Search Results (96)

The ongoing global outbreak of mpox caused by clade IIb viruses has led to more than 100,000 confirmed cases around the world, highlighting the urgent need for antiviral research to combat current and future mpox outbreaks. Reporter viruses expressing fluorescent proteins to monitor viral replication and virus spreading in cell culture provide a powerful tool for antiviral drug screening. In this work, we engineered two recombinant mpox clade IIb viruses by inserting, under the control of the vaccinia early/late promoter 7.5, the coding sequence of two different fluorescent proteins (EGFP and TurboFP635) in a previously unreported location within the viral genome. These recombinant viruses replicate in BSC-1 cells at rates similar to those of the parental virus. We show how these reporter mpox viruses allow the discrimination of infected cells by cell flow cytometry and facilitate the quantification of viral spread in cell culture. Finally, we validated these reporter viruses with two previously known inhibitors of poxvirus replication, cytosine arabinoside (AraC) and bisbenzimide. Full article

The automation of educational and instructional assessment plays a crucial role in enhancing the quality of teaching management. In physics education, calculation problems with intricate problem-solving ideas pose challenges to the intelligent grading of tests. This study explores the automatic grading of physics problems through a combination of large language models and prompt engineering. By comparing the performance of four prompt strategies (one-shot, few-shot, chain of thought, tree of thought) within two large model frameworks, namely ERNIEBot-4-turbo and GPT-4o. This study finds that the tree of thought prompt can better assess calculation problems with complex ideas (N = 100, ACC ≥ 0.9, kappa > 0.8) and reduce the performance gap between different models. This research provides valuable insights for the automation of assessments in physics education. Full article

The European Union’s path towards zero carbon dioxide emissions for new passenger vehicles necessitates a transitional period in which conventional vehicles coexist with zero-emission alternatives. This shift requires targeted strategies from engineers and policymakers, particularly in the area of road design, to reduce pollution. This study aims to investigate the environmental benefits of converting a two-lane urban roundabout into a turbo-roundabout through a virtual microsimulation approach using PTV VISSIM. The simulated model was calibrated and validated with real-world daily traffic data by properly adjusting the driving behavior parameters and comparing observed and modeled traffic volumes and queues. The Vehicle-Specific Power (VSP) emission method was applied to model, calculate and illustrate emissions by analyzing vehicle trajectories for the examined scenarios. Results show a statistically significant reduction in emissions for nearly all trips, with emissions decreasing by up to 44% across the intersection and its surrounding areas, and up to 23% at the intersection itself. Emissions are largely influenced by trip duration and traffic efficiency, both of which are enhanced by the improved geometric configuration of the case study intersection. These findings highlight that turbo-roundabouts represent an effective, environmentally sustainable design solution for urban intersections. Full article

This study focuses on optimizing traffic flow at uncontrolled intersections by comparing the effectiveness of different intersection types: roundabouts, signal-controlled intersections, and turbo-roundabouts. The purpose is to determine which type offers the best solution for enhancing traffic efficiency, reducing delays, and improving safety. The research employs simulation-based modeling to analyze traffic performance under varying traffic conditions. Critical parameters such as vehicle flow rate, average delay time, and capacity are used to assess the performance of each intersection type. The results indicate that turbo-roundabouts outperform conventional roundabouts and signal-controlled intersections in terms of both capacity and reduction in delays. The findings suggest that implementing turbo-roundabouts at high-traffic intersections can significantly improve traffic flow and reduce congestion. However, the effectiveness of each solution is context-dependent, with signal-controlled intersections still being advantageous under specific conditions, particularly in highly urbanized areas. This study provides valuable insights for transportation planners and engineers, highlighting the importance of intersection design in traffic optimization. Full article

For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat exchanger advancements. This paper presents the development of a high-pressure and high-temperature (HPHT) heat exchanger test facility designed for aero-engine heat exchangers. The maximum temperature and pressure of the test facility were configured to simulate the conditions of the last-stage compressor of a large civil engine, specifically 1000 K and 5.5 MPa. These conditions were achieved using multiple electric heater systems in conjunction with an air compression system consisting of three turbo compressor units and a reciprocating compressor unit. A commissioning test was conducted using a compact tubular heat exchanger, and the results indicate that the test facility operates stably and that the measured data closely align with the predicted performance of the heat exchanger. A commissioning test of the tubular heat exchanger showed a thermal imbalance of 1.02% between the high-pressure (HP) and low-pressure (LP) lines. This level of imbalance is consistent with the ISO standard uncertainty of ±2.3% for heat dissipation. In addition, CFD simulation results indicated an average deviation of approximately 1.4% in the low-pressure outlet temperature. The close alignment between experimental and CFD results confirms the theoretical reliability of the test bench. The HPHT thermal performance test facility will be expected to serve as a critical test bed for evaluating heat exchangers for current and future gas turbine applications. Full article

This article examines the flight dynamics and trajectory analysis of a parachute–payload system deployed from a C-17 aircraft. The aircraft is modeled with an open cargo door, extended flaps, and four turbo-fan engines operating at an altitude of 2000 feet Above Ground Level (AGL) and an airspeed of 150 knots. The payloads consist of simplified CONEX containers measuring either 192 inches or 240 inches in length, 9 feet in width, and 5.3 feet in height, with their mass and moments of inertia specified. At positive deck angles, gravitational forces cause these payloads to begin a gradual descent from the rear of the aircraft. For aircraft at zero deck angle, a ring-slot parachute with approximately 20% geometric porosity is utilized to extract the payload from the aircraft. This study specifically employs the CREATE-AV Kestrel simulation software to model the chute-payload system. The extraction and suspension lines are represented using Kestrel’s Catenary capability, with the extraction line connected to the floating confluence points of the CONEX container and the chute. The chute and payload will experience coupled motion, allowing for an in-depth analysis of the flight dynamics and trajectory of both elements. The trajectory data obtained will be compared to that of a payload (without chute and cables) exiting the aircraft at positive deck angles. An adaptive mesh refinement technique is applied to accurately capture the engine exhaust flow and the wake generated by the C-17, chute, and payloads. Friction and ejector forces are estimated to align the exit velocity and timing with those recorded during flight testing. The results indicate that the simulation of extracted payloads aligns with expected trends observed in flight tests. Notably, higher deck angles result in longer distances from the ramp, leading to increased exit velocities and reduced payload rotation rates. All payloads exhibit clockwise rotation upon leaving the ramp. The parachute extraction method yields significantly higher exit velocities and shorter exit times, while the payload-chute acceleration correlates with the predicted drag of the chute as demonstrated in prior studies. Full article

Laser metal deposition is a promising way to repair the surface defects of single-crystal components in turbo engines. Understanding the mechanisms and improving the efficiency of the repair have been long-standing problems. In this study, the influence of the substrate orientation on the laser metal deposition (LMD) was investigated and its effect on repair layer-step was examined. LMD experiments were conducted on single crystal superalloys with a normal substrate orientation (001)/[100] and with an optimized substrate orientation (101)/[10$\overline{1}$]. It reveals that the laser cladding with the optimized orientation leads to a larger height of the [001] dendrite region than that with the normal orientation. The calculated results of the growth velocity, thermal gradient, and susceptibility to CET in the dendrite-preferred growth direction indicate that, for the (101)/[10$\overline{1}$] orientation, the [001]/[100] boundary is located at relative high position in each layer, which not only decreases the formation ability of stray grain significantly, but also eliminates the appearance of the maximum susceptibility. This makes the necessary dilution position much higher, and thus, a large cladding step can be selected. Our findings could find potential applications in laser repair of single-crystal components. Full article

Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity analysis and inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, we incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity. This study also examine the impact of prompt engineering specifically designed for crash severity inference. The LLMs were tasked with crash severity inference to: (1) evaluate the models’ capabilities in crash severity analysis, (2) assess the effectiveness of CoT and domain-informed prompt engineering, and (3) examine the reasoning abilities with the CoT framework. Our results showed that LLaMA3-70B consistently outperformed the other models, particularly in zero-shot settings. The CoT and Prompt Engineering techniques significantly enhanced performance, improving logical reasoning and addressing alignment issues. Notably, the CoT offers valuable insights into LLMs’ reasoning process, unleashing their capacity to consider diverse factors such as environmental conditions, driver behavior, and vehicle characteristics in severity analysis and inference. Full article

This study investigates the performance and environmental impact of n-butanol blended with Jet-A fuel in turbo engines, aiming to assess its viability as a sustainable aviation fuel (SAF). The research involves the experimental testing of various blends, ranging from low to high concentrations of n-butanol, to determine their effects on engine performance and emissions. The experimental setup includes comprehensive measurements of engine parameters such as thrust, fuel consumption rates, and exhaust gas temperatures. Emissions of sulfur dioxide (SO₂), and carbon monoxide (CO) are also analyzed to evaluate environmental impacts. Key findings indicate that n-butanol/Jet-A blends can significantly enhance combustion efficiency and reduce emissions compared to conventional Jet-A fuel. Higher n-butanol concentrations lead to improved thermal efficiency and lower SO₂ and CO emissions. This study underscores the potential of n-butanol as an SAF for turbo engines, highlighting its ability to mitigate environmental impacts while maintaining or improving engine performance. This research supports the feasibility of integrating n-butanol into Jet-A blends for turbo engine applications, emphasizing their role in achieving more environmentally friendly aviation operations. Full article

According to the characteristics of the rotor system in an aero-engine and the vibrational test requirements of the aero-engine ground test, suitable vibration measurement sensors and test positions were selected. The vibration signals at the casings for the compressor and turbine of a twin-spool turbo-jet engine were collected under the states of maximum power and afterburning respectively, and the power spectrum analysis was carried out to determine the positions and causes of vibration. Furthermore, methods and preventive measures for eliminating vibration have been proposed. The results indicated that the main rotor vibration excited by mass imbalance in the twin-spool turbo-jet engine was significant. Rotor spindle misalignment or rotor radial stiffness unevenness also induced the vibration. The aerodynamic pulse vibration formed by the rotor blades of the first stage of the low pressure compressor was large, and rub induced vibration fault may occur at the turbine rotor seals. Based on the power spectrum analysis technology, the rotor system faults information including the type, position, and the degree can be quickly identified, and useful attempts and explorations have been made to reduce the vibration faults of the twin-spool turbo-jet engine. Full article

Background and Objectives: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30–50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. Methods: We analyze 751 neuroradiology cases from the American Journal of Neuroradiology using GPT-4 Turbo with customized prompts to improve diagnostic precision. Results: Initially, GPT-4 Turbo achieved a baseline diagnostic accuracy of 55.1%. By reformatting responses to list five diagnostic candidates and applying a 90% confidence threshold, the highest precision of the diagnosis increased to 72.9%, with the candidate list providing the correct diagnosis at 85.9%, reducing the misdiagnosis rate to 14.1%. However, this threshold reduced the number of cases that responded. Conclusions: Strategic prompt engineering and high confidence thresholds significantly reduce misdiagnoses and improve the precision of the LLM diagnostic in neuroradiology. More research is needed to optimize these approaches for broader clinical implementation, balancing accuracy and utility. Full article

This study investigates the efficacy of advanced large language models, specifically GPT-4o, Claude-3.5 Sonnet, and GPT-3.5 Turbo, in detecting software vulnerabilities. Our experiment utilized vulnerable and secure code samples from the NIST Software Assurance Reference Dataset (SARD), focusing on C++, Java, and Python. We employed three distinct prompting techniques as follows: Concise, Tip Setting, and Step-by-Step. The results demonstrate that GPT-4o and Claude-3.5 Sonnet significantly outperform GPT-3.5 Turbo in vulnerability detection. GPT-4o showed the highest improvement with the Step-by-Step prompt, achieving an F1 score of 0.9072. Claude-3.5 Sonnet exhibited consistent high performance across all prompt types, with its Step-by-Step prompt yielding the best overall results (F1 score: 0.8933, AUC: 0.74). In contrast, GPT-3.5 Turbo showed minimal performance changes across prompts, with the Tip Setting prompt performing best (AUC: 0.65, F1 score: 0.6772), yet significantly lower than the other models. Our findings highlight the potential of advanced models in enhancing software security and underscore the importance of prompt engineering in optimizing their performance. Full article

In numerical simulations, achieving high accuracy without significantly increasing computational cost is often a challenge. To address this issue, this paper proposes an improved finite volume Weighted Essentially Non-Oscillatory (WENO) scheme for structured grids. By employing a single-point quadrature rule to perform flux integration on the control volume faces, this scheme is designed for use in NUAA-Turbo three-dimensional fluid solvers based on structured grids, utilizing RANS and RANS/LES coupling to simulate turbomachinery flows. Firstly, the new WENO scheme is validated against classical numerical test cases to evaluate its stability and reliability in handling discontinuities, double Mach reflection problems, and Rayleigh–Taylor (RT) instability. Compared to the original scheme, this improved finite-volume WENO scheme demonstrates better stability near discontinuities and more effectively resolves flow features at the same grid resolution. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, calculations using RANS are performed and the results obtained with WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupling simulations of turbine wake and film cooling. The results indicate that the improved finite-volume WENO scheme provides better stability and accuracy in engineering applications. For instance, the average error in calculating compressor efficiency characteristics is reduced from 0.76% to 0.05%, the error in turbine vane pressure distribution compared to the experimental values is within 1%, and the error in film cooling efficiency centerline distribution compared to the experimental values is within 3%. Additionally, the qualitative results of turbine wake and film cooling show that even with a small number of grid points, more detailed flow physics can be captured, thereby reducing computational costs in aerodynamic applications. Full article

The rapid evolution of large language models, in particular OpenAI’s GPT-3.5-turbo and GPT-4, indicates a growing interest in advanced computational methodologies. This paper proposes a novel approach to synthetic data generation and knowledge distillation through prompt engineering. The potential of large language models (LLMs) is used to address the problem of unbalanced training datasets for other machine learning models. This is not only a common issue but also a crucial determinant of the final model quality and performance. Three prompting strategies have been considered: basic, composite, and similarity prompts. Although the initial results do not match the performance of comprehensive datasets, the similarity prompts method exhibits considerable promise, thus outperforming other methods. The investigation of our rebalancing methods opens pathways for future research on leveraging continuously developed LLMs for the enhanced generation of high-quality synthetic data. This could have an impact on many large-scale engineering applications. Full article