Additional Features Research Articles

Improved filters for angular filter refractometry

Angular filter refractometry is an optical diagnostic that measures the absolute contours of a line-integrated density gradient by placing a filter with alternating opaque and transparent zones in the focal plane of a probe beam, which produce corresponding alternating light and dark regions in the image plane. Identifying transitions between these regions with specific zones on the angular filter (AF) allows the line-integrated density to be determined, but the sign of the density gradient at each transition is degenerate and must be broken using other information about the object plasma. Additional features from diffraction in the filter plane often complicate data analysis. In this paper, we present an improved AF design that uses a stochastic pixel pattern with a sinusoidal radial profile to minimize unwanted diffraction effects in the image caused by the sharp edges of the filter bands. We also present a technique in which a pair of AFs with different patterns on two branches of the same probe beam can be used to break the density gradient degeneracy. Both techniques are demonstrated using a synthetic diagnostic and data collected on the OMEGA EP (extended performance) laser.

The role of connectivity in conservation planning for species with obligatory interactions: Prospects for future climate scenarios

AbstractClimate change may lead to range shifts, and barriers to such displacements may result in extirpations from previously suitable habitats. This may be particularly important in freshwater ecosystems that are highly fragmented by anthropogenic obstacles, such as dams and other smaller in‐stream barriers. Conservation planning in freshwaters should consider the dynamic effects of climate change and the ability of species to cope with it. In this study, we developed a framework for incorporating climate‐driven dispersal barriers into conservation planning taking into account the medium and long‐term impacts of climate change and species with obligatory interactions. Given that freshwater mussels (Bivalvia: Unionida) are a group of highly threatened organisms dependent on fish hosts to complete their larval development and dispersal, we used Marxan to prioritize areas for their joint conservation in the Iberian Peninsula as a case study. We tested two connectivity scenarios between current and future habitats, (i) unlimited dispersal capacity and (ii) dispersal constrained by artificial barriers, and also identified priority translocation areas for species that were unable to disperse. Accounting for the effects of climate change on species distributions allowed the identification of long‐term conservation areas, but disregarding artificial barriers to dispersal may lead to unrealistic solutions. Integrating the location of barriers allowed the identification of priority areas that are more likely to be colonized in the future following climatic shifts, although this resulted in an additional loss of six to eight features (~5%–7%) compared to solutions without dispersal constraints. Between 173 and 357 artificial barriers (~1.6%–3.3%) will potentially block species dispersal to irreplaceable planning units. Where removal of artificial barriers is unfeasible, conservation translocations may additionally cover up to eight additional features that do not meet conservation targets due to dispersal constraints. This study highlights the challenge of identifying protected areas to safeguard biodiversity under climate change.

Abstract PR016: Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis

Abstract Cancer evolution involves multiple stochastic processes across many scales. In addition to genomic and transcriptomic changes, hallmarks of cancer within solid tumors include the malignant co-evolution of the tumor microenvironment. Although the evaluation of tissue architecture plays a key role in pathologist staging, the automated extraction of additional structural features remains far from translation to the clinic. However, one particularly effective novel methodology for extracting patterns from these clinical images is topological data analysis(TDA). TDA is a field that studies the shape of data and its connectedness using concepts from geometric and algebraic topology. Recent work using topological data analysis has allowed the extraction of image features. Such features have been associated with clinical outcomes in both the analysis of H&E images in prostate cancer and CT images in lung cancer. We set out to question how the topological features of tissues, including tumor and stromal composition, evolve during treatment and between original sites and metastasis. To analyze this we use a combination of tissue sections, imaging, and cell-automata models and persistent homology and cubical persistence techniques from TDA. We apply certain topological methods for the first time to tissue images (H&E) and to the output of spatial simulations. We focus specifically on analyzing the evolution of topological features in ovarian and lung cancer and compare our features to 2D and 3D simulations (HAL) with varying game theoretical interactions as well as comparing features across tissues from matched primary and metastatic sites. We find that several topological features are conserved between primary and metastatic sites and some feature evolution is replicated in simulation. Citation Format: Rowan Barker-Clarke, Jason M. Gray, Jacob G. Scott. Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR016.

OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION

Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.

IgG glycosylation associates with risk of progression from latent to active tuberculosis

Glycosylation motifs shape antibody structure, stability and antigen affinity and play an important role in antibody localization and function. Serum IgG glycosylation profiles are significantly altered in infectious diseases, including tuberculosis (TB), but have not been studied in the context of progression from latent to active TB. We performed a longitudinal study of paired bulk IgG glycosylation and transcriptomic profiling in blood from individuals with active TB (ATB) or latent TB infection (LTBI) before and after treatment. We identified that a combination of two IgG1 glycosylation traits were sufficient to distinguish ATB from LTBI with high specificity and sensitivity, prior to, and after treatment. Importantly, these two features positively correlated with previously defined cellular and RNA signatures of ATB risk in LTBI, namely monocyte to lymphocyte ratio and the expression of interferon (IFN)-associated gene signature of progression (IFN-risk signature) in blood prior to treatment. Additional glycosylation features at higher prevalence in LTBI individuals with high expression of the IFN-risk signature prior to treatment included fucosylation on IgG1, IgG2 and IgG3. Together, our results demonstrate that bulk IgG glycosylation features could be useful in stratifying the risk of LTBI reactivation and progression to ATB.

Practical Overview of 123I-Ioflupane Imaging in Parkinsonian Syndromes.

Parkinsonian syndromes are a heterogeneous group of progressive neurodegenerative disorders involving the nigrostriatal dopaminergic pathway and are characterized by a wide spectrum of motor and nonmotor symptoms. These syndromes are quite common and can profoundly impact the lives of patients and their families. In addition to classic Parkinson disease, parkinsonian syndromes include multiple additional disorders known collectively as Parkinson-plus syndromes or atypical parkinsonism. These are characterized by the classic parkinsonian motor symptoms with additional distinguishing clinical features. Dopamine transporter SPECT has been developed as a diagnostic tool to assess the levels of dopamine transporters in the striatum. This imaging assessment, which uses iodine 123 (123I) ioflupane, can be useful to differentiate parkinsonian syndromes caused by nigrostriatal degeneration from other clinical mimics such as essential tremor or psychogenic tremor. Dopamine transporter imaging plays a crucial role in diagnosing parkinsonian syndromes, particularly in patients who do not clearly fulfill the clinical criteria for diagnosis. Diagnostic clarification can allow early treatment in appropriate patients and avoid misdiagnosis. At present, only the qualitative interpretation of dopamine transporter SPECT is approved by the U.S. Food and Drug Administration, but quantitative interpretation is often used to supplement qualitative interpretation. The authors provide an overview of patient preparation, common imaging findings, and potential pitfalls that radiologists and nuclear medicine physicians should know when performing and interpreting dopamine transporter examinations. Alternatives to 123I-ioflupane imaging for the evaluation of nigrostriatal degeneration are also briefly discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Intenzo and Colarossi in this issue.

Modeling of three-dimensional blood flow in microchannels using a two-fluid method

This work presents a novel two-fluid method based on our recently proposed viscosity model for red blood cells (RBCs)—for simulating three-dimensional (3D) blood flow in a microchannel of dimension comparable to the diameter of red blood cells and larger. Toward this, whole blood is assumed as a suspension of red blood cells in blood plasma, with each phase considered as interpenetrating continua having its separate mass and momentum conservation equations. The proposed approach-based performance study is presented after comprehensively validating it with experimental data for blood flow in a uniform, sudden expansion-constriction, and Y-shaped bifurcated rectangular microchannels over—an extensive range of size (25–330 μm), flow rates (11.8 μl/h–30 ml/h), and inlet hematocrit (0%–45%). The proposed approach effectively captures significant biophysical and biomechanical insights into blood flow. It highlights a migration of red blood cells toward the center of the microchannel and the formation of a cell-free layer near the wall. Notably, with the introduction of constriction and expansion in the microchannel, it predicts a fivefold enhancement of the cell-free layer. The Fahraeus and Fahraeus–Lindquist effects are also demonstrated in microchannels, with less than 300 μm characteristic dimensions. These findings are consistent with experimental evidence. In addition to experimentally evident phenomena, our simulations unveil several additional flow phenomena and features of blood flow in the microchannel. It is observed that the presence of confluence (merging flow) is more disturbing to the blood flow than the presence of diverging bifurcations (splitting flow). Furthermore, after the confluence, velocity profiles exhibit a local peak that persists up to the microchannel outlet. Primary contribution of this work lies in the proposal of a two-fluid method for simulating 3D blood flow in complex geometries. This approach provides a comprehensive understanding of blood flow dynamics in microchannels and can be applied to optimize dimensions and geometries during the initial phases of plasma separation microdevices development.

Datum-Wise Inference in Structured Environments

In various application domains (e.g., health, psychology), experts use Bayesian networks to represent relationships among variables. However, these variables are not in practice directly observable, but can be instead inferred via noisy but costly features. Herein, we study the problem of datum–wise feature selection and classification in the case where the label of each data instance is described by a known Bayesian network, and features are available at a cost. The goal is to accurately classify each data instance, while keeping the feature acquisition cost minimum. To this end, we first propose a forward pass algorithm that sequentially acquires features to infer the label of each variable in the Bayesian network. During this process, the proposed algorithm uses both the acquired features and the Bayesian network relationships. In an effort to improve classification accuracy, we also devise a backward pass algorithm, which exploits Bayesian network relationships along with evidence. We discuss the computational complexity of both algorithms and experimentally assess their performance on 11 datasets. We observe that the forward pass algorithm achieves higher accuracy using a small fraction of features compared to state–of–the–art, while the backward pass algorithm enhances accuracy without acquiring additional features.

Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The reason for using this combination is their ability not only to match strings but also to correct word typing errors. This research contributes by developing a self-attention mechanism through a modified fuzzy string-matching model with enhanced word feature structures. The goal is to utilize this self-attention mechanism in constructing the Indonesian medical bidirectional encoder representations from transformers (IM-BERT). This will serve as a foundation for additional features to provide accurate answers in the Indonesian medical question and answer system, achieving an exact match of 85.7% and an F1-score of 87.6%.

FEATURES, ATTRIBUTES AND CRITERIA OF DEMOCRATIC POLITICAL REGIMES (COMPARATIVE ANALYSIS)

In the context of the continual and sustainable development of social order and civil society, the comprehension of the features, signs, and criteria of a democratic political regime is transforming and necessitates reconsideration, rendering this work pertinent. The objective of this article is to delineate the substance of the array of features and characteristics inherent to a democratic political regime, contrasting them with those of an authoritarian and totalitarian political regime. Additionally, it aims to elucidate the criterion by which distinctions between various types of political regimes can be made. The present publication employs analytical, synthetic, scientific generalization, theoretical comparison, and formal logical methods. The article explicates the contentious nature of the concept of "political regime" and presents diverse scientific approaches to its comprehension. It posits that the criterion for categorizing political regimes into democratic, authoritarian, and totalitarian forms hinges on "the peculiarities of interaction between civil society and the State, the degree of permeation of private life of citizens, and various spheres of the social reality of State power." The author provides a comparative analysis not only of democratic political regimes but also of authoritarian and totalitarian ones. The author conducts a detailed examination of the content associated with political science categories such as "features," "signs," and "criteria" of a political regime. Furthermore, the author introduces a personalized understanding of these concepts and offers insights into the interrelations among the terms under consideration. The article delineates an approach wherein the fundamental classification of political regimes is proposed to be nuanced. According to the author, political regimes ought to be categorized into democratic and non-democratic, with the latter further subcategorized into authoritarian and totalitarian. The author delineates the conventional features of a democratic political regime from the perspective of political science and expounds upon additional features which, in the author's estimation, might signify the presence of a democratic political regime in a given state. The assertion is made that a direct correlation between the form of government and the political regime in the state does not exist, and illustrative examples supporting this perspective are provided.

Equine common variable immunodeficiency: Lessons from 100 clinical cases

SummaryThe clinical manifestation of recurrent fevers and infections alerts the clinician to the possibility of an underlying immunodeficiency. Common variable immunodeficiency (CVID) in the horse is a rare late‐onset, nonfamilial immunological disorder of B cell depletion and/or dysfunction with resultant inadequate antibody production. The most common clinical presentations in horses with CVID are recurrent upper and/or lower respiratory infections, meningitis and/or ataxia, cholangiohepatitis, infectious colitis, infectious dermatitis and severe gastrointestinal parasitism. Immune‐mediated and lymphoproliferative conditions are additional clinical features. The diagnosis of CVID in horses is based on a persistent hypogammaglobulinaemia primarily caused by a serum IgG concentration below 10.00 g/L in at least two different measurements, often accompanied by a serum IgM concentration below 0.50 g/L. Most horses with CVID show a persistent peripheral blood B cell distribution below 6% of total circulating lymphocytes, indicating severe B cell paucity or depletion, but the B cell distribution can be within the normal reference interval. Post‐mortem findings add diagnostic information about the distribution of B and T cells in lymphoid tissues. Clinical management of horses with immunodeficiency is intense and expensive, and these factors weigh on the difficult decision of elective euthanasia. To date, no genetic mutation has been identified in horse patients with CVID, and the large number of breeds of single‐affected individuals in a same herd or immediate lineage from various parts of the United States and the world do not point at an obvious inheritable mechanism of disease or environmental risk factors. This article describes the clinical and immunological findings in 100 equine cases, and comparisons with the disease in human patients.

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

This study delves into the application of machine learning (ML) and deep learning (DL) techniques for the analysis of seismic data, aiming to identify and categorize patterns and anomalies within seismic events. Using a robust dataset, we applied three distinct clustering approaches: K-Means, DBSCAN, and an Autoencoder-based method, each offering unique perspectives on the data. K-Means clustering provided a fundamental partitioning of the data into five predefined clusters, facilitating the identification of broad seismic patterns. DBSCAN, a density-based clustering algorithm, offered insights into the spatial distribution and density of seismic events, adeptly pinpointing anomalies and outliers that signify unusual seismic activity. The Autoencoder, leveraging deep learning, excelled in capturing complex and non-linear relationships within the data, revealing subtle patterns not immediately apparent through traditional methods. The effectiveness of these clustering techniques was quantitatively evaluated using the Silhouette Score and the Davies-Bouldin Score, alongside visual assessments through PCA and t-SNE for dimensionality reduction. The results indicated that while K-Means provided clear partitioning, DBSCAN excelled in outlier detection, and the Autoencoder offered a balanced approach with its nuanced analysis capabilities. Our comprehensive analysis underscores the significance of employing a multi-methodological approach in seismic data analysis, as each method contributes uniquely to the understanding of seismic events. The insights gained from this study are valuable for enhancing predictive models and improving disaster risk management strategies in seismology. Future research directions include the integration of additional seismic features, validation against larger datasets, and the development of hybrid models to further refine the predictive accuracy of seismic event analysis.

Metal-Organic Frameworks as a Tunable Platform to Deconvolute Stereoelectronic Effects on the Catalytic Activity of Thioanisole Oxidation.

The local environment of a metal active site plays an important role in affecting the catalytic activity and selectivity. In recent studies, tailoring the behavior of a molybdenum-based active site via modulation of the first coordination sphere has led to improved thioanisole oxidation performance, but disentangling electronic effects from steric influences that arise from these modifications is nontrivial, especially in heterogeneous systems. To this end, the tunability of metal-organic frameworks (MOFs) makes them promising scaffolds for controlling the coordination sphere of a heterogeneous, catalytically active metal site while offering additional attractive features such as crystallinity and high porosity. Herein, we report a variety of MOF-supported Mo species, which were investigated for catalytic thioanisole oxidation to methyl phenyl sulfoxide and/or methyl phenyl sulfone using tert-butyl hydroperoxide (tBHP) as the oxidant. In particular, MOFs of contrasting node architectures were targeted, presenting a unique opportunity to investigate the stereoelectronic control of Mo active sites in a systematic manner. A Zr6-based MOF, NU-1000, was employed along with its sulfated analogue Zr6-based NU-1000-SO4 to anchor a dioxomolybdenum species, which enabled examination of support-mediated active site polarizability on catalytic performance. In addition, a MOF containing a mixed metal node, Mo-MFU-4l, was used to probe the stereoelectronic impact of an N-donor ligand environment on the catalytic activity of the transmetalated Mo center. Characterization techniques, including single crystal X-ray diffraction, were concomitantly used with reaction time course profiles to better comprehend the dynamics of different Mo active sites, thus correlating structural change with activity.

Improving gene set enrichment analysis (GSEA) by using regulation directionality.

To infer the biological meaning from transcriptome data, it is useful to focus on genes that are regulated by the same regulator, i.e., regulons. Unfortunately, current gene set enrichment analysis (GSEA) tools do not consider whether a gene is activated or repressed by a regulator. This distinction is crucial when analyzing regulons since a regulator can work as an activator of certain genes and as a repressor of other genes, yet both sets of genes belong to the same regulon. Therefore, simply averaging expression differences of the genes of such a regulon will not properly reflect the activity of the regulator. What makes it more complicated is the fact that many genes are regulated by different transcription factors, and current transcriptome analysis tools are unable to indicate which regulator is most likely responsible for the observed expression difference of a gene. To address these challenges, we developed the gene set enrichment analysis program GINtool. Additional features of GINtool are novel graphical representations to facilitate the visualization of gene set analyses of transcriptome data, the possibility to include functional categories as gene sets for analysis, and the option to analyze expression differences within operons, which is useful when analyzing prokaryotic transcriptome and also proteome data.IMPORTANCEMeasuring the activity of all genes in cells is a common way to elucidate the function and regulation of genes. These transcriptome analyses produce large amounts of data since genomes contain thousands of genes. The analysis of these large data sets is challenging. Therefore, we developed a new software tool called GINtool that can facilitate the analysis of transcriptome data by using prior knowledge of gene sets controlled by the same regulator, the so-called regulons. An important novelty of GINtool is that it can take into account the directionality of gene regulation in these analyses, i.e., whether a gene is activated or repressed, which is crucial to assess whether a regulon or functional category is affected. GINtool also includes new graphical methods to facilitate the visual inspection of regulation events in transcriptome data sets. These and additional analysis methods included in GINtool make it a powerful software tool to analyze transcriptome data.

Program Peningkatan Aset PAUD Seroja dengan Mengembangkan Fasilitas Pembelajaran Berkualitas Pada Anak Usia Dini Berbasis Web

This research aims to improve early childhood learning facilities at PAUD Seroja through the implementation of web applications as part of community service activities. The success of this research can be seen through the effectiveness of the application in managing inventory data, teacher information, and student information. The proposed improvement suggestions include regular updates, increasing the level of security, as well as developing additional features such as payment system integration, as concrete steps in optimizing this service activity. The application implementation process involves active participation from users, who are also stakeholders in service activities. The main hope is that this application will not only increase the efficiency of information management at PAUD Seroja but also make a positive contribution to the experience and quality of early childhood learning. Recommendations and input from users will be the main basis for continuous improvement and further development, which is under the spirit of community service activities. This research contributes to efforts to develop early childhood education by utilizing information technology, as well as confirming the role of service activities as a means of optimizing the use of technology in improving the quality of learning at PAUD Seroja.

A new treatment modality for the treatment of unligated thoracic side branch of left internal mammary artery: Underexpanded stent

The left internal mammary artery (LIMA) is often preferred in coronary artery bypass grafting (CABG). LIMA has additional features such as less fenestration in the endothelial layer, less intercellular permeability, anti-thrombotic activity, rapid lipolysis, and less lipid synthesis, resulting in an excellent long-term patency rate. LIMA side branches or anatomical variations that are not ligated intraoperatively may cause coronary ischemia in the postoperative period. Although various treatment modalities such as coiling, vascular plug embolization, and surgical ligation have been described in treating LIMA side branches, no studies show the superiority of one treatment modality over another. In this case, the advantages, disadvantages, and critical points affecting the success of unligated LIMA side branches treatment methods were discussed.

Atherosclerotic plaque features relevant to rupture-risk detected by clinical photon-counting CT ex vivo: a proof-of-concept study

BackgroundTo identify subjects with rupture-prone atherosclerotic plaques before thrombotic events occur is an unmet clinical need. Thus, this proof-of-concept study aims to determine which rupture-prone plaque features can be detected using clinically available photon-counting computed tomography (PCCT).MethodsIn this retrospective study, advanced atherosclerotic plaques (ex vivo, paraffin-embedded) from the Carotid Plaque Imaging Project were scanned by PCCT with reconstructed energy levels (45, 70, 120, 190 keV). Density in HU was measured in 97 regions of interest (ROIs) representing rupture-prone plaque features as demonstrated by histopathology (thrombus, lipid core, necrosis, fibrosis, intraplaque haemorrhage, calcium). The relationship between HU and energy was then assessed using a mixed-effects model for each plaque feature.ResultsPlaques from five men (age 79 ± 8 [mean ± standard deviation]) were included in the study. Comparing differences in coefficients (b1diff) of matched ROIs on plaque images obtained by PCCT and histology confirmed that calcium was distinguishable from all other analysed features. Of greater novelty, additional rupture-prone plaque features proved discernible from each other, particularly when comparing haemorrhage with fibrous cap (p = 0.017), lipids (p = 0.003) and necrosis (p = 0.004) and thrombus compared to fibrosis (p = 0.048), fibrous cap (p = 0.028), lipids (p = 0.015) and necrosis (p = 0.017).ConclusionsClinically available PCCT detects not only calcification, but also other rupture-prone features of human carotid plaques ex vivo.Relevance statementImproved atherosclerotic plaque characterisation by photon-counting CT provides the ability to distinguish not only calcium, but also rupture-prone plaque features such as haemorrhage and thrombus. This may potentially improve monitoring and risk stratification of atherosclerotic patients in order to prevent strokes.Key points• CT of atherosclerotic plaques mainly detects calcium.• Many components, such as intra-plaque haemorrhage and lipids, determine increased plaque rupture risk.• Ex vivo carotid plaque photon-counting CT distinguishes haemorrhage and thrombus.• Improved plaque photon-counting CT evaluation may refine risk stratification accuracy to prevent strokes.Graphical

Machine Learning for Intrusion Detection for Massive IoT Networks

Wide area networks such as fog and internet of things often encounter network level security. There would exist a continued trade-off between the error rate (authentication metric), system overhead, computational complexity and latency of the system. Hence an extremely meticulous system design with appropriate choice of stochastic parameters and authentication scheme should be adopted. In this proposed work, an acceleration learning based LSTM network has been proposed to detect attacks in IoT networks. It can be observed from the obtained results that the proposed system attains better performance compared to previously existing system. The performance enhancement can be attributed to additional features computed and the LSTM with acceleration used to train and further detect errors. Keywords: Internet of Things (IoT), Network Level Security, Neural Networks, Deep Learning, Accuracy, Gateway Utility.

Windage Torque Reduction in Low-Pressure Turbine Cavities Part II: Experimental and Numerical Results

Abstract Minimizing the losses within a low-pressure turbine (LPT) system is critical for the design of next-generation ultra-high bypass ratio aero-engines. The stator-well cavity windage torque can be a significant source of loss within the system, influenced by the ingestion of mainstream annulus air with a tangential velocity opposite to that of the rotor. This paper presents experimental and numerical results of three carefully designed Flow Control Concepts (FCCs)—additional geometric features on the stator surfaces, which were optimized to minimize the windage torque within a scaled, engine-representative stator-well cavity. FCC1 and FCC2 featured rows of guide vanes at the inlet to the downstream and upstream wheel-spaces, respectively. FCC3 combined FCC1 and FCC2. Superposed flows were introduced to the upstream section of the cavity, which modelled the low-radius coolant and higher radius leakage between the rotor blades. In addition to torque measurements, total and static pressures were collected, from which the cavity swirl ratio was derived. Additional swirl measurements were collected using a five-hole aerodynamic probe, which traversed radially at the entrance and exit of the cavity. A cavity windage torque reduction of 55% on the baseline (which has no flow control) was measured for FCC3, at the design condition with superposed flow. For this concept, an increase in the cavity swirl in both the upstream and downstream wheel-spaces was demonstrated experimentally and numerically. With increasing superposed flow, the contribution of FCC1 surpassed FCC2, due to more mass flow entering the downstream wheel-space across the rotor fins (passing FCC1), and less ingestion from the annulus into the upstream wheel-space (passing FCC2). The torque changes from the concepts are explained using the fluid dynamic evidence from experimental swirl measurements and computational simulations. The simulations allow translation to engine-operating conditions and practical information to the engine designer.

Changing-look Active Galactic Nuclei from the Dark Energy Spectroscopic Instrument. I. Sample from the Early Data

Changing-look active galactic nuclei (CL AGNs) can be generally confirmed by the emergence (turn-on) or disappearance (turn-off) of broad emission lines (BELs), associated with a transient timescale (about 100 ∼ 5000 days) that is much shorter than predicted by traditional accretion disk models. We carry out a systematic CL AGN search by crossmatching the spectra coming from the Dark Energy Spectroscopic Instrument and the Sloan Digital Sky Survey. Following previous studies, we identify CL AGNs based on Hα, Hβ, and Mg ii at z ≤ 0.75 and Mg ii, C iii], and C iv at z > 0.75. We present 56 CL AGNs based on visual inspection and three selection criteria, including 2 Hα, 34 Hβ, 9 Mg ii, 18 C iii], and 1 C iv CL AGN. Eight cases show simultaneous appearances/disappearances of two BELs. We also present 44 CL AGN candidates with significant flux variation of BELs, but remaining strong broad components. In the confirmed CL AGNs, 10 cases show additional CL candidate features for different lines. In this paper, we find: (1) a 24:32 ratio of turn-on to turn-off CL AGNs; (2) an upper-limit transition timescale ranging from 330 to 5762 days in the rest frame; and (3) the majority of CL AGNs follow the bluer-when-brighter trend. Our results greatly increase the current CL census (∼30%) and would be conducive to exploring the underlying physical mechanism.