Jack Miller Center

Abstract Modern aeroengine designers face the challenge of reducing fuel consumption, which is pushing compressor technology into new design spaces. To optimize these design spaces, high-fidelity computational models are crucial to the compressor design process. It is important to validate these models with experimental data. Nonintrusive measurement techniques allow for the acquisition of data well suited for this comparison, as they do not lead to local disruptions of the flow field. This investigation utilized three-component laser Doppler velocimetry to acquire a unique dataset of detailed unsteady velocity measurements in the vaned diffuser of an aeroengine centrifugal compressor. These measurements allowed for a thorough study of the flow development in the vaneless and semivaneless space of the diffuser as well as through the diffuser passage. The components of the Reynolds stress tensor were also determined at multiple locations in the diffuser flow field. These data are used to study the jet and wake propagation from the impeller exit flow field into the diffuser passage and the resulting secondary flow structures. Predictions from unsteady computational fluid dynamics (CFD) simulations using the shear stress transport (SST) and baseline k–ω explicit algebraic Reynolds stress model (BSL-EARSM) turbulence models are also compared with these experimental data. Both turbulence models yielded results that qualitatively agreed with the experimental radial velocity profile through the vaneless and semivaneless space. With the experimentally determined Reynolds stress tensor, the turbulent kinetic energy (TKE) is calculated at multiple points through the flow field and is compared to the TKE at the same geometric locations in the computational flow field. This comparison highlights the difference in dissipation and production of turbulence between the experimental data and the computational predictions. Investigating the differences in TKE throughout the diffuser helps elucidate the differences in predicted flow structures in the diffuser passage.

In software engineering, deep learning models are increasingly deployed for critical tasks such as bug detection and code review. However, overfitting remains a challenge that affects the quality, reliability, and trustworthiness of software systems that utilize deep learning models. Overfitting can be (1) prevented (e.g., using dropout or early stopping) or (2) detected in a trained model (e.g., using correlation-based approaches). Both overfitting detection and prevention approaches that are currently used have constraints (e.g., requiring modification of the model structure, and high computing resources). In this paper, we propose a simple, yet powerful approach that can both detect and prevent overfitting based on the training history (i.e., validation losses). Our approach first trains a time series classifier on training histories of overfit models. This classifier is then used to detect if a trained model is overfit. In addition, our trained classifier can be used to prevent overfitting by identifying the optimal point to stop a model's training. We evaluate our approach on its ability to identify and prevent overfitting in real-world samples. We compare our approach against correlation-based detection approaches and the most commonly used prevention approach (i.e., early stopping). Our approach achieves an F1 score of 0.91 which is at least 5% higher than the current best-performing non-intrusive overfitting detection approach. Furthermore, our approach can stop training to avoid overfitting at least 32% of the times earlier than early stopping and has the same or a better rate of returning the best model.

ABSTRACTWe show theoretically that variable production costs reduce systematic risk of firms' cash flows if capital and variable inputs are complementary in firms' production and input prices are procyclical. In our dynamic model, this operating hedge effect is weaker for more profitable firms, giving rise to a gross profitability premium. Moreover, gross profitability and value factors are distinct and negatively correlated, and their premia are not captured by the capital asset pricing model (CAPM). We estimate the model by simulated method of moments, and find that its main implications for stock returns and cash flow dynamics are quantitatively consistent with the data.

Background: The lung parenchyma is affected by pneumonia, an acute infectious disease that can be brought on by fungus, bacteria, or viruses. By vaccination, a healthy diet, and the abolition of environmental variables, pneumonia can be avoided. All worldwide deaths of children under the age of five are due to it. Aim: To prevent pneumonia with easy steps, and can be treated with easy, affordable medications with adequate care, early detection, and prompt admission of sick children to hospitals. Methods: This case series study was carried at pediatric department of district kohat hospital from august 2022 to Feb 2023. Ethical acceptance certificate was obtained from the hospital. Informed written consent was taken from those parents who were willing to answer and were fully qualified the inclusive criteria. Total 139 patients were enrolled in the study. The results were analyzed through spss-ver 24 Results: In clinical sign and symptoms highest ratio was seen in lethargy 33(23.74%) and lowest ratio seen in cyanosis 4 (2.87%), in gender wise the male 82(59%) presentation was more than females 57(41%). Lowest ratio of vaccination was seen in 2(28%) dose 6 and highest in non-vaccination was 21(44%) in dose 1. Conclusion: The health planners should concentrate on the missed epi schedule. Efficacy, we must employ media like radio, television, and newspapers Keywords: Pneumonia, education, health, presentation, Kohat

Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability identification is important because it can evaluate large codebases more efficiently than manual code auditing. Many Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years. However, a survey that summarises, classifies, and analyses the application of ML/DL models for vulnerability detection is missing. It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey. This could result in essential areas of research being overlooked or under-represented, leading to a skewed understanding of the state of the art in vulnerability detection. This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches via five primary research questions (RQs). Specifically, our RQ1 examines the trend of publications that leverage ML/DL for vulnerability detection, including the evolution of research and the distribution of publication venues. RQ2 describes vulnerability datasets used by existing ML/DL-based models, including their sources, types, and representations, as well as analyses of the embedding techniques used by these approaches. RQ3 explores the model architectures and design assumptions of ML/DL-based vulnerability detection approaches. RQ4 summarises the type and frequency of vulnerabilities that are covered by existing studies. Lastly, RQ5 presents a list of current challenges to be researched and an outline of a potential research roadmap that highlights crucial opportunities for future work.

Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures. To avoid misleading practitioners, AIOps model interpretations should be consistent (i.e., different AIOps models on the same task agree with one another on feature importance). However, many AIOps studies violate established practices in the machine learning community when deriving interpretations, such as interpreting models with suboptimal performance, though the impact of such violations on the interpretation consistency has not been studied. In this paper, we investigate the consistency of AIOps model interpretation along three dimensions: internal consistency, external consistency, and time consistency. We conduct a case study on two AIOps tasks: predicting Google cluster job failures, and Backblaze hard drive failures. We find that the randomness from learners, hyperparameter tuning, and data sampling should be controlled to generate consistent interpretations. AIOps models with AUCs greater than 0.75 yield more consistent interpretation compared to low-performing models. Finally, AIOps models that are constructed with the Sliding Window or Full History approaches have the most consistent interpretation with the trends presented in the entire datasets. Our study provides valuable guidelines for practitioners to derive consistent AIOps model interpretation.