function under normal operating conditions of the Hjorth model. The Hjorth model is chosen because its hazard rate can be constant, increasing, decreasing, or bathtub-shaped, which reduces errors due to incorrect hazard assumptions. Stress affects lifetime through a log-linear relationship applied to the scale and the shape parameter. We derive the full likelihood for the proposed censoring plan across several stress levels, obtain maximum likelihood estimates with confidence intervals based on the observed information matrix, and develop a Bayesian analysis with informative prior distributions and sampling by Markov chain Monte Carlo technique. We then estimate reliability at normal operating conditions together with its interval estimates by both approaches. Extensive Monte Carlo simulations demonstrate the superior accuracy of the Bayesian estimators, especially when the number of observed failures is small or censoring is heavy, while maintaining interval coverage close to the nominal level. The practical utility of the proposed methodology is demonstrated through its application to real-world accelerated lifetime data sets. Applications to real-world data sets show that the proposed model fits the data well and yields reliable estimates of reliability at normal operating conditions.OPEN ACCESS Received: 26/07/2025 Accepted: 28/10/2025 Published: 23/01/202
Variations in morphology and microstructure caused by different melt pool dynamics in laser powder bed fusion of ultrathin walls
No full textAnalytical Investigation of Global and Local Instability in Additively Manufactured Lattice-Core Sandwich Columns
No full textTheoretical Analysis of Seepage Field Distribution Induced by Pre-Excavation Dewatering Considering the Thickness of Waterproof Curtain and Delayed Responses of Water Table
No full textDewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten structural stability. To mitigate such risks, suspended waterproof curtains have been widely employed to elongate seepage paths and reduce groundwater flow velocities. However, accurately predicting seepage field, especially under transient groundwater conditions with phreatic surfaces and varying curtain geometries, remains challenging. This study develops a theoretical model addressing transient groundwater seepage in foundation pits, explicitly considering a moving phreatic surface, curtain penetration depth and thickness. The proposed analytical solution is validated against experimental results and numerical simulations performed using COMSOL Multiphysics. Parametric analyses reveal that decreasing the vertical distance between the retaining wall base and the impermeable layer from 30 to 10 m reduces external groundwater drawdown by approximately 52%. Additionally, thicker waterproof curtains markedly decrease internal drawdown magnitudes, redirect seepage pathways, and effectively lower external groundwater depletion. Analyses on specific yield reveal delayed water release significantly moderates drawdown rates, reducing ultimate drawdown magnitudes. Furthermore, elevated internal excavation water levels intensify hydraulic head differences, substantially extending seepage-affected zones and amplifying drawdown responses both inside and outside the foundation pit. Overall, these findings provide critical theoretical insights for optimizing foundation pit design and improving dewatering practices, ensuring excavation safety and mitigating environmental impacts.OPEN ACCESS Received: 11/08/2025 Accepted: 05/11/2025 Accepted: 03/02/202
NAS-Driven Quantitative Assessment of Overpressure Genesis in Sedimentary Basins
No full textThe study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation and distribution patterns of hydrocarbon resources is essential for practical hydrocarbon exploration. However, the prediction of anomalous high-pressure genesis currently encounters numerous challenges, including the complexity of overpressure genesis, the superimposed effect of multiple mechanisms, and the dependence of traditional models on manual analysis, resulting in inefficiency and quantification challenges. To this end, this paper proposes a method for identifying and quantitatively evaluating stratigraphic overpressure mechanisms based on neural architecture search, to enable rapid and accurate quantitative evaluation of overpressure mechanisms. The results show that the main anomalous high-pressure genesis mechanisms in the target work area include undercompaction, fluid expansion and tectonic compression, with contribution rates of approximately 73% for undercompaction, and 9% and 18% for fluid expansion and tectonic compression, respectively. The model’s accuracy in the test set reaches 95.4%, significantly enhancing the identification accuracy of anomalous stratigraphic pressure genesis and its superposition relationships. The innovation of this paper lies in the combination of wave velocity-density rendezvous map with clustering algorithm and neural architecture search algorithm, offering an efficient approach to identify multiple overpressure genesis mechanisms and predict pore pressure through machine learning algorithms, which is of great theoretical significance and practical application value.OPEN ACCESS Received: 08/08/2025 Accepted: 14/10/2025 Published: 03/02/202
How Can an Appropriate CFD Model be Developed for Turbulent Flow in Rough Pipes?: Evidence from Friction Factor Prediction
No full textThis paper answers the question: “How can an appropriate turbulent rough pipe flow computational fluid dynamics (CFD) model be developed?” The Reynolds-averaged Navier-Stokes equations with the standard k-epsilon turbulence model and scalable wall functions were solved to obtain Fanning friction factors and mean velocity profiles in inflectional and monotonic rough pipes. CFD models with near-wall grid sizes from four dimensionless wall distances and two roughness treatment approaches were simulated. Eight roughness Reynolds numbers, covering the lower end of the transitionally rough regime through the fully rough regime, were studied for each roughness type. Appropriate roughness and turbulence model constants for turbulent rough pipe flows in the transitionally rough regime were determined. For model validation, the predicted mean axial velocity profiles for Reynolds numbers of 5 × 104and 5 × 105exhibited good agreement with the reference experimental data. A total of 208 CFD simulations (32 from our previous works and 176 from the present study) were analyzed. Finally, based on comparisons between predicted Fanning friction factors and established correlations, appropriate CFD models for turbulent flows in inflectional and monotonic rough pipes were identified. Suitable CFD models for accurately predicting mean velocity profiles at roughness Reynolds numbers below 11.225 were also obtained, although with the caution that improved mean velocity prediction may reduce Fanning friction factor accuracy. Furthermore, the present CFD work provides essential guidance for extending simulations to other rough surface types and rough-wall flow situations.OPEN ACCESS Received: 25/11/2025 Accepted: 30/12/202