Search Results (133)

Model validation is an essential part of CFD-based projects. Despite being successfully employed for decades, the level and extent of CFD model validation details vary significantly in the published literature, which, in turn, adversely affects the repeatability and usefulness of published models and data. This study explores the various challenges associated with validating CFD models of thermodynamic components, namely, the compressors and their performance evaluation. The methodology involves blade generation through TurboGrid and BladeGen, mesh generation to ensure computational efficiency, and pre-processing with CFX to define boundary conditions and turbulence models, all within ANSYS 2024 R1. Three case studies are discussed, each assessing different compressor configurations and common challenges encountered during the model validation stage. Based on the case studies, a number of recommendations are presented relating to best practices in terms of both the use of published materials to validate new models and the level of detail required for experimental or simulation publication to ensure they can be replicated or used to validate a new model. Full article

Unified Modeling Language (UML) diagrams serve as essential tools for visualizing system structure and behavior in software design. With the emergence of Large Language Models (LLMs) that automate various phases of software development, there is growing interest in leveraging these models for UML diagram generation. This study presents a comprehensive empirical investigation into the effectiveness of GPT-4-turbo in generating four fundamental UML diagram types: Class, Deployment, Use Case, and Sequence diagrams. We developed a novel rule-based prompt-engineering framework that transforms domain scenarios into optimized prompts for LLM processing. The generated diagrams were then synthesized using PlantUML and evaluated through a rigorous survey involving 121 computer science and software engineering students across three U.S. universities. Participants assessed both the completeness and correctness of LLM-assisted and human-created diagrams by examining specific elements within each diagram type. Statistical analyses, including paired t-tests, Wilcoxon signed-rank tests, and effect size calculations, validate the significance of our findings. The results reveal that while LLM-assisted diagrams achieve meaningful levels of completeness and correctness (ranging from 61.1% to 67.7%), they consistently underperform compared to human-created diagrams. The performance gap varies by diagram type, with Sequence diagrams showing the closest alignment to human quality and Use Case diagrams exhibiting the largest discrepancy. This research contributes a validated framework for evaluating LLM-generated UML diagrams and provides empirically-grounded insights into the current capabilities and limitations of LLMs in software modeling education. Full article

Turbo Codes (TCs) are a family of convolutional codes that provide powerful Forward Error Correction (FEC) and operate near the Shannon limit for channel capacity. In the context of modern communication systems, such as those conforming to the DVB-RCS2 standard, Turbo Encoders (TEs) play a crucial role in ensuring robust data transmission over noisy satellite links. A key computational bottleneck in the Turbo Encoder is the non-uniform interleaving stage, where input bits are rearranged according to a dynamically generated permutation pattern. This stage often requires the intermediate storage of data, resulting in increased latency and reduced throughput, especially in embedded or real-time systems. This paper introduces a vector processing algorithm designed to accelerate the interleaving stage of the Turbo Encoder. The proposed algorithm is tailored for vector DSP architectures (e.g., CEVA-XC4500), and leverages the hardware’s SIMD capabilities to perform the permutation operation in a structured, phase-wise manner. Our method adopts a modular Load–Execute–Store design, facilitating efficient memory alignment, deterministic latency, and hardware portability. We present a detailed breakdown of the algorithm’s implementation, compare it with a conventional scalar (serial) model, and analyze its compatibility with the DVB-RCS2 specification. Experimental results demonstrate significant performance improvements, achieving a speed-up factor of up to 3.4× in total cycles, 4.8× in write operations, and 7.3× in read operations, relative to the baseline scalar implementation. The findings highlight the effectiveness of vectorized permutation in FEC pipelines and its relevance for high-throughput, low-power communication systems. Full article

Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study examined a total of 56 participants, comprising 31 SpA patients (n = 31) and 25 healthy controls (n = 25), who underwent sacroiliac joint MRI between August 2018 and August 2020. T2 mapping images were generated using multi-echo turbo spin echo (TSE) sequence, and quantitative T2 relaxation times were measured from bone and cartilage regions. Statistical analysis employed appropriate parametric and non-parametric tests with significance set at p < 0.05. Results: The mean T2 relaxation time measured from the areas with osteitis of SpA patients (100.23 ± 7.41 ms; 95% CI: 97.51–102.95) was significantly higher than that of the control group in normal bone (69.44 ± 4.37 ms; 95% CI: 67.64–71.24), and this difference was found to be statistically significant (p < 0.001). No significant difference was observed between cartilage T2 relaxation times in SpA patients and controls (p > 0.05). Conclusions: T2 mapping serves as a valuable quantitative imaging biomarker for diagnosing sacroiliitis associated with SpA, particularly by detecting bone marrow edema. The technique shows promise for objective disease assessment, though larger studies are needed to establish standardized reference values for T2 relaxation times in osteitis to enhance diagnostic accuracy and facilitate treatment monitoring. Full article

The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges. Full article

Generative artificial intelligence (genAI) shows promising results in clinical practice. This study compared a GPT-4-turbo virtual assistant with physicians from Italy, France, Spain, and Portugal on medical knowledge derived from national exams while analysing knowledge retention over time and domain-specific performance. Via a digital platform, 17,144 physicians provided 221,574 answers to 600 exam questions between December 2022 and February 2024. Physicians were stratified by years since graduation and specialty, and the assistant answered the same questions in each native language. Differences in proportions of correct answers were tested with binomial logistic regression (odds ratios, 95% CI) or Fisher’s exact test (α = 0.05). The assistant outperformed physicians in all countries (72–96% vs. 46–62%; logistic regression, p < 0.001). Physicians also trailed the assistant across most knowledge domains (p < 0.001), except paediatrics (45% vs. 52%; Fisher, p = 0.60). Accuracy declined with seniority, falling 4–10% between the youngest and oldest cohorts (logistic regression, p < 0.001). Overall, genAI exceeds practising doctors on broad medical knowledge and may help counter knowledge attrition, though paediatrics remains a domain requiring targeted refinement. Full article

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ($T_1$ and $T_2$) and flow rate (${\dot{V}}_1$) and cooling water inlet and outlet temperatures ($T_3$ and $T_4$) and flow rate (${\dot{V}}_3$), as well as chiller power consumption (${\dot{W}}_c$). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system. Full article

This paper presents a comprehensive study on dynamic rotor balancing and vibration analysis as part of a predictive maintenance framework for thermal power plants, with a case study focused on the TVF-100-2 turbo generator. The methodology involves on-site multi-plane balancing under real operational conditions, supported by spectral vibration diagnostics, phase angle evaluation, and orbit analysis. These advanced techniques enable precise identification of unbalance-related vibration issues and their effective mitigation without disassembly. This study demonstrates how integrating dynamic balancing with continuous monitoring and diagnostic analysis enhances operational stability and extends equipment lifespan. The findings contribute to more efficient predictive maintenance strategies, with significant implications for reducing downtime and improving the reliability of rotating machinery in thermal power generation. Full article

The incorporation of artificial intelligence in educational contexts has significantly transformed the support provided to students facing learning difficulties, facilitating both the management of their educational process and their emotions. Additionally, online comments play a vital role in understanding student feelings. Analyzing comments on social media platforms can help identify students in vulnerable situations so that timely interventions can be implemented. However, manually analyzing student-generated content on social media platforms is challenging due to the large amount of data and the frequency with which it is posted. In this sense, the recent revolution in artificial intelligence, marked by the implementation of powerful large language models (LLMs), may contribute to the classification of student comments. This study compared the effectiveness of a supervised learning approach using five different LLMs: bert-base-uncased, roberta-base, gpt-4o-mini-2024-07-18, gpt-3.5-turbo-0125, and gpt-neo-125m. The evaluation was carried out after fine-tuning them specifically to classify student comments on social media platforms with anxiety/depression or neutral labels. The results obtained were as follows: gpt-4o-mini-2024-07-18 and gpt-3.5-turbo-0125 obtained 98.93%, roberta-base 98.14%, bert-base-uncased 97.13%, and gpt-neo-125m 96.43%. Therefore, when comparing the effectiveness of these models, it was determined that all LLMs performed well in this classification task. Full article

Turbo sazae, a commercially and ecologically significant marine gastropod traditionally found in Jeju Island and the southern coast of Korea, is experiencing a reported northward expansion into the East Sea, likely influenced by rising seawater temperatures. This study provides preliminary genetic insights into the genetic structure and connectivity of T. sazae populations between Jeju and the East Sea using mitochondrial COI sequences. Samples from 6 geographically distinct locations were analyzed, with three cloned replicates generated to enhance sequence reliability. Genetic diversity, haplotype distribution, and population differentiation were then assessed. Our analysis reveals potential genetic connectivity between Jeju and East Sea populations, possibly driven by larval dispersal via the Kuroshio and Tsushima Currents, highlighted by the predominance of shared haplotype EJ1 (60.0% in Jeju, 50.0% in East Sea). Bayesian phylogenetic analysis estimated the time to the most recent common ancestor (MRCA) between Jeju and East Sea populations at approximately 9.7 to 23.3 million years ago, indicating ancient divergence rather than very recent separation. Pairwise FST values and AMOVA results showed generally low levels of genetic differentiation. Given the small sample sizes and use of a single mitochondrial marker, these findings should be interpreted cautiously as preliminary evidence. Nevertheless, this study highlights the need for continued genetic monitoring of T. sazae populations under climate-driven range shifts and provides a foundation for future research incorporating broader genomic approaches. Full article

Integrating causal inference and estimation methods, especially in Natural Language Processsing (NLP), is essential to improve interpretability and robustness in deep learning (DL) models. The objectives are to present the IV-NLP methodology and its application. IV-NLP integrates two approaches. The first defines the process of the inference and estimation of the causal effect in original, predicted, and synthetic data. The second one includes a validation method of the results obtained by the selected Large-Language Model (LLM). IV-NLP proposes to use synthetic data in predictive tasks only if the causal effect pattern of the synthetic data is aligned with the causal effect pattern of the original data. DL models, the Instrumental Variable (IV) method, statistical methods, and GPT-3.5-turbo-0125 were used for its application, including an intervention method using a variation of the Retrieval-Augmented Generation (RAG) technique. Our findings reveal notable discrepancies between the original and synthetic data, highlighting that the synthetic data do not fully capture the underlying causal effect patterns of the original data, evidencing homogeneity and low diversity in the synthetic data. Interestingly, when evaluating the causal effect in the predictions made by our three best DL models, it was verified that the model with the lowest accuracy (84.50%) was fully aligned with the overall causal effect pattern. These results demonstrate the potential of integrating DL and LLM models with causal inference methods. Full article

Due to the limitations of pure electric power endurance, turbo-electric hybrid power systems, which offer a high power-to-weight ratio, present a reliable solution for medium- and large-sized vertical take-off and landing (VTOL) aircraft. Traditional energy management strategies often fail to minimize fuel consumption across the entire flight profile while meeting power demands under varying flight conditions. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based energy management strategy (EMS) specifically designed for turbo-electric hybrid propulsion systems. Firstly, the proposed strategy employs a Prior Knowledge-Guided Deep Reinforcement Learning (PKGDRL) method, which integrates domain-specific knowledge into the Deep Deterministic Policy Gradient (DDPG) algorithm to improve learning efficiency and enhance fuel economy. Then, by narrowing the exploration space, the PKGDRL method accelerates convergence and achieves superior fuel and energy efficiency. Simulation results show that PKGDRL has a strong generalization capability in all operating conditions, with a fuel economy difference of only 1.6% from the offline benchmark of the optimization algorithm, and in addition, the PKG module enables the DRL method to achieve a huge improvement in terms of fuel economy and convergence rate. In particular, the prospect theory (PT) in the PKG module improves fuel economy by 0.81%. Future research will explore the application of PKGDRL in the direction of real-time total power prediction and adaptive energy management under complex operating conditions to enhance the generalization capability of EMS. Full article

Cedar virus (CedV), closely related to the Hendra and Nipah viruses, is a novel Henipavirus that was originally isolated from flying foxes in Australia in 2012. Although its glycoprotein G exhibits relatively low sequence similarity with its counterparts of the Hendra and Nipah viruses, CedV also uses ephrin receptors, i.e., ephrins B1, B2, A2 and A5, to enters human cells. Nevertheless, the entry mechanism of CedV into bat cells remains unexplored. Considering that Rousettus aegyptiacus (Egyptian Rousette bat, ERB) is postulated to be a reservoir host for henipaviruses, we aim to reveal the receptors utilized by CedV to enable its entry into ERB cells. To this end, we cloned the class A and B ephrins of ERB and generated CHO-K1 cells stably expressing individual ephrins. We also developed a lentivirus-based pseudovirus system containing the firefly luciferase reporter. Assessment of the luciferase activity in cells expressing single ephrins demonstrated that the ERB ephrin B1 and B2 mediated CedV pseudovirus entry. Further, we generated a recombinant CedV expressing the fluorescent protein TurboFP635 (rCedV-nTurbo635). By performing high-content microscopy and flow cytometry, we unveiled that, in addition to ephrin B1 and B2, ephrin A5 was also able to mediate rCedV-nTurbo635 entry, although to a much lesser extent. In contrast to human ephrin A2, ERB ephrin A2 failed to mediate rCedV-nTurbo635 entry. Finally, we generated ERB epithelial cells with ephrin B1 and/or ephrin B2 knockdown (KD). The entry of rCedV-nTurbo635 into ERB epithelial cells was drastically impaired by ephrin B1/B2 KD, validating the importance of ephrin B1 and B2 in its entry. Altogether, we conclude that CedV primarily employs ERB ephrin B1, B2 and, possibly, A5 for its entry into ERB cells. Full article

Premature seal failure induced by the unevenness of interstage pressure distribution in multi-stage brush seals significantly compromises the sealing efficiency of Air-Turbo Rocket (ATR) engines operating under high-pressure (megapascal-level) differential conditions. Conventional pressure equalization designs for such seals often result in significant leakage rate increases. This study addresses the pressure imbalance phenomenon in four-tooth three-stage brush composite seals through a novel fractal–geometric porous-media model, rigorously validated against experimental data. Systematic investigations were conducted to elucidate the effects of structural parameters and operational conditions on both sealing performance and pressure distribution characteristics. Key findings reveal that, under the prototype structure parameter, the first-, second-, and third-stage brush bundles account for 18.3%, 30.0%, and 43.3% of the total pressure drop, respectively, with grate teeth contributing 8.4%, demonstrating an inherent pressure imbalance. Axial brush spacing exhibits a minimal impact on the pressure distribution, while the gradient thickness settings of the brush bundles show limited influence. Radial clearance optimization and gradient backplate height adjustment effectively regulate pressure distribution, albeit with associated leakage rate increases. Structural modifications based on these principles achieved only a 5.8% leakage increment while reducing the maximum bundle pressure drop by 23%, demonstrating effective pressure balancing. A simplified analysis of entropy reveals that the fundamental mechanism governing the pressure imbalance stems from non-uniform entropy generation caused by aerodynamic damping dissipation across sequential brush stages. These findings establish a dampened dissipation-based theoretical framework for designing high-performance multistage brush seals in aerospace applications, providing critical insights for achieving an optimal balance between leakage control and pressure equalization in extreme-pressure environments. Full article

Background/Objectives: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM’s capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. Methods: We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating “long-term memory” and “short-term memory” components on a curated neurological corpus. Five representative clinical scenarios were presented to 13 neurologists and the AI system. Participants formulated differential diagnoses based on initial presentations, followed by definitive diagnoses after receiving conclusive clinical information. Two senior academic neurologists blindly evaluated all responses, while an independent investigator assessed the verifiability of AI-generated information. Results: AI achieved a significantly higher normalized score (86.17%) compared to neurologists (55.11%, p < 0.001). For differential diagnosis questions, AI scored 85% versus 46.15% for neurologists, and for final diagnosis, 88.24% versus 70.93%. AI obtained 15 maximum scores in its 20 evaluations and responded in under 30 s compared to neurologists’ average of 9 min. All AI-provided references were classified as relevant with no hallucinatory content detected. Conclusions: A specialized LLM demonstrated superior diagnostic performance compared to practicing neurologists across complex clinical challenges. This indicates that appropriately harnessed LLMs with curated knowledge bases can achieve domain-specific relevance in complex clinical disciplines, suggesting potential for AI as a time-efficient asset in clinical practice. Full article